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A no-BS, insider breakdown of the most powerful people search APIs in 2026.
Finding up-to-date information on people – whether for recruiting talent, generating sales leads, or verifying identities – is a critical task for many organizations. Manually scouring sites like LinkedIn, GitHub, Stack Overflow, and others is time-consuming, and that’s where People Search APIs come into play. These APIs connect your software directly to vast databases of personal and professional profiles, allowing you to retrieve rich profile data (jobs, skills, social links, contact info, etc.) with a simple query.
In 2026, this landscape is rapidly evolving: large data providers are expanding globally (some charging extra for extensive international coverage), while new AI-powered entrants are changing how we search and screen candidates.
This guide reviews 10 of the best people search APIs available as we head into 2026, examining their data sources, features, pricing, use cases, limitations, and how each is adapting to trends like AI agents and automated candidate matching.
People Data Labs is often considered one of the largest and most comprehensive people data providers. It offers a massive database of professional profiles – over 1.5 billion unique person profiles globally – aggregated from public web data and partner sources. PDL’s Person Search and Enrichment APIs allow you to find individuals by criteria like name, company, title, skills, or even a LinkedIn URL, and retrieve detailed profile records. A single profile can include 100+ attributes: work history, education, skills, social media URLs (including LinkedIn, GitHub, Stack Overflow if available), and verified contact details (emails, phone numbers) if you have access to premium data fields. This breadth of data makes PDL extremely powerful for building rich candidate or lead profiles.
Features and Coverage: One standout aspect of PDL is its global coverage – data spans over 180 countries, making it suitable for international people searches. All profiles are merged and deduplicated from multiple sources to provide a “full picture” of a person (for example, connecting a LinkedIn profile to their GitHub, personal website, and so on). PDL boasts high accuracy on crucial fields (they report ~95% accuracy for emails and ~90% for phone numbers) and offers 200+ data points per profile by breaking information into granular fields. For instance, an address is split into street, city, country, etc., which means you get very detailed structured data. This granularity is helpful if you need to filter or sort by specific fields, though it can be overwhelming for simple use cases.
Pricing and Use Cases: People Data Labs uses a credit-based pricing model that can be complex for newcomers. They do have a free tier (100 person lookups/month) and a starting Pro plan around $98/month for 350 person queries, but costs escalate quickly for higher volumes or additional endpoints. In fact, PDL is known to be on the pricier side – small businesses often find it hard to afford at scale, and enterprise plans can run in the thousands per month. Each profile or enrichment consumes credits, and certain premium fields or global region data might require higher-tier plans. Despite the cost, companies pay a premium for PDL when they need the most complete profiles for tasks like candidate sourcing, B2B lead enrichment, or data science projects. Recruiters use PDL to instantly enrich a list of candidates (e.g. adding emails, skills, and social links to each LinkedIn profile in their ATS). Sales teams integrate it to get verified contact info and job titles for prospect lists. Because the data is pre-collected, responses are fast – suitable for real-time applications where you input an email or name and get an immediate profile match.
Limitations: The richness of PDL’s dataset comes with a couple of trade-offs. Firstly, data freshness can be a concern – the underlying profiles are typically updated on a monthly cadence by default. That means very recent job changes or newly added profiles might not appear until the next update, which could be a drawback if real-time updates are critical. PDL has been addressing this with incremental refreshes, but it’s fundamentally not “live-scraping” on each query. Secondly, the pricing structure (credit tiers, add-ons for certain data types, requirement of business email to sign up) adds complexity; it’s easy to burn through credits if you’re not careful, and costs for large-scale usage can become significant. PDL is also strictly an API/data provider – it’s not an end-user tool and not very “developer-friendly” in terms of onboarding (by design, it caters to platforms and engineers who will integrate the API into their own systems). Lastly, while PDL emphasizes compliance (GDPR, CCPA, and even SOC 2/ISO 27001 certifications) and legally-sourced data, one should always ensure using the data in a privacy-compliant way – especially when dealing with EU or other regions where extra regulations might apply.
In summary, People Data Labs is a top choice if you need a massive, all-encompassing people dataset and are willing to invest in quality. It shines in scenarios like building a global candidate database or enriching millions of records with deep details. Just be prepared for the enterprise-oriented pricing and make sure to manage credit usage wisely. PDL’s premium positioning has also spurred many alternatives (some of which we’ll cover below) that aim to offer more flexible pricing or fresher data, but few match PDL’s sheer breadth of profile data points.
HeroHunt.ai is a newer entrant that leverages artificial intelligence to supercharge people search, especially for recruiting tech talent. Originally launched as an AI-powered recruiter platform, HeroHunt recently launched an API to give developers access to its talent search engine. What sets HeroHunt apart is its focus on searching multiple platforms at once (LinkedIn, GitHub, Stack Overflow, and others) and using AI to interpret search queries and even job descriptions. In fact, HeroHunt can take a full job description or natural language query as input, and the AI will generate a targeted candidate search from it – rather than relying only on strict filters. This makes it uniquely powerful for finding specialized candidates (say, “Python developers with open-source contributions in fintech”) without manually crafting complex boolean strings.
Coverage and Data: Despite being relatively new, HeroHunt boasts a huge talent index of over 1 billion candidate profiles worldwide, aggregated from LinkedIn and various developer communities. It essentially tries to “find all LinkedIn profiles for a fraction of the price,” as well as GitHub and Stack Overflow profiles. One major advantage is that HeroHunt doesn’t just list a person’s profiles – it also provides contact details for many candidates, including verified email addresses and sometimes phone numbers. For example, if HeroHunt finds a software engineer on GitHub and LinkedIn, it will often return their professional email so you can reach out directly. The platform emphasizes finding publicly available contacts and verifying them (to ensure low bounce rates), which is incredibly useful for recruiters and sales alike. According to the company, their engine can uncover full contact details (emails or socials) for candidates, with verification for higher response rates.
AI-Powered Search and Screening: HeroHunt’s API isn’t just a dumb data lookup – it’s augmented by AI in several steps of the hiring funnel. When you query the API with a role or keywords, an AI (powered by a language model) parses the context to broaden or refine the search beyond exact keyword matching. This helps in finding profiles that might not exactly match a keyword but fit the intent (for instance, understanding synonyms, related skills, or context from a job description). After retrieving candidate profiles, HeroHunt applies an AI screening layer: it analyzes each candidate’s skills, experience and online activity and then scores or ranks candidates based on how well they match the query. Recruiters using HeroHunt’s interface see a summarized “AI Screening” section, which is essentially a snapshot of the candidate’s qualifications and a relevancy score. Through the API, you can similarly get AI-generated relevancy scores or summaries of candidates. This screening step is a game-changer – it saves time by automatically highlighting the best fits, and it provides insight (e.g. which skills or keywords contributed to the match). As an example, HeroHunt’s AI might read a candidate’s GitHub contributions and LinkedIn skills and then say “Candidate A has strong Python and Docker experience, matching 8/10 of your criteria” in a summary format. This is something traditional data APIs don’t offer out of the box.
Beyond screening, HeroHunt even automates outreach (in the full product): it can draft personalized messages for candidates. While outreach is likely outside the scope of the API (it’s more a feature of their platform UI using their own email system), it demonstrates the trend of AI agents handling end-to-end recruiting tasks. With the API, developers could still use the data to trigger their own outreach flows, armed with the knowledge that contacts are verified and profiles pre-screened for fit.
Use Cases and Pricing: HeroHunt.ai’s API is ideal for tech recruitment platforms, AI recruiting assistants, or any application that needs to quickly find and rank talent. For example, an applicant tracking system (ATS) could integrate HeroHunt to source passive candidates: given a job req, automatically search for matching people across the web and import their profiles plus contact info, ready for a recruiter to reach out. It’s also useful for building specialized candidate lists (like “all front-end developers in Amsterdam who contribute on Stack Overflow”) without manually visiting each site. Since HeroHunt covers GitHub and Stack Overflow profiles in addition to LinkedIn, it’s particularly attractive for engineering recruitment – it uncovers talent that might not be actively looking for jobs (passive candidates) but have an online footprint that signals expertise. This widens the talent pool beyond just LinkedIn’s active users.
In terms of pricing, HeroHunt positions itself as more accessible than legacy data giants. There is a free trial and even a free tier for small usage, which is great for startups or testing. Paid plans are typically subscription-based (with seat licenses for the UI) – for the API, one would likely go for a custom plan depending on the volume of searches/candidates. The company has marketed itself as significantly more affordable for large-scale LinkedIn search than traditional methods (hence “fraction of the price”). Unlike some data providers that charge extra for each data type or region, HeroHunt’s value proposition is in unlimited searching across its whole 1B profile index for a flat rate per month (with some reasonable usage limits). This can be a huge cost saver if you regularly need to search globally. It’s worth noting that because HeroHunt actively crawls public web data, it does not rely on LinkedIn’s API (which is very restricted); this means it can show you essentially any public LinkedIn profile, circumventing the limitations of official LinkedIn search – but it also must be careful to remain compliant and not trigger anti-scraping measures. So far, HeroHunt has managed this by focusing on publicly available info and respecting privacy norms.
Limitations: As with any newer AI-driven service, you should be aware of a few limitations. HeroHunt’s AI matching, while advanced, isn’t foolproof – sometimes the AI might surface candidates who look like a match on paper but aren’t truly interested or available (that final step of human vetting is still needed). The data it provides is public info plus any emails it can find; it does not have confidential or private candidate data beyond what’s openly accessible. So, for example, if a person’s LinkedIn is very sparse and they have no GitHub, HeroHunt might not have much more on them than another service would. Additionally, relying on AI screening means you might miss some unconventional candidates (the AI could conceivably overlook a profile that is a great fit but written in an unusual way). However, you can usually still access the raw profile data as well, so nothing stops a human from reviewing all results.
Finally, since HeroHunt is providing contact data and automating outreach, users must ensure they follow anti-spam and privacy regulations. If you suddenly email a hundred candidates in Europe, make sure you’re doing it in a GDPR-compliant way (e.g., these are professional contacts relevant to a job opportunity, etc.). HeroHunt does mention it finds verified contact details with high deliverability, which implies they also verify emails (likely via SMTP checks or past response history), increasing the chances your emails won’t bounce. This is a great benefit over some older tools where email quality was hit-or-miss.
In summary, HeroHunt.ai represents the next generation of people search APIs: it uses AI to handle the heavy lifting of both finding the right people across multiple networks and assessing their fit. It’s especially valuable for global tech recruitment, providing a one-stop solution to source from LinkedIn, GitHub, Stack Overflow, etc., plus get the contacts to reach out. As of late 2025, HeroHunt is an up-and-coming player challenging the bigger data providers with its modern, AI-first approach and more accessible pricing. For organizations looking to incorporate an AI recruiter into their workflow or product, HeroHunt.ai is definitely one to consider.
Coresignal is a data provider known for its emphasis on “always fresh” public web data, particularly professional profiles and company data. It’s a go-to choice for many when it comes to tapping into LinkedIn data in an ethical, structured way. Unlike some people search APIs that operate like black boxes, Coresignal offers more of a data-platform approach: you can access entire datasets (like all employees, or all companies) or query their APIs for specific people/companies as needed. It is especially popular among data scientists, HR tech builders, and investors who want bulk or custom analyses, since Coresignal provides not just search but also large-scale data dumps, analytics, and even historical data.
Data and Scale: Coresignal’s strength lies in its scale and data freshness. As of 2025, they advertise having over 800 million professional profiles in their employee dataset. These profiles are aggregated from multiple public sources – primarily LinkedIn, but also alternative professional networks, public CV databases, and other web sources. They also have data on tens of millions of companies and hundreds of millions of job postings. One thing to highlight: all of Coresignal’s data is ethically collected from publicly available information and updated regularly (some data is updated daily or in real-time via their APIs). This means Coresignal does not provide any information that isn’t public – for example, they will include a person’s job titles, experience, skills, etc., because those might be visible on a public LinkedIn or similar profile, but they will not provide personal emails or phone numbers since those are usually private. In fact, Coresignal explicitly states that their data does not include private contact information and focuses on public professional data to remain compliant with GDPR and other regulations.
So, what do you get in a typical Coresignal Employee API response? A rich professional profile: name, current title and company, past work history, education credentials, skills (they often infer skills from the profile text), location, and even some calculated insights like “projected salary” range for the role (they use statistical models to estimate salaries based on role, location, etc., which is quite unique). An example from their documentation shows fields like inferred skills, last graduation date, and even projected base salary percentiles for a given person’s role. These extra data points can be useful for analytics – e.g., an HR tool might use the salary estimate as a clue for seniority or to inform a recruiter about market rates.
Coresignal’s dataset is global. Since it’s largely drawn from LinkedIn and similar sources, it covers professionals across all regions (LinkedIn’s largest user base is US/Europe, but also millions in Asia, LATAM, etc.). They report having hundreds of millions of profiles across North America, Europe, and beyond. In practice, if a professional has any sort of public profile online, Coresignal aims to have it. If your use case requires heavy coverage in Europe or other regions where privacy laws are stricter, Coresignal is actually a solid choice because of their compliance focus – all data is consented or public. Unlike some providers that might charge extra or have patchy data for European contacts, Coresignal treats global data uniformly since it’s collected openly (they don’t rely on user-contributed contact data like some sales lead tools do).
API and Integration: Using Coresignal is a bit different from a simple “find person by name” API. They have a few API endpoints: Employee Search API where you can query for profiles by criteria (e.g., search by job title, company, location filters – essentially replicating a LinkedIn Recruiter search via API), and Employee Enrichment API where you can input something like a LinkedIn profile URL or ID and get back the structured profile data for that specific person. The search API is useful if you want to find a list of people (for example, “Software Engineers at Google in Canada”) and it will return matching profiles. The enrichment API is more for updating info on a known person (e.g., you have a LinkedIn URL and want to pull their latest data fields). Coresignal also provides self-service tools where you can download bulk data or set up a pipeline to get updates as new data comes in (they offer data feeds delivered via cloud storage or direct database access for large clients).
One advantage is their real-time update options – they introduced a Real-Time API (RTAPI) that can fetch the latest info on a profile on-demand. Essentially, if you pass a LinkedIn URL to this RTAPI, it will retrieve the profile live and give you updated data in seconds, meaning you’re not limited to the last monthly refresh. This is important if you need absolutely current data (like the person changed jobs last week). However, real-time calls might be more rate-limited or costlier since they involve on-the-fly scraping.
Use Cases: Coresignal is widely used in scenarios where the breadth and freshness of professional data are key. For example, recruitment platforms use it for talent sourcing – rather than manually searching LinkedIn, a recruiting tool can query Coresignal’s API to find all candidates fitting a role and then display them to the user. Because it doesn’t supply contact info, these platforms might use Coresignal to identify candidates and then use another service for contact finding (or manually reach out via LinkedIn). Another big use is in HR analytics and talent market research: with hundreds of millions of profiles, data scientists can analyze trends (like how many AI engineers are in a region, which skills are most common, etc.). Investors and marketing analysts also use Coresignal’s data to gauge company headcounts, growth (by looking at hiring data in profiles), or overall industry movement. Essentially, if you need to tap into LinkedIn-like data in a programmatic way and you want a trustworthy source, Coresignal is a top choice.
Pricing: Coresignal doesn’t publicly list simple prices because they tailor it to your needs – for instance, you might pay for a certain number of API calls per month or purchase a dataset subscription. They do offer free trial credits so developers can experiment with the APIs. Typically, expect a subscription model (monthly or annual) based on data access. It’s generally more accessible than something like ZoomInfo in cost, but it also doesn’t give you ready-made emails/phones. So there’s a trade-off: you might pay Coresignal for profiles and separately use something like an email finding service if needed. The scalability is worth noting – Coresignal’s infrastructure is built to handle large volumes (since some clients literally download the entire dataset). If you plan to make heavy, automated use of a people data API, Coresignal can probably handle a high query throughput and huge result sets, which not all services can.
Limitations: The main limitation is that Coresignal does not provide personal contact data. This is intentional to remain privacy-compliant. They may give a business email if it’s publicly available on the profile (occasionally people list an email on GitHub or a personal site which gets picked up), but you shouldn’t rely on Coresignal for emails/phones. So, for pure sales lead generation (where direct contact info is the goal), Coresignal alone isn’t sufficient – you’d pair it with an enrichment tool for contacts (some of the other APIs in this list can serve that role). Another consideration: because it’s ethically sourced, if a professional’s profile is private or they aren’t on any public site, that person won’t be in Coresignal. In contrast, a service like ZoomInfo might have data on someone from an email list or a third-party source even if that person’s profile is locked down – so the approaches differ. Finally, interpreting the data might require some expertise; Coresignal gives very detailed structured data (like a JSON with many nested fields). For a non-technical user or small team, this could be overkill. However, they have improved their documentation and even a dashboard to run test queries, which helps.
In summary, Coresignal is an excellent solution when you need extensive, up-to-date professional profiles at scale, and you prioritize data compliance and structure. It’s essentially the closest thing to getting LinkedIn’s public data via API, without violating terms (they have navigated the legal landscape by focusing on what is publicly permissible). Companies building innovative HR tech, labor market analytics, or large-scale recruitment databases will find Coresignal invaluable. Just remember you might need to supplement it for contact info if outreach is your end goal. Coresignal exemplifies a trend towards open-web data in people search – instead of relying on closed platforms, it continually gathers what’s openly available and makes it easy to consume via APIs.
ZoomInfo is often considered the 800-pound gorilla of B2B contact and people data. It’s a sales intelligence powerhouse that has been around for years, amassing a huge database of business contacts and company information. If your goal is to find not just who fits a certain professional profile but also get their direct contact details (work email, direct phone number) with a high degree of accuracy, ZoomInfo has long been a top choice. It’s especially popular with enterprise sales and marketing teams for lead generation, but it also has applications in recruiting (through a product they used to call TalentOS) since it covers professional profiles extensively.
Database Size and Accuracy: ZoomInfo’s database is enormous and global. It reportedly has over 100 million professional contacts in its core database (some sources cite even higher, like 150M+ or more, especially after recent acquisitions). This includes people across all industries and regions – though its strength historically has been North America, ZoomInfo has expanded international coverage in recent years (including Europe and APAC) by acquiring regional data providers. In addition, ZoomInfo tracks company data (over 100 million companies, including firmographics, technographics, etc.) which pairs with the people data. One of ZoomInfo’s claims to fame is its data accuracy: they employ a mix of web crawling, partnerships, community contributions, and a large team of human researchers to verify data. They often tout 90%+ accuracy rates on key fields. For instance, they claim around 92% accuracy on contact info like emails – very important for sales teams who don’t want emails bouncing. They keep data fresh by processing over a billion data points daily, ensuring that if someone changes jobs or a phone number, it gets updated quickly. In short, you are paying for one of the most up-to-date and vetted contact databases available.
API and Features: ZoomInfo is a bit unique in that it’s not just an API offering; it’s a full platform with web interfaces, plugins, and integrations. However, they do offer APIs and data services for those who want to integrate ZoomInfo data into their own systems. Through the ZoomInfo API, you can perform person search queries (for example, find all people with X title at companies in Y industry), you can enrich a profile (input an email or LinkedIn URL to get all data on that person), or do company lookups. They also have endpoints for things like getting news or intent signals on companies. The API results include the person’s name, title, company, work history, education, and crucially verified contact details (work email, phone, sometimes mobile). It’s worth noting ZoomInfo’s data often includes corporate phone numbers and personal work emails, which are usually not publicly displayed on LinkedIn – they gather these through their own means (user contributions, partnerships, or guess-and-verify algorithms). This crosses into non-public info, which is why ZoomInfo has a stringent compliance setup and requires users to adhere to usage rules (for example, if you’re emailing EU data subjects, you need proper consent or a lawful basis).
One big selling point is ZoomInfo’s integration ecosystem. They have native connectors to CRM systems like Salesforce, HubSpot, Microsoft Dynamics, and marketing automation platforms. So a lot of customers just use those integrations to feed data in rather than coding to the API. But if you’re a developer, the API lets you tap into the same data. They also provide features like webhooks or real-time sync where, say, when a lead comes into your system with just a name and company, you can call ZoomInfo to instantly append their email/phone and push it back into your CRM. This speeds up lead response times dramatically (sales reps have the info to call a lead within seconds of them submitting a form).
Use Cases: ZoomInfo is best known for sales prospecting. A typical use case: a sales team wants to target VP-level executives at fintech companies with 50-200 employees in the UK – using ZoomInfo’s API, they can run a query to find all people that match that criteria, get a list of those contacts with emails and phone numbers, and then load that into an outreach tool. Another use case is for account-based marketing: marketing teams use ZoomInfo data to enrich inbound leads or to create targeted lists for campaigns. For recruiting, ZoomInfo’s Talent search (if included) helps find candidates with certain roles and then reach out to them via email/phone outside of LinkedIn. It’s particularly useful for hiring sales or business roles where corporate contact info is readily used. Some companies also use ZoomInfo’s company data for things like updating their CRM accounts with industry info, revenue, employee count, etc., but our focus here is people data.
Pricing: ZoomInfo is known to be one of the more expensive solutions in this space. Typically, ZoomInfo contracts are annual and can range from $10,000 per year upward. Their pricing isn’t transparent publicly; it often depends on the number of seats (users) and the data features you need. They sometimes bundle credits for contact downloads or API usage. For small startups, this price can be a barrier – hence why alternatives exist (like Apollo, Lusha, etc., which we’ll discuss) offering lower entry points. Also, ZoomInfo might have separate modules – for example, if you want intent data or their Recruiter/TalentOS module, those could be add-ons. The key point is that ZoomInfo tends to serve mid-to-large enterprises who are willing to invest in a robust data solution. If you just need a handful of leads or candidates occasionally, ZoomInfo is likely overkill and overpriced for that. But if your team’s output (sales deals or hires) is heavily dependent on constantly having fresh, accurate contact info, the ROI can justify the cost.
One interesting note: because of the cost, some companies use ZoomInfo in a targeted way – e.g., use other cheaper APIs for bulk prospect building, and then only call ZoomInfo’s API to “verify” or fill gaps for high-value targets where accuracy matters most. ZoomInfo does offer a Contact Verify API where you send in a list of contacts and it will return which ones are still good or enrich them – it costs credits but helps clean your data.
Strengths and Limitations: ZoomInfo’s strength is clearly its depth and reliability of contact data. It also has very powerful filtering (e.g., you can filter by department, seniority, company revenue, technologies used in company, etc., making it easier to zero in on the right people). Their data is constantly refreshing (they often detect job changes quickly, sometimes faster than people updating their LinkedIn). For many, ZoomInfo data becomes the “source of truth” for company and contact info in their internal systems.
However, a few limitations: While global, ZoomInfo’s data coverage can be uneven – the US is extremely well-covered, Europe is strong but sometimes missing personal emails due to GDPR (they have increased GDPR-compliant data for Europe, but quantity might be less than US), and other regions like Asia or Africa might have less coverage. They do charge uniformly high prices, even if you only need one region’s data. Also, because they gather a lot of data via third-party contributions (they had a Community Edition where users traded their Outlook contacts for access, etc.), occasionally information can be outdated or wrong – e.g., a person left a company but ZoomInfo still shows them there if it hasn’t caught up. They mitigate this with verification steps, but no database is perfect. ZoomInfo is also quite strict about usage – sharing data externally or using it beyond the agreed use case can breach terms, so you must use it for your own direct business needs (not reselling the data, for instance).
Privacy-wise, ZoomInfo has to comply with laws, so they provide opt-out mechanisms and only allow use of the data for legitimate interests (like B2B outreach). If you’re using their API in an application that shows data to end-users, you likely need to have some agreement in place that those end-users also won’t misuse the data.
In conclusion, ZoomInfo remains a top-tier people search and enrichment API for 2026 if budget is not a primary concern and you need high-quality contact data at scale. It’s favored by enterprises for its comprehensive profiles and constant updates. The API integration can seamlessly power sales and recruiting workflows by injecting emails and phone numbers of prospects directly where you need them. Just be prepared to invest significantly, and consider it more of a “premium data service” rather than a scrappy DIY solution. For organizations that rely on continuous outreach and can’t afford bad data, ZoomInfo is often viewed as worth the cost – hence its dominance in the market. That said, the hefty price tag has paved the way for a number of competitors offering more affordable (if sometimes smaller-scale) alternatives, several of which we’ll cover next.
Clearbit is a well-known name in the data enrichment space, focusing on both people and company intelligence. As of late 2025, Clearbit’s technology has been integrated into HubSpot’s platform (rebranded internally as “Breeze Intelligence”), but it still operates with its own APIs and identity outside of HubSpot as well. Clearbit made its mark by offering simple, developer-friendly APIs that can take an identifier like an email or domain and return rich information about the person or company behind it. It’s widely used to enrich leads (e.g., augment a sign-up’s email with their name, title, company, social profiles) and for prospecting via its “Prospector” API.
Data and Capabilities: Clearbit’s People API provides detailed profile info given something like an email address (or company + name). The data typically includes full name, job title, seniority, company name and details, location, social media handles (they often provide LinkedIn, Twitter, GitHub usernames if found), and even a profile photo URL. It essentially links an email to a person’s digital footprint. For company lookups, Clearbit can take a domain and return firmographic info (size, industry, tech used, etc.). Clearbit aggregates data from a variety of public and third-party sources, aiming for accuracy and currency. While not as massive as ZoomInfo, Clearbit advertises a global reach of around 389 million contacts and 50 million companies in its dataset, which is substantial. It refreshes its entire dataset roughly every 30 days to ensure data isn’t stale. That means if someone’s role or email changes, within a month that should be reflected (and sometimes sooner if their systems detect changes earlier).
One interesting feature Clearbit had pioneered is real-time enrichment – as soon as a new lead comes in or a user signs up on your product, a call to Clearbit’s API can instantly append all this info so you can personalize responses or route leads appropriately (for example, flagging if a sign-up is a “CEO at a 500-person company” vs “Student”). This real-time aspect is a big reason SaaS companies integrated Clearbit in their growth stacks.
Clearbit also had a Prospector API which allows searching for people at a given company by role. For example, you could query “find all VP of Engineering at Company X” and it would return names and emails if available. This is more restrictive than a broad people search (you need a specific company), but it’s useful when you know the target company and want a list of relevant contacts.
Integration (HubSpot Breeze): The acquisition/partnership with HubSpot means that Clearbit’s data is now directly available to HubSpot CRM users for enrichment and analytics. If you’re in the HubSpot ecosystem, this is great because the data flows natively (hence the term “Breeze Intelligence” implying a seamless integration). For API users outside of HubSpot, Clearbit still functions like before – you’d use their REST API with an API key to get data. It remains very developer-friendly, with clear documentation and examples. They support bulk lookups as well, which is handy if you need to enrich lists of emails in batch rather than one by one.
Use Cases: Clearbit is often the choice for startups and growth teams who need data enrichment without the enterprise complexity of something like ZoomInfo. Common use cases include: enriching sign-up forms (to reduce the number of fields a user has to manually fill out), lead scoring (using enriched data like company size or role seniority to prioritize leads), personalized marketing (knowing the industry or role of a person to tailor messages), and simple people search for sales (e.g., find contacts at target accounts). Recruiters may use Clearbit to quickly get a candidate’s public info (like if you have a personal email from a resume, Clearbit might reveal their LinkedIn or GitHub so you can vet their background more). Also, Clearbit’s social media data (like Twitter handle, GitHub) is something some other B2B datasets don’t always provide, which can be useful for getting a fuller view of a person.
Clearbit’s email finding and verification are strong but generally focused on work emails. If you have a person’s name and company, Clearbit can often guess or retrieve their work email (and verify it). This is great for sales outreach. However, it doesn’t usually provide personal emails (e.g., Gmail) because it’s geared to B2B interactions.
Pricing: Clearbit historically offered a free tier (for small volumes or during trials) and then credit-based pricing where you pay per lookup or per month for a set of credits. Their pricing for enrichment was something like $0.01–$0.02 per enrichment for large volumes, but packages would start in the few hundred dollars a month for teams. There was mention in some sources of plans starting around $999/month for full access, which likely refers to a more comprehensive plan for bigger companies. However, for smaller needs, they had plans in the <$100 range too (for limited usage). The key is flexibility – you buy credits and use them for either person or company enrichments, etc. Now under HubSpot, if you’re a HubSpot customer, there might be new pricing bundles that include Clearbit data.
Compared to ZoomInfo, Clearbit is generally more affordable and flexible for lower volumes. But it also doesn’t give you giant lists of leads out of the box; it’s more about enriching or doing targeted finds. If you needed to, say, build a list of 10,000 contacts at various companies from scratch, Clearbit’s Prospector could do some of that, but you might quickly hit credit limits or need to combine it with other data.
Strengths and Limitations: One of Clearbit’s strengths is simplicity and speed. It’s called by many as a very straightforward API that “just works” – you send an email, you get structured JSON with predictable fields like employment.title, employment.company, person.name.full, person.linkedin, etc. It’s easy to integrate in code. It’s also quite fast, making real-time user enrichment feasible (low latency responses). Another strength is being developer-centric and having good support for startups; it gained trust in the tech community early on.
In terms of coverage, Clearbit is broad but not exhaustive. If an individual isn’t in their index (which might happen if the person has a very private presence or works at a very small firm), you might get no result or partial data. Clearbit’s data on companies (like number of employees, technologies used) is rich, but for people, it focuses on professional personas. It might not include as much personal info as something like Pipl would (Clearbit isn’t designed for personal background checking, for example). Also, Clearbit’s updates every 30 days mean that very recent changes might not reflect instantly – although for most use cases a few weeks lag is fine.
Clearbit had some limitations on search – outside of the Prospector by company, you couldn’t just query “find all marketers in New York” directly. It was more oriented to enrich or find within a company. This is where competitors like Lusha or Apollo (coming next) expanded capabilities to allow broader searching on multiple criteria through their platforms.
Finally, privacy compliance: Clearbit is GDPR and CCPA aware; they process business data and allow opting out. Since they deal with less “sensitive” personal data (mostly business context), the compliance risk is lower, but companies using Clearbit still should disclose that they enrich user data using third-party sources (often done in privacy policies) because, for example, if you auto-enrich an EU user’s data upon signup, you should inform them that you obtained their info from a third-party source.
Conclusion: Clearbit stands as a developer-friendly, mid-market people data API that excels in enrichment and quick insights. By late 2025 and into 2026, with its integration as HubSpot’s Breeze, it shows the trend of data enrichment becoming a built-in feature of CRM and marketing platforms. It’s an ideal choice if you need to quickly turn minimal info into a fuller profile – say, turning an email into a name, title, company, and LinkedIn URL – to streamline your sales or onboarding processes. While it may not replace the giant databases for huge lead list generation, it often works in tandem with them: for instance, you might get a raw list of emails from somewhere and use Clearbit to fill in the gaps about those people. Its ease of use and reasonable pricing tiers have made it a staple for many growth-stage companies looking to leverage data without breaking the bank.
Cognism is a UK-based people search and sales intelligence platform that has risen to prominence by focusing on global data compliance and high-quality contact info, particularly in Europe. Often thought of as a European alternative to ZoomInfo, Cognism provides B2B contact data (people and companies) and emphasizes its strengths in areas like mobile phone data and GDPR-compliant practices. It’s trusted by many companies (especially in EMEA) for finding prospects and candidates with verified contact details.
Data Coverage and Specialties: Cognism claims a database of about 400 million professional profiles worldwide. This includes a large number of contacts in Europe, which historically were harder to find from US-centric providers. One of Cognism’s flagship offerings is what they call Diamond Data®, which refers to phone contacts that are verified by hand (essentially, phone numbers that have been phone-called or otherwise validated for correctness). They boast around 12.5 million phone-verified contacts in that subset. This is valuable because getting someone’s mobile or direct dial is gold for sales outreach – and having it verified means you’re less likely to call a wrong number or reach a gatekeeper.
Cognism’s profiles include typical fields: name, job title, company, work email, phone number, location, etc., along with some extras like intent signals (e.g., if the company is hiring or if a person shows buying intent, depending on what data they gather). They might also provide social links (LinkedIn profile URLs) to help you quickly identify the person. In terms of global reach, Cognism has strong coverage not only in the US and UK but across continental Europe and other regions, claiming compliance with GDPR to allow usage of that data. They also have a healthy database of EU mobile numbers and emails – something not all competitors can claim, due to stricter regulations and sourcing challenges in Europe.
API and Integration: Cognism offers data via an API (they support REST and even GraphQL for flexibility). Through the API, you can perform people search by criteria, enrichment by email, or get company data. They also provide webhooks for real-time updates, which is a developer-friendly feature if you want to be notified of new data or changes. In addition to their API, Cognism integrates with CRMs and sales engagement tools, similar to others in the space. But focusing on the API side – you’d typically authenticate and then use endpoints like /search/people with filters (title, location, etc.) or /enrich/person with an email to get info on a single person.
One interesting integration Cognism has is with outbound sales tools: for example, they can connect with Outreach or Salesloft to push contacts directly into campaigns. This shows they are very sales-team oriented. For our context (people search APIs broadly), Cognism can absolutely be used for recruiting as well – if you’re sourcing candidates, having personal emails or phone numbers is a bonus (though ensure you handle personal data carefully).
Use Cases: The primary use case is sales lead generation – building lists of potential customers and reaching out via phone or email. Sales teams love the phone data because cold calling is still alive and well, and reaching someone on their mobile can be more effective than a generic email. Cognism’s data is also used in marketing for campaigns and in recruitment for finding candidate contact info outside of LinkedIn. For instance, a recruiter could use Cognism to get the personal email or phone of a software engineer they found on LinkedIn, enabling them to approach the candidate directly rather than via InMail. Given Cognism’s focus on verified contacts, the likelihood of reaching the person is higher.
Cognism also provides intent signals and tracking – they incorporate data like when a company is growing or has funding, so you can time your outreach. They even have a feature to notify you when a contact shows buying intent (by consuming certain content). These advanced features might not all be exposed via raw API, but they illustrate the intelligence layer Cognism adds on top of raw data.
Compliance and Differentiator: A key selling point for Cognism is being fully compliant (GDPR, CCPA) and having certifications like SOC 2. This is crucial for companies operating in regulated environments or just concerned about data privacy. They have processes for managing consents and opt-outs. In Europe, using a data provider that is GDPR-compliant provides reassurance that you can legally contact those prospects (under legitimate interest, etc.). Cognism often emphasizes that their data usage has a legal basis and they maintain a do-not-call list and other safeguards.
Pricing: Cognism doesn’t publicly list fixed prices – like others, it’s typically a subscription model. They cater to various company sizes, starting from smaller packages up to large enterprise deals. Anecdotally, Cognism’s pricing tends to be slightly more affordable than ZoomInfo for similar volumes, which has made it attractive especially to UK and EU customers. They often customize quotes based on whether you want just data or the whole platform (they have a UI platform as well for sales reps). Also, features like Diamond Data might be add-ons (you pay more to access the phone-verified contacts). For example, you might have a base plan for emails and a premium for mobile numbers. This modular approach lets companies choose how much to spend based on their needs.
Strengths: The main strengths are data quality (especially phones & EU data) and compliance. Cognism also has good customer support and onboarding – they often assign an account manager to help new customers get value quickly, indicating a focus on user success. Their API being versatile (REST/GraphQL) is nice for developers who want to integrate it in custom ways. Additionally, Cognism’s data tends to be updated frequently (they mention nightly re-verification for some data points), so you get up-to-date info.
Limitations: While Cognism is strong in Europe, it may have slightly less coverage in some areas compared to ZoomInfo in raw numbers (ZoomInfo has been aggressive in data acquisition globally). However, if Cognism has enough of the relevant data you need, that’s not an issue. Another potential limitation is that if you are a very small business or individual developer, Cognism might feel more “enterprise” in its approach – it’s geared toward teams with a budget and strategy for using the data. They may not have a self-serve monthly plan as low-cost as something like Apollo’s entry-level. Also, their focus is B2B – they won’t have much on consumers or people outside professional contexts.
For recruiting, one must consider that contacting a candidate on their personal phone might be seen as intrusive if not done thoughtfully. The same data that’s great for a sales cold-call might require a gentler touch in recruiting (but having that contact is still valuable). Recruiters using Cognism should likely use emails first and be mindful of local regulations (e.g., times not to call, etc., especially if reaching personal numbers).
In summary, Cognism has emerged as a key player in the people search API market by delivering high-accuracy contact data with a compliance-first mindset. It’s an excellent choice if your organization values having verified direct dials and wants to ensure data usage is above board, particularly in Europe. The API provides robust people search and enrichment capabilities, and the platform’s extra intelligence features add context that can be very useful. For many companies in 2025/2026, Cognism is seen as a more modern, perhaps leaner alternative to the giants, and it has earned trust especially in regions where trust is paramount. If your focus is quality over sheer quantity – e.g., you prefer to have 1,000 excellent contacts rather than 10,000 mixed-quality ones – Cognism should be on your shortlist.
Lusha is another popular people search and enrichment provider, known for its simplicity and focus on providing contact details for B2B leads. It started as a browser extension that would show you contact info (emails, phone numbers) when viewing profiles on LinkedIn or other sites. Over time, Lusha expanded to a full platform with an API and a large database. It’s particularly favored by small-to-mid size sales teams and recruiters who need a quick, affordable way to get in touch with prospects or candidates.
Database and Coverage: Lusha’s database is large but slightly more modest in scale compared to some big players, which actually works fine for targeted use. As of 2025, Lusha had about 100 million people profiles and tens of millions of company profiles. They often break down their counts by region or type in marketing material: for instance, roughly 45 million contacts in North America and 21 million in Europe were cited, along with millions in other regions. This indicates a strong coverage in the US and decent coverage in Europe (with GDPR compliance, as they emphasize). They also list about 50 million profiles for enterprise businesses and 44 million for SMBs, suggesting they cover a range of company sizes. What this means practically is that Lusha can likely provide data on most common prospects (especially in tech, sales, marketing roles which are often targeted) but might not have as deep penetration into very obscure niches or extremely senior/executive levels as a more expensive database that sources multiple ways. However, for a huge swath of common use cases, Lusha’s data is ample.
The kind of data Lusha provides for a person typically includes: name, current role and company, work email (and sometimes personal email if they have it, but usually professional emails), phone number (often a mobile or direct line, if available), company info (industry, size), and social profile links (like LinkedIn URL). The data is aggregated from various sources, and Lusha uses some community-driven approach as well (similar to how ZoomInfo had done, they may use user contributions to validate data). They claim to use AI to continuously refresh and cross-check data – for example, if a user finds a number is wrong, Lusha’s system might flag and update that.
API and Platform: Lusha offers an API for programmatic access, while many users still use the web app or browser extension for manual lookups. The API allows you to do things like search for contacts by criteria (company, name, etc.) or directly retrieve a person’s contact info by inputting an identifier such as a LinkedIn profile URL or email. It’s a RESTful API that returns JSON. They also have bulk functionality – for instance, you can upload a list of people or companies and Lusha will enrich them in batch.
One thing Lusha is known for is its straightforward credit system. Each contact lookup or reveal costs a credit. They often give a few free credits monthly on the free plan (like 5 credits per month free), and then paid plans give you more. This model is very transparent: if you need more contacts, you move up to a bigger plan or buy extra credits.
Lusha integrates with CRM systems (Salesforce, etc.) and provides plugins, which is great for workflow. But focusing on the API usage: a developer could integrate Lusha into an internal tool so that when a salesperson enters a company name and job title, the system hits Lusha’s API to retrieve a list of matching people and their contacts, which is then displayed or stored.
Use Cases: Sales development reps and recruiters are the main users. For example, a recruiter finds a promising candidate on LinkedIn – rather than sending an InMail (which might go unanswered), they use Lusha to get that person’s work email or phone and reach out directly with a more personalized message. Similarly, a sales rep identifying decision-makers at a target company can use Lusha to quickly get the email and phone of the CTO or Head of Procurement they need to contact. Lusha’s data is also used to enrich CRM records: if you have only a person’s name and company, Lusha can fill in their contact info so your database is more complete and your team can start outreach immediately.
Small businesses appreciate Lusha because it offers a free plan and affordable paid plans. The free plan (often 5 credits/month) lets you test the waters. The paid plans start around $49 to $99 per month per user for a decent amount of credits, which is far less daunting than multi-thousand-dollar contracts. There’s also typically a team or “Scale” plan where credits are pooled and unlimited usage might be offered at a higher flat rate. This tiered approach means Lusha scales from an individual user (like a solo recruiter can literally just spend $50 in a month and get what they need) up to larger teams that might negotiate custom deals.
Compliance: Lusha, like others, had to adapt to data regulations. They are GDPR and CCPA compliant, and note certifications like SOC 2 and ISO 27701 for privacy. They ensure that the contacts provided are business-related and that they manage removal requests. For example, if someone doesn’t want their info shared, they can opt out from Lusha’s database. Lusha’s approach to compliance includes things like not providing personal emails in the EU unless allowed and marking which contacts are okay to use (21 million GDPR-compliant EU contacts suggests they have consent or lawful basis for those).
Strengths: Lusha’s main strength is its user-friendliness and cost-effectiveness. It’s easy to get started, you don’t need a lengthy sales process – you can sign up on the website, get a few credits, and immediately see value by pulling a contact. The interface (and by extension, the API’s simplicity) is attractive to those who want quick results without dealing with complex filters or data dumps. Also, the data focusing on emails and direct dials is exactly what many want – it’s very action-oriented (find the person, contact the person). The addition of some AI features (like daily AI-recommended prospects or an “Intent Mode” that regenerates contacts dynamically based on buying signals) shows Lusha is innovating to keep users engaged and targeting the right people.
Limitations: Since Lusha’s database is partly built via community and partnerships, sometimes users have reported that certain contacts might be outdated (e.g., someone changed jobs but Lusha still lists them at the old company). They do refresh data in real-time to an extent, but maybe not to the level of ZoomInfo’s army of data curators – hence why Lusha is cheaper. The good news is they give free credits monthly, so you can always verify a bit. Another limitation is that heavy-duty search (like complex multi-criteria queries) is less granular than some enterprise tools. Lusha’s search is good (you can filter by job title, company, country, etc.), but for example, it might not have 20 different filters like “years of experience” or “company revenue” in the way others might. It’s streamlined.
Also, Lusha’s focus is B2B sales; if you needed something like verifying someone’s identity or finding personal background info, that’s not the goal here. It’s strictly professional context.
Lastly, keep in mind any data pulled from Lusha should be used responsibly – just because you have someone’s cell phone doesn’t mean you should call it at dinner time or spam it with texts. The quality of outreach still matters. Lusha even provides a community feedback portal and support which is nice if something’s off (they have live chat support for paid users, etc.).
In summary, Lusha is among the best people search APIs for organizations that need a practical, budget-friendly way to get contact info for leads or candidates. Its growth in the market shows that many prefer an agile tool that can be used as needed without massive contracts. It delivers the core needs – who to contact and how to reach them – and does so with a decent level of accuracy and compliance. Many companies in 2026 will continue to use Lusha as either a primary data source or as a backup to fill gaps left by others. It embodies the trend of democratizing access to contact data, making what used to be enterprise-only (finding direct contacts) available to anyone with a browser or an API key.
Apollo.io is a platform that has gained huge popularity, especially among startups and growth hackers, as a one-stop solution combining a people database with sales engagement tools. Apollo offers a vast contact database (comparable to Lusha or even approaching ZoomInfo in size) and integrates it with features like email sequencing, A/B testing, and CRM syncing. For our focus, Apollo’s People Search API and enrichment capabilities allow you to programmatically find and retrieve prospect or candidate information.
Data Size and Sources: Apollo.io boasts over 210 million contacts in its database. These contacts come from a mix of sources: Apollo has its own web crawling and partnerships, and it also had a community aspect where users could contribute data by connecting their email accounts (similar to how some others operate). This means Apollo’s database is quite comprehensive and often updated. It contains professional profiles across industries worldwide, including emails (business emails predominantly) and often phone numbers.
Apollo is known for having a robust set of filters (over 65 filters) for searching contacts. You can filter by job title, department, company size, location, keywords in profile, technologies used at company, and so on. This granularity is reminiscent of LinkedIn Sales Navigator’s search, but Apollo’s twist is that once you find the contacts, you immediately get their direct contact info for outreach – eliminating the need to find that info elsewhere.
Apart from direct search, Apollo also does enrichment: you can give it a single identifier like a LinkedIn URL or email and it will return the profile with all the fields they have. The data returned usually includes name, title, company, work email, possibly a mobile number if available, LinkedIn URL, location, and sometimes notes like if the contact was recently active or if the company is hiring, etc.
API and Developer Use: Apollo offers an API where you can do everything from listing contacts based on queries to enriching a single contact. Developers can integrate Apollo to, for example, automatically build prospect lists for sales reps or to enrich incoming leads. Apollo also has webhooks and a strong developer platform, which means you can set up triggers (say, a new contact is added to a list in Apollo, it auto-syncs to your app via webhook). For those building an internal tool or a startup that needs people data, Apollo’s API is quite appealing because of the usage-based model and the breadth of data.
One distinctive aspect of Apollo is its bulk capabilities. If you need to enrich hundreds of records, Apollo can batch that efficiently. Also, Apollo’s system can verify emails in real-time when you go to send (they try to ensure deliverability ~ they advertise high deliverability rates for the emails they provide, since they verify them).
Built-in Engagement Tools: While not directly about the API, it’s worth noting that Apollo’s platform includes email sending, tracking, and sequence automation. This indicates their data is meant to be acted on quickly. With the API, a company could theoretically pull a list of contacts and then plug them into their own email system or another engagement tool, but many just use Apollo’s built-in system. If you’re using just the API, you’ll likely integrate with your CRM or ATS. For example, a recruiting team could use Apollo’s API to find 50 candidate emails matching a profile, then automatically import those into their recruiting CRM for the sourcers to reach out.
Pricing: Apollo.io is known for its flexible pricing and a generous free tier. They often have a free plan that gives, say, 50 free credits (contacts) plus some phone credits, making it very accessible to try out. Then they have paid plans which might start around $49/month per user on an annual contract, which includes a certain number of credits, and higher plans around $99-$119/month with more features or unlimited credits for that user (in reality they often have fair use limits). They also allow purchasing extra credits or going usage-based – notably, Apollo’s API is usage-based where you pay for the successful API calls (the data you actually get). This pay-as-you-go model is nice because you only pay for what you need, and there are no hidden fees beyond maybe the difference between basic and premium data (if they have tiers for things like mobile numbers).
Compared to ZoomInfo’s big contracts, Apollo is much more startup-friendly. You can spend a few hundred dollars and get thousands of contacts easily. That said, Apollo’s data, while broad, might require some cleaning (like verifying role is current) because if someone left a job and Apollo hasn’t updated yet, you might pull outdated info. They mitigate that with monthly audits and an opt-out system.
Use Cases: Apollo is widely used for sales prospecting, but it’s also used for recruiting, marketing, and even market research. For recruiting specifically, Apollo can find lots of tech professionals’ emails because many tech folks use their work email online in some context. If you’re sourcing, say, software engineers in a region, Apollo could be an alternative to LinkedIn Recruiter for initial contact (though not all engineers’ emails are easily found, many are). For sales, Apollo is used to build targeted lists like “all e-commerce companies in USA using Shopify, find me the Marketing Directors there.” Because Apollo knows company tech stacks (they integrate builtwith data for example) and has those filters, you can do that kind of search.
Apollo’s data is often used by growth hackers who want to quickly test campaigns – e.g., they come out with a new product for HR managers, they go to Apollo, pull 200 HR manager contacts in their vertical, and run an outreach sequence.
Strengths: Apollo’s strengths include cost-effectiveness, a large database, and combined functionality. The fact you get both the data and the toolset in one is great for teams that don’t want to juggle multiple platforms. For developers, the strength is the API is well-documented and they even have an API Explorer and a community around it. Also, Apollo’s UI or API allows easy export if needed (you’re not too locked in; you can get your data out via CSV or API). Apollo also regularly updates its features, influenced by a large user base (for instance, the mention of them doing “opt-out compliance” and data checks shows they adapt to user needs and regulatory requirements).
Limitations: Apollo’s huge growth meant that early on, some users found data quality issues (like duplicates, outdated records). They have improved a lot, but you may still find occasional inaccuracies. It’s always good practice to double-check high-value contacts manually or through other means. Another limitation is that because Apollo is used by many, some prospect email addresses might get a lot of outreach – meaning if you use Apollo to email a certain CTO, chances are some other sales reps have too. This isn’t unique to Apollo, but it’s something to consider – highly targeted accounts might be fatigued if they appear on popular data platforms. Standing out with personalization and good messaging is key.
From a technical perspective, if using the API heavily, watch out for rate limits or cost – if you inadvertently trigger calls for massive results sets, you could use a lot of credits (Apollo’s API might return paginated results for large searches, so you’d need to iterate carefully).
Privacy: Apollo has an opt-out link on their site for individuals and they comply with GDPR by removing or marking those who opted out. As a user of the data, you should still treat it carefully (for example, if you import Apollo contacts into EU, you might want to do legitimate interest assessments or ensure your outreach includes an opt-out option for them).
In conclusion, Apollo.io stands as a comprehensive and highly accessible people search solution in 2026. It’s become a favorite for many because it merges a broad data engine with practical tools and an API, all at a reasonable price point. For teams that want quick results without enterprise red tape, Apollo is often the answer. It embodies the modern approach of “find the data and act on it in one flow,” which is incredibly efficient. Whether you’re a startup growth lead building a sales pipeline or a recruiter expanding your candidate reach, Apollo can serve you well. Just use the power responsibly – with great amounts of data comes the responsibility to personalize and humanize your approach, something Apollo’s data can enable but not replace.
Pipl takes a different angle compared to the primarily B2B-focused APIs we’ve discussed so far. Pipl is known as an identity resolution and people search API that connects the dots on individuals across the deep web. It’s often used in fraud detection, background checks, and investigative contexts, but its vast coverage of personal data also makes it useful for recruiters or others who need to find comprehensive info about a person using minimal input. Pipl’s specialty is that you can start with just a fragment of information – say a name and city, or a phone number, or an email address – and Pipl’s engine will search its index to return a consolidated profile of that person (aggregating data from social media, public records, professional sites, etc.).
Database Size and Content: Pipl has one of the largest people databases in the industry, claiming over 3 billion online identities indexed. Essentially, Pipl continuously crawls and collects pieces of identity information from all over the internet (think social network profiles, personal websites, forums, public records like court filings or business registrations, leaked databases, etc.) and uses algorithms to link those pieces together into “identity clusters.” So, for a given individual, Pipl might have found their LinkedIn profile, a Twitter account, perhaps a mention in a news article, a phone number listed on a business site, an old blog post, etc., and it will connect those if they appear to refer to the same person.
When you query Pipl (via API or their search UI), you can input anything you know: email, phone, full name, username, physical address, etc. Pipl will then return possible matches with a confidence score. A Pipl profile output can be very rich – it might include the person’s full name, age or age range (if deducible from public records), various addresses (current and past), phone numbers, email addresses, social media usernames (LinkedIn, Facebook, Twitter, GitHub, etc.), work history snippets (from profiles or mentions), education (if found), and even names of possible relatives or associates (like if public records tie two people at the same address, etc.). It’s essentially a people search engine on steroids that spans both professional and personal data.
For a recruiter or marketer, Pipl could be used to find alternate contact means for someone. For example, if you only have a candidate’s Stack Overflow handle and first name, Pipl might help identify their full name and an email. Or if you have a personal email, Pipl might give you their LinkedIn and work info.
Global Coverage: Pipl is truly global in coverage – they span data sources from many countries. That said, they are very focused on identity verification uses, so they ensure their data usage is in line with privacy laws (for instance, in the EU they might limit certain personal info unless you have a lawful use like fraud prevention). They advertise that they have data across the globe; indeed global coverage is a strong suit – whether it’s a professional in the US or a citizen in another country, Pipl likely has some data point connecting to them, given the ubiquity of digital traces.
API Usage: The Pipl API (often called the Pipl Data API) allows sending a search query in JSON with whatever information you have (they support a structured input of fields like first name, last name, email, phone, country, etc., or you can just do a single string query). The response will contain one or multiple profiles that match. Each profile comes with fields and a confidence score. For example, if you search by an email, usually you get one profile that’s a high confidence match to that email, containing everything Pipl knows tied to that email.
Pipl’s API is used in all sorts of apps: onboarding verification (checking if a new user is real by seeing if their email or phone has a history), fraud scoring (flagging if an email/phone combo doesn’t align with a person’s name/location as given), or investigative tools (like Maltego transforms using Pipl to pivot from one identifier to others). In recruiting, it could be integrated in a sourcing tool: imagine you have a list of names from a conference, you could feed them to Pipl to get their contact and social info.
Accuracy and Updation: Because Pipl aggregates from many sources, some data could be outdated (e.g., an old address), but they typically label fields with timestamps or source info. They continuously update their index, so new info appears as it’s found. One advantage is the breadth – sometimes Pipl will show records that you wouldn’t find via normal search engines, like a mention on a small community site, or an old MySpace profile, etc. That’s why it’s favored by investigators.
However, Pipl’s mission is more about verifying identity and providing a complete picture rather than specifically listing “that person’s current job title and work email”. It might have those if they appear in some source, but Pipl isn’t geared to maintain a clean, up-to-date resume-like profile for someone – other APIs (like PDL or Coresignal) are better for occupational info. Instead, Pipl excels at finding contact details and connecting identities. For example, it might reveal that the John Doe you’re looking at (with a given email) also has a second email and a Skype ID – which could be useful in different contexts.
Use Cases and Limitations: Pipl is used by background check services, law enforcement, fraud prevention teams, and also by recruiters and journalists who need to dig into someone’s footprint. For recruiting, the benefit is to discover personal contact info (like personal email or cell) for a passive candidate, or to verify that a candidate’s provided info matches their public profiles (for instance, ensure they didn’t lie about employment – if Pipl finds a LinkedIn under that name with a different history, that’s a clue).
A limitation is that Pipl’s rich data needs to be handled carefully under privacy laws. The user of the API must have a permissible purpose (Pipl itself requires users to agree to terms that they’ll use the data in legally allowed ways). It’s not meant for casual browsing of anyone; typically it’s used when you have a reason (like verifying an identity in a transaction).
From a technical perspective, the Pipl API is a bit heavier than others – the responses can be big, and you might have to parse through multiple results if the input is common (e.g., searching “John Smith in New York” will yield many possible matches). The API will give you scores and likely you’ll pick the top match.
Pricing: Pipl tends to offer enterprise packages given its focus on commercial use cases. They might have tiers based on number of searches. It’s not as self-serve as, say, Apollo or Lusha. However, for moderate use, some smaller plans or pay-per-search could be available via resellers. Because it’s a premium service (it helps catch fraud for banks, etc.), the pricing might be higher per query compared to a basic contact lookup service. But considering one Pipl search can return not just an email but a whole person’s dossier, the value is correspondingly high.
Strengths: Comprehensiveness and flexibility of input. Pipl will take whatever you have and try to make sense of it. That’s incredibly useful when other APIs fail because you don’t have exact details. It’s also great for finding personal contact info (personal emails, phone numbers) that purely professional databases might ignore or not have. And it doesn’t limit to business people – it includes folks who might not have a LinkedIn but have other traces online.
Limitations: It’s not a curated “business profile” database. So if all you need is someone’s current job title and work email, Pipl might be overkill and possibly not as direct. Also, false positives can occur – if two people share a name and city, info might intertwine (Pipl tries to separate them, but it’s not impossible some data crosses if the signals are similar). Usually, though, the profiles are well-resolved.
For recruiting specifically, Pipl should be used ethically – e.g., if you get a personal phone, it might be best to email first. But having that option means you’re not stuck waiting on a LinkedIn message. It can increase response rates when used properly.
In summary, Pipl is like the ultimate people search API for deep and broad identity data. In 2026, as digital footprints grow, Pipl’s relevance in verifying and connecting identities is huge. It complements the other APIs in this list: where those focus on professional attributes and publicly shared contact info, Pipl can fill in the gaps with more personal or obscure data. It’s a powerful tool when you have little to go on, and it’s often the secret weapon behind many investigative search tools. For any application requiring thorough people lookup across the web’s long tail, Pipl is a top choice.
Trestle IQ (often just called Trestle) is a people data API that has gained attention for its emphasis on identity verification and detailed location-based data. It’s a bit different from some others on this list in that it heavily focuses on linking people to physical addresses and ensuring high accuracy in matching identities (useful in background checks, fraud prevention, etc.), while still offering robust people search for sales or recruiting. Think of Trestle as an API that bridges the gap between traditional contact databases and more verification-oriented datasets like address records.
Data Coverage and Unique Features: Trestle IQ’s dataset includes around 479 million individual identities, which is sizeable. A standout feature is what they call a “Proprietary Address Graph” – essentially a huge map of 1.79 billion address-to-name linkages with very high accuracy. This means Trestle excels at confirming where a person lives or has lived, and matching people to addresses (and by extension, perhaps to other household members, etc.). For many industries, that’s crucial (e.g., verifying someone’s address in financial services, or finding a current address for collections).
In the context of people search, how does that help? For one, it ensures that if you’re searching for “Jane Doe” and you have a location, Trestle can disambiguate and find the right Jane Doe with more confidence by using address history. Also, if you find a candidate and need to know if they’re open to relocation or confirm their general location, an address match can be useful data.
Trestle’s people profiles include typical professional info (name, employment, etc.), but also include demographic info like addresses, possibly age or DOB range, and other identity markers. They pull from sources like public records, phone directories, social profiles, and professional networks. It’s a blended dataset where you might get a bit of what you’d see from Pipl (addresses, phones) along with what you’d see from a business contact DB (jobs, skills).
They also emphasize real-time updates and high query throughput. Trestle’s API can handle up to 1,000 queries per second with 99.99% uptime, indicating it’s built for enterprise scale (like if you needed to verify millions of records quickly). Data freshness in Trestle is maintained continuously – for instance, they claim some details update within minutes (like detecting a phone carrier change) and bigger changes like address changes are checked monthly.
Pricing Model: Trestle IQ is relatively transparent in pricing from what’s been shared: they have a Starter plan around $220/month for 1,000 queries, and a Small Business plan $440/month for 5,000 queries, plus enterprise plans above that. This indicates roughly ~$0.22 per query at the starter level, scaling down with volume. That pricing is actually quite approachable for the capability, which suggests they are trying to attract customers away from pricier incumbents by being affordable. They also offer a free demo/trial which is good for testing.
Given that each “query” could be a person search or identity lookup, one has to manage how they use it (but the allowance of thousands per month even at a few hundred dollars is plenty for many use cases).
Use Cases: Trestle IQ is leveraged in scenarios like customer onboarding (KYC), marketing lead validation, skip tracing (finding people who moved), and more. For sales/recruiting, one might use Trestle to get a person’s comprehensive contact profile: e.g., you have a name and state from a lead list, use Trestle to fetch current address, phone, email, etc., to enrich the lead and perhaps confirm they’re the right individual.
It’s also used in compliance and fraud – if you want to ensure a person is real and consistent, Trestle’s data can cross-verify what they provided. For recruiters, if someone applies and you want to verify their past locations or identities (maybe in sensitive hires), an API like Trestle could help in the background check process (though typically specialized background check services do that as well).
Trestle also provides advanced filters and multi-field search. You can query by combinations: name + address, or phone + last name, etc. This is helpful when you have partial info. They pride themselves on 95%+ accuracy in location-based matching, which is a niche strength.
Region Coverage: They have strong data in North America and Europe (explicitly stated). Given the nature of address data, they likely have better depth in the US (where public records are more accessible) and decent coverage in Europe. If your targets are heavily in Asia or other regions, Trestle might not have as much (just as an assumption, since address linking data is often regional). However, they do incorporate social and professional data too, so they won’t be completely blind outside NA/EU; it’s just that their core differentiator (the address graph) shines in NA/EU where such data is abundant.
Developer Experience: Trestle markets itself as “developer-first” with things like SDKs, Postman collections, and great docs. They seem to focus on making integration smooth and providing good support (like dedicated onboarding for enterprise, etc.). This is important if you plan to integrate it deeply into your processes.
Strengths: Trestle’s key strengths are identity accuracy, especially around addresses and demographics, and high performance. If your use case needs fast, high-volume queries and rock-solid uptime (like real-time systems pinging an API on every transaction), Trestle is built for that. Also, the pricing is straightforward and scalable, which can be a breath of fresh air compared to some providers requiring negotiations for everything.
For companies doing global people data projects, Trestle can be a core component in ensuring data is consistent (for example, a CRM cleanup – Trestle could be used to identify duplicates by matching people via address/phone, not just name).
Limitations: If you compare Trestle to something like People Data Labs, Trestle may not have as many professional fields (like detailed resume info or hundreds of social URLs). It’s more about contactability and verification. So for pure recruiting profile depth, Trestle might not replace a LinkedIn-focused database; instead, it complements by adding verified contact info and filling missing pieces. It likely doesn’t give insights like “skills” beyond what could be inferred.
Another consideration: Trestle’s focus on personal data (addresses, etc.) means you should handle it carefully under privacy law (though addresses and phones from public records are generally allowed to use under certain conditions, you just want to be sure to treat EU data carefully and honor any suppression lists they provide).
Trestle might also be less known than others, so it’s in a growth phase – but being featured in sources like the Crustdata analysis suggests it’s proving itself.
Conclusion: Trestle IQ is a powerful yet underrated people search API that combines traditional contact data with modern needs like real-time updates and high accuracy. It’s particularly valuable if verifying and pinpointing a person’s identity is as important as simply finding them. The API’s speed and capacity make it attractive for enterprise applications that cannot tolerate downtime or lag. For organizations in 2026 dealing with big data on people – whether verifying users or enriching large lead lists – Trestle offers a solid solution. It represents how the people search field is also catering to fraud and compliance use cases, not just sales/recruiting, and in doing so, it offers benefits (like address accuracy) that purely sales-focused tools might lack. If you want an insider tip: using Trestle alongside a more resume-focused API could give you a 360° view – one gives you where they worked and their LinkedIn, the other confirms where they live and how to contact them. Such combinations are becoming a proven method in the industry.
As we head further into 2026 and beyond, we envision a world where finding the right person (for a job, for a sales opportunity, for a partnership) is less about searching and more about matching. The technology will proactively surface the best connections for our needs, scoring and ranking them, and even initiating contact. The APIs discussed here are the building blocks of that future. By harnessing them – individually or in combination – businesses can significantly accelerate how they recruit talent, generate leads, and verify identities.
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