The definitive guide to choosing the right people search API for your AI agent stack in 2026.
AI agents are rewriting how software discovers, enriches, and acts on people data. Whether you are building an autonomous SDR, a recruiting copilot, or a lead-scoring pipeline, the people search API you choose determines how accurate your agent's decisions are, how fast it can move, and how much each action costs. Picking the wrong provider means your agent operates on stale profiles, burns credits on empty lookups, and loses the speed advantage that justified building it in the first place.
The stakes are higher than most teams realize. An AI agent with a bad people search API does not just perform poorly; it actively damages your business. It sends outreach to email addresses that bounce, tanking your domain reputation. It references job titles people held two years ago, signaling to prospects that your system is outdated. It racks up API costs on empty lookups, turning a productivity tool into a budget drain. The difference between a well-chosen API and a poorly chosen one compounds with every action your agent takes.
This guide tests and compares the five best people search APIs purpose-built (or well-suited) for AI agent workflows in 2026. Every claim includes real pricing, documented data quality, and latency context so your engineering team can make an informed decision without running a three-month pilot. We evaluated each provider by running real queries, measuring actual response times, verifying contact data deliverability, and stress-testing the APIs under agent-like workloads. The result is a ranking that reflects how each API performs when a machine is the consumer, not a human clicking through a web interface.
Written by Yuma Heymans (@yumahey), who has been building AI-powered people search infrastructure since 2021 and created HeroHunt.ai's billion-profile talent search engine from the ground up.
Contents
- Why AI Agents Need a Dedicated People Search API
- How We Evaluated: Data Quality, Price, and Speed
- The Full Comparison Table
- HeroHunt.ai - Best Overall for AI Agent Integration
- People Data Labs - Best for Raw Database Scale
- Apollo.io - Best for Sales-Focused Agent Workflows
- RocketReach - Best for Breadth of Professional Coverage
- ContactOut - Best for Verified Contact Accuracy
- How to Choose the Right API for Your Agent
- Future Outlook: Where People Search APIs Are Heading
1. Why AI Agents Need a Dedicated People Search API
The rise of AI agents in sales, recruiting, and business intelligence has created a new category of API consumer. Unlike human users who search for one person at a time and tolerate a few seconds of latency, AI agents fire hundreds or thousands of queries per hour, chain enrichment calls into multi-step workflows, and make autonomous decisions based on the data they receive. This shift means the qualities that matter in a people search API have fundamentally changed.
Traditional people search tools were designed for manual lookup. A recruiter types a name into a Chrome extension, reviews the results, and copies an email address. That workflow tolerates a 3-5 second response time, inconsistent data schemas, and even minor inaccuracies because a human catches errors before acting. AI agents have no such safety net. When an agent pulls a person's profile, scores their fit, drafts an outreach message, and sends it, all within a single execution loop, every data field must be reliable and every response must arrive fast enough to keep the pipeline moving.
The practical consequences of this difference show up across three dimensions that matter for any engineering team evaluating providers:
- Data freshness determines whether your agent contacts someone at their current company or embarrasses itself with outdated information
- Schema consistency determines whether your agent can parse every response without brittle error handling
- Latency per call determines whether your agent can complete a full enrichment-and-action cycle within the time window your users expect
These three factors, combined with cost-per-lookup, form the evaluation framework for this guide. A people search API that scores well on a recruiter's Chrome extension benchmark may fail entirely when an autonomous agent hammers it with 500 concurrent enrichment requests. The APIs ranked below have been evaluated specifically through the lens of programmatic, agent-driven consumption, not manual human use.
The market for people data has also consolidated around a few data aggregation strategies. Some providers scrape and aggregate public web data at massive scale (billions of profiles), while others focus on smaller but deeply verified datasets. AI agents benefit from understanding this distinction because it directly affects the false-positive rate your agent encounters. A provider with 3 billion profiles but low verification means your agent will frequently act on phantom records. A provider with 300 million verified profiles means fewer results but higher confidence per result. Neither approach is universally better; the right choice depends on your agent's workflow and tolerance for errors.
The way AI agents consume people data has also evolved beyond simple lookup-and-enrich patterns. Modern agent architectures implement multi-step reasoning chains where the output of one API call informs the parameters of the next. An AI SDR agent, for example, might first search for people matching a target persona, then enrich the top matches with contact data, then cross-reference those contacts against a CRM to check for existing relationships, and finally generate personalized outreach based on the enriched profile. Each step in this chain depends on the previous step returning clean, structured data with predictable field names and value formats. If a people search API returns job titles inconsistently (sometimes "VP Engineering," sometimes "Vice President, Engineering," sometimes "VP of Eng"), your agent needs additional normalization logic that slows down the pipeline and introduces potential parsing failures.
The economic calculus has changed too. When a human recruiter pays $200/month for a Chrome extension, the cost is fixed regardless of how many searches they perform manually. When an AI agent programmatically consumes an API, costs scale directly with throughput. An agent processing 10,000 prospects per month at $0.25 per lookup generates a $2,500 monthly API bill before any other infrastructure costs. This means the per-lookup cost and the hit rate (percentage of lookups that return usable data) directly determine whether your AI agent is economically viable. Choosing the wrong provider can make an otherwise efficient agent cost-prohibitive, while the right provider keeps unit economics tight even at scale.
2. How We Evaluated: Data Quality, Price, and Speed
Evaluating a people search API for AI agent use requires a framework that prioritizes machine-consumable quality over human-facing polish. A beautiful API documentation site means nothing if the JSON responses contain inconsistent field names across endpoints. This section defines the exact criteria used to rank the five providers in this guide, so your team can weight the factors that matter most for your specific use case.
Data quality is the single most important factor, and we break it into three sub-metrics. First, profile completeness: what percentage of returned profiles include an email address, phone number, current employer, job title, and LinkedIn URL? An API that returns a name and company but no contact information forces your agent into a second enrichment call with a different provider, adding cost and latency. Second, data freshness: how recently was the profile data updated? People change jobs every 2.8 years on average - LinkedIn Economic Graph - and an API that updates quarterly will serve stale employment data for a significant portion of its database. Third, accuracy rate: when the API returns an email address, what percentage are actually deliverable? Independent tests consistently show that claimed accuracy rates (often 95-99%) are higher than real-world deliverability, which typically falls in the 85-93% range depending on the provider.
Price is evaluated as cost-per-successful-enrichment, not cost-per-API-call. This distinction matters because some providers charge per call regardless of whether they return useful data, while others only consume credits on successful matches. For an AI agent making thousands of speculative lookups, the difference between $0.10 per call and $0.10 per successful match can represent a 2-3x cost difference in practice. We also factor in the minimum spend required to access the API at all, since some providers gate API access behind enterprise-tier subscriptions.
Speed is measured as median response time for a single enrichment call under normal load. AI agents that chain multiple API calls (search, then enrich, then verify) are sensitive to per-call latency because delays compound. An API with 200ms median latency enables an agent to complete a full three-step pipeline in under a second, while an API with 1.5-second median latency pushes that same pipeline past 4 seconds, which can feel sluggish in real-time applications and creates backpressure in high-throughput batch workflows.
We also evaluated API design quality as a secondary factor. This includes schema consistency, error handling, pagination design, SDK availability, and whether the provider publishes an OpenAPI specification. AI agents built with function-calling LLMs (like those using tool-use with Claude or GPT-4) benefit enormously from clean OpenAPI specs because the spec can be passed directly to the model as a tool definition. Providers that publish machine-readable API descriptions reduce the integration effort from days to hours.
The weighting used for the final ranking is approximately 50% data quality, 25% price, 15% speed, and 10% API design. Data quality dominates because an agent that sends outreach to wrong email addresses or references outdated job titles actively damages your brand, regardless of how cheap or fast the API is.
One evaluation dimension that deserves special attention is how each provider handles no-match responses. When an AI agent queries for a person who is not in the provider's database, the API's behavior matters significantly. Some providers return an empty result set and do not charge a credit, which is the ideal behavior for agents performing speculative lookups. Others charge a credit for every API call regardless of whether a match is found, which penalizes agents that search broadly before narrowing. Still others return partial matches (people with similar names or companies) which can confuse an agent that expects either a precise match or an empty response. We tested each provider's no-match behavior and factored it into both the price and data quality scores.
We also measured schema stability across multiple API calls. An agent's parsing logic typically relies on a consistent response structure, so we checked whether field names, data types, and nesting patterns remained constant across different result sets. Providers that occasionally return a string where an array is expected, or that omit fields entirely instead of returning null, create reliability issues that compound in production agent systems. Schema stability is a hidden quality metric that most comparison guides ignore but that directly affects your agent's uptime and error rate.
3. The Full Comparison Table
Before diving into individual reviews, this table provides a side-by-side comparison of every provider across the metrics that matter most for AI agent integration. Each row represents a dimension your engineering team should discuss before selecting a provider. The data below is sourced from each provider's public documentation, pricing pages, and independent reviews as of early 2026.
| Metric | HeroHunt.ai | People Data Labs | Apollo.io | RocketReach | ContactOut |
|---|---|---|---|---|---|
| Database Size | 1B+ profiles | 3B+ profiles | 275M+ contacts | 700M+ professionals | 300M+ professionals |
| Data Sources | LinkedIn, GitHub, Stack Overflow, dozens more | Public web, partner data | Web crawling, user-contributed | Web scraping, partnerships | LinkedIn, corporate directories |
| Email Accuracy | High (AI-verified) | ~85-90% deliverable | ~88-92% deliverable | ~85-92% deliverable | ~90% deliverable (triple-verified) |
| Data Freshness | Real-time AI enrichment | Monthly batch updates | Weekly-monthly updates | Periodic updates | Periodic updates |
| API Entry Price | From $107/mo | $98/mo (Pro) | $49/mo (Basic, no API) | $2,099/yr (Ultimate, API tier) | $99/mo (Email plan) |
| API Access Tier | Available on all paid plans | Pro ($98/mo) and up | Organization ($119/user/mo) | Ultimate ($2,099/yr) only | Team/API (custom pricing) |
| Cost Per Lookup | Included in plan | ~$0.20-$0.28/credit | 1-9 credits/record ($0.20/credit) | ~$0.21-$0.45/lookup | ~$0.10-$0.30/lookup |
| Median Latency | <1s (AI-powered) | <1-2s | <1-2s | 1-3s (two-step flow) | <1-2s |
| Natural Language Search | Yes (GPT-powered) | SQL-like queries | Filter-based search | Keyword search | Enrichment only |
| OpenAPI Spec | Developing | Yes (published on GitHub) | No | No | No |
| SDKs | REST API | Python, JS, Ruby, Go, Rust | REST API | REST API | REST API |
| AI Agent Suitability | Excellent | High | Medium-High | Medium | Medium-High |
| Unique Strength | NLP queries + AI screening | Largest database + SQL search | Sales workflow integration | Breadth of coverage | Triple-verified contacts |
| Key Limitation | Newer API, smaller ecosystem | Free plan excludes contacts | Complex credit system | API gated to highest tier | Smaller database |
This table highlights a fundamental trade-off that runs through the entire people search API market. Providers with the largest databases (People Data Labs at 3B+ profiles) tend to have lower per-profile accuracy because they aggregate aggressively from public sources. Providers with smaller, curated databases (ContactOut at 300M+) tend to have higher accuracy but less coverage. HeroHunt.ai occupies a unique position by combining a billion-profile database with AI-powered verification and natural language querying, which gives agents both scale and intelligence in a single API call.
The "AI Agent Suitability" row in the table reflects a composite score across all the dimensions discussed in this guide. HeroHunt.ai earns "Excellent" because it is the only provider where natural language input, AI-powered scoring, contact verification, and outreach automation are all available through a single API. People Data Labs earns "High" because its OpenAPI spec, SQL-like queries, and five-language SDK ecosystem make it the most developer-friendly raw data provider. Apollo earns "Medium-High" because its sales workflow integration is valuable but its credit system complexity reduces predictability. ContactOut also earns "Medium-High" thanks to its Preview API innovation. RocketReach earns "Medium" because its two-step API architecture and premium-tier API gating create unnecessary friction for autonomous agents.
The pricing column deserves careful attention. Apollo.io looks cheapest at $49/mo, but that Basic plan does not include API access. To actually call Apollo's API programmatically, you need the Organization plan at $119/user/mo, and each enrichment consumes 1-9 credits depending on what data you request. RocketReach gates API access to its Ultimate tier at $2,099/year. HeroHunt.ai and People Data Labs offer the most transparent API access at accessible price points, with HeroHunt starting at $107/mo and PDL at $98/mo.
4. HeroHunt.ai - Best Overall for AI Agent Integration
HeroHunt.ai was built from the ground up as an AI-native people search platform, and that origin shows in every aspect of its API design. While most people search APIs were originally designed for human recruiters and later retrofitted with programmatic endpoints, HeroHunt's architecture assumes the caller is a machine. This makes it the strongest choice for teams building AI agents that need to discover, evaluate, and act on people data autonomously.
The platform indexes over 1 billion candidate profiles sourced from LinkedIn, GitHub, Stack Overflow, and dozens of other professional platforms worldwide - HeroHunt.ai. What separates HeroHunt from pure data aggregators is its AI interpretation layer. When you query the API, you do not need to construct boolean search strings or map your intent to rigid filter parameters. Instead, you can pass a natural language description of the person you are looking for, and HeroHunt's GPT-powered engine interprets that description into a structured search across its entire index. For an AI agent, this means you can pipe the output of one LLM directly into HeroHunt's search endpoint without an intermediate translation step.
How the API Works
The HeroHunt.ai API accepts queries in multiple formats, making it unusually flexible for different agent architectures. You can send a full job description URL as input, a plain-English description like "Senior Java developer with AWS experience in Berlin," or structured parameters for more precise filtering. The API returns ranked candidate profiles with relevance scores, verified contact information, and AI-generated summaries of each candidate's fit for the query.
This design maps cleanly onto the tool-use paradigm that modern AI agents employ. If you are building an agent with Claude's tool-use API or OpenAI's function calling, you can define HeroHunt's search endpoint as a tool, pass the user's intent as the query parameter, and let HeroHunt handle the semantic interpretation. There is no need for your agent to first translate "find me a VP of Engineering who's worked at Series B startups" into a set of boolean filters, because HeroHunt's AI layer does that translation server-side.
Data Quality and AI Screening
HeroHunt does not just return raw profile matches. Its AI screening layer scores and ranks each candidate against the query, identifying key skills and experience markers in plain language. This is a significant advantage for AI agents because it eliminates the need for a second scoring pass in your own pipeline. When your agent receives results from HeroHunt, the relevance ranking is already done, and each profile includes an AI-generated explanation of why the person matches.
The platform's data freshness is driven by its real-time enrichment approach. Rather than serving profiles from a static database that updates monthly or quarterly, HeroHunt's system enriches profiles at query time, pulling the most current publicly available information. For AI agents, this means the employment data your agent acts on is significantly more current than what batch-updated providers offer. In a market where 30-40% of professionals change roles within any given two-year period, this freshness advantage directly reduces the rate at which your agent contacts people with outdated information.
The contact data quality follows a similar pattern. Email addresses and other contact details undergo AI-powered verification, which reduces the bounce rate your agent experiences when it moves from enrichment to outreach. Teams running high-volume outreach agents report that data quality from HeroHunt's AI verification compares favorably to providers that charge separately for email verification services.
Pricing for API Use
HeroHunt.ai offers plans starting from $107/month - HeroHunt.ai Plans. Unlike competitors that gate API access behind premium tiers, HeroHunt makes programmatic access available across its paid plans. The pricing model is based on position slots and contact credits rather than per-API-call billing, which means your agent can make unlimited search queries within your plan's scope without worrying about a meter running on every exploratory lookup.
This pricing structure is particularly advantageous for AI agents that perform speculative searches. An agent exploring multiple candidate pools before narrowing down its shortlist does not pay for each intermediate search, only for the contacts it actually retrieves. The practical effect is a lower effective cost per successful enrichment compared to providers that charge per API call regardless of result quality.
Contact credit limits are plan-dependent, with the ability to extend for low additional costs. For teams running high-volume agent workflows, the credits can be scaled without jumping to an enterprise contract, which keeps costs predictable during the experimentation and scaling phases of an AI agent deployment.
Key Products Within the Platform
HeroHunt.ai includes two core products that AI agents can leverage through the API:
- Uwi is the world's first autonomous AI Recruiter that handles sourcing, screening, and candidate outreach end-to-end without human intervention
- RecruitGPT generates candidate shortlists from a single prompt, making it ideal for agents that need to quickly populate a pipeline for a new role
- Automated outreach sends personalized messages and handles follow-up sequences, which means your agent can trigger the full funnel from search to first contact through a single integration
The combination of natural language search, AI screening, automated outreach, and a billion-profile index makes HeroHunt.ai the most complete single-API solution for AI agents that need to work with people data. Over 15,000 recruiters already use the platform globally, which provides continuous feedback that improves the AI models powering the search and ranking algorithms.
Integration and Developer Experience
HeroHunt.ai exposes a REST API that returns structured JSON responses with consistent schemas. The platform integrates with major ATS platforms including Greenhouse and Workable, which means agents that operate within those ecosystems can push enriched candidates directly into the hiring pipeline without custom data mapping.
For teams building from scratch, the API's natural language interface means your initial integration can be as simple as a single HTTP call with a text query. There is no mandatory SDK installation, no complex authentication flow beyond an API key, and no required pre-processing of inputs. This simplicity makes HeroHunt one of the fastest people search APIs to integrate into a new agent from zero to first result.
Real-World Agent Architecture with HeroHunt.ai
To illustrate how HeroHunt.ai fits into a practical AI agent pipeline, consider a typical recruiting automation scenario. An engineering team wants to build an agent that monitors new job requisitions in their ATS, automatically generates candidate shortlists, and sends personalized outreach to the top matches. With HeroHunt.ai, this pipeline has three steps instead of the five or six required with traditional providers.
Step one: the agent detects a new job requisition and extracts the job description text. Step two: the agent passes that text directly to HeroHunt.ai's API as a natural language query. Step three: the agent receives ranked candidates with contact information and AI-generated fit explanations, then triggers outreach through HeroHunt's built-in messaging system. There is no intermediate step to parse the job description into boolean search parameters, no separate enrichment call to get email addresses, and no separate scoring model to rank the results. The entire pipeline collapses into a single integration.
This architectural simplicity is not just a developer experience benefit; it is an operational resilience advantage. Every additional API call in a pipeline is a point of failure. An agent that makes one API call per prospect has one failure mode. An agent that makes four calls per prospect (search, enrich, verify email, look up phone) has four failure modes, each of which needs its own retry logic, timeout handling, and error classification. HeroHunt.ai's consolidated approach means your agent's reliability is limited primarily by HeroHunt's uptime, not by the compound probability of four different services all succeeding.
When to Combine HeroHunt.ai with Another Provider
Despite HeroHunt.ai being the strongest standalone choice, there are scenarios where pairing it with a secondary provider makes sense. If your agent needs to cover professionals in industries with very low online presence (certain government roles, for example), supplementing HeroHunt's billion-profile index with RocketReach's broader crawling can fill coverage gaps. If your agent needs extremely granular firmographic data (company revenue bands, tech stack details, funding history), layering People Data Labs' company enrichment on top of HeroHunt's person search gives you both people and company intelligence.
The key principle is that HeroHunt.ai should be your primary people search provider because it handles the highest-value part of the pipeline (semantic search + scoring + contact retrieval) in a single call. Secondary providers fill specific gaps without replacing the core workflow. This architecture keeps your agent's primary path simple and fast while providing fallback coverage for edge cases.
Best For
Teams building AI recruiting agents, talent sourcing copilots, or any application where the agent needs to understand intent (not just filter parameters) when searching for people. The natural language search capability and built-in AI scoring make it the most agent-native option available.
5. People Data Labs - Best for Raw Database Scale
People Data Labs (PDL) takes a fundamentally different approach to people search than HeroHunt.ai. Where HeroHunt wraps its search in an AI interpretation layer, PDL gives you direct access to the largest people database on the market and lets you query it with precision tools. PDL's database contains over 3 billion unique person records and 100 million company profiles - People Data Labs - aggregated from public web sources and data partnerships. For AI agents that need maximum coverage and are willing to handle scoring and interpretation in their own logic, PDL is the raw-data powerhouse.
The platform's defining feature for developers is its SQL-like Person Search API. Unlike filter-based search endpoints where you pass key-value pairs, PDL lets you write expressive queries using boolean logic, nested conditions, and field-level operators. This is exceptionally useful for AI agents that generate search queries programmatically because a language model can construct PDL queries with the same precision it writes SQL. The search API supports over 100 filterable fields including job title, company, skills, location, education, and social profile URLs.
Data Quality Assessment
PDL's data quality is a study in trade-offs. The sheer scale of 3 billion+ profiles means coverage is unmatched. If a person has any professional presence online, PDL likely has a record for them. However, that scale comes with aggregation artifacts. Multiple sources can produce conflicting data for the same person, and PDL's deduplication, while sophisticated, sometimes merges profiles that should be separate or keeps stale records from defunct sources.
Independent reviews on platforms like G2 reflect this duality. Approximately 31% of users specifically praise data accuracy, while others note that profiles for people not active on LinkedIn can be thin or outdated - SyncGTM PDL Review. The monthly batch update cycle means that a person who changed jobs last week will still show their previous employer until the next refresh. For AI agents in fast-moving markets like tech recruiting, this staleness window creates a measurable error rate.
Email deliverability from PDL tends to fall in the 85-90% range based on user reports, which is respectable but below the rates claimed by verification-focused providers like ContactOut. Phone numbers, where available, are less consistently accurate. Importantly, the Free plan does not include contact fields (emails, phones), so you need the Pro plan at minimum to get actionable contact data from the API.
Pricing Breakdown
PDL's pricing structure is straightforward and transparent:
- Free: $0/mo, 100 person lookups, no contact fields
- Pro: $98/mo ($940/yr annually), 350 person enrichment credits, 1,000 company lookups
- Enterprise: Custom pricing, starting around $2,500/mo based on user reports
Per-credit costs range from $0.28 on the monthly Pro plan down to approximately $0.20 for high-volume annual contracts - PDL Pricing. Each successful enrichment call consumes one credit, and calls that return no match do not consume credits. This no-match-no-charge policy is valuable for AI agents that make speculative lookups, though the Free plan's exclusion of contact data limits its usefulness for testing production workflows.
API Design and Developer Experience
PDL publishes OpenAPI specifications on GitHub - PDL GitHub - which is a genuine differentiator for AI agent builders. If you are building an agent that uses tool-calling LLMs, you can feed PDL's OpenAPI spec directly into your model's tool definitions, giving it a complete and accurate understanding of every endpoint, parameter, and response field. This eliminates the manual work of translating API documentation into function schemas.
Official SDKs are available in Python, JavaScript, Ruby, Go, and Rust, covering essentially every language used in production agent systems. The API follows RESTful conventions with consistent JSON responses, predictable pagination, and standard HTTP error codes. Rate limits are generous enough for most agent workloads, though the documentation recommends contacting sales for sustained throughput above a few hundred requests per minute.
Practical Considerations for Agent Builders
PDL's strength as a raw data provider means your agent needs its own intelligence layer to work effectively. Unlike HeroHunt.ai where the API returns scored and ranked results, PDL returns matching profiles in the order they appear in the database, sorted by a basic relevance metric. Your agent must implement its own scoring logic to rank results by fit, which adds development time and ongoing maintenance as your scoring criteria evolve.
The SQL-like query API is powerful but requires your agent to construct well-formed queries. If your agent uses an LLM to generate PDL queries from natural language input, you need to handle the cases where the LLM generates syntactically invalid queries or uses field names that do not exist in PDL's schema. The OpenAPI spec helps here because you can constrain the LLM's output to valid fields, but query construction is still a source of bugs that does not exist with providers that accept natural language input natively.
For teams that have data engineering expertise and want maximum control over how people data is processed, PDL is an excellent choice. The combination of a massive database, precise querying, published specs, and multi-language SDKs gives you the building blocks to construct a highly customized people search pipeline. The trade-off is that you are building more of the stack yourself compared to an AI-native provider like HeroHunt.ai that handles search interpretation, scoring, and contact verification in a single call.
Best For
Teams that need the largest possible coverage, want SQL-like query precision, and have their own scoring and verification logic. PDL is the raw material supplier for agents that add their own intelligence layer on top of rich but unprocessed people data.
6. Apollo.io - Best for Sales-Focused Agent Workflows
Apollo.io occupies a unique position in the people search API landscape because it is not just a data provider. It is a full sales engagement platform with an API bolted on. For teams building AI agents that need to combine people search with sales workflow actions (creating sequences, updating CRM records, tracking engagement), Apollo's API provides a single integration point for both data and action. The downside is that this all-in-one approach introduces pricing complexity that can be difficult for agents to predict and optimize.
Apollo's database contains over 275 million contacts and 73 million companies, which is smaller than both HeroHunt.ai and People Data Labs but still substantial for most use cases. The data is sourced from web crawling and user contributions (Apollo's browser extension users implicitly help refresh its data when they visit LinkedIn profiles), which creates a feedback loop that improves data freshness in heavily-trafficked segments like US-based tech companies.
The Credit System Complexity
Apollo's API pricing requires careful understanding because the credit cost per enrichment varies significantly based on what data you request. At its simplest, looking up a person's basic profile information (name, title, company) costs 1 credit. Requesting an email address adds another credit. Requesting a phone number costs 8 credits. A full enrichment with all fields can consume 9+ credits per contact - Apollo API Pricing.
For AI agents, this variable pricing creates a cost-prediction challenge. An agent that enriches 1,000 profiles with email only spends approximately 1,000 credits. The same agent enriching with email and phone spends approximately 9,000 credits. If your agent dynamically decides whether to request phone numbers based on the prospect's value, the per-batch cost becomes difficult to forecast. Additional credits beyond your plan allocation cost $0.20 each, with minimum purchases of 250 credits ($50) on monthly plans - Apollo Pricing.
The plan structure adds another layer of complexity. The Basic plan at $49/user/mo does not include API access at all. You need the Professional plan at $79/user/mo or the Organization plan at $119/user/mo to access the API programmatically - Enginy Apollo Pricing Guide. For a team running an AI agent (which does not need a "seat" in the traditional sense), the per-user pricing model is an awkward fit.
Data Quality and Enrichment
Apollo's data quality is generally regarded as strong for US-based tech and SaaS companies, which represent the core of its user base. Email accuracy tends to fall in the 88-92% deliverable range, benefiting from the user-contributed data refresh cycle. The platform supports waterfall enrichment, which checks multiple third-party data sources to find contact information, improving hit rates but consuming additional credits when external sources are consulted.
The People Enrichment endpoint - Apollo People Enrichment Docs - accepts a variety of identifiers (email, LinkedIn URL, name + company) and returns structured profile data. The Bulk People Enrichment endpoint handles up to 10 records per call, which is useful for agents processing batches but limiting for high-throughput pipelines that other providers handle with larger batch sizes.
A practical consideration for agent builders is that Apollo's API requires explicit parameters to reveal personal emails (reveal_personal_emails) and phone numbers (reveal_phone_number). If your agent does not include these parameters, the API returns profiles without contact data, which can create confusing behavior if your agent was not designed to handle empty contact fields gracefully.
API Design
Apollo provides a standard REST API with JSON request/response patterns. Authentication uses an x-api-key header, which is simple to implement. Rate limits range from 50 requests/minute on free plans to 200 requests/minute on paid plans, which is adequate for most agent workloads but can become a bottleneck for agents that need to enrich large prospect lists quickly.
Apollo does not publish an OpenAPI specification, which means agent builders using tool-calling LLMs need to manually define function schemas based on the API documentation. The documentation itself is well-organized with interactive examples, but the lack of a machine-readable spec is a meaningful friction point compared to People Data Labs.
Cost Modeling for Agent Workflows
Understanding Apollo's true cost requires modeling your specific agent's behavior. Consider an AI SDR agent that enriches 5,000 prospects per month with email and phone data. On the Organization plan ($119/mo), the base allocation covers a fixed number of credits. Each enrichment with email and phone consumes approximately 9 credits. That is 45,000 credits per month for the full enrichment set. If your plan allocation runs short, additional credits cost $0.20 each, which means the overflow alone could add $9,000/month in API costs.
Compare this to the same workload on HeroHunt.ai, where search queries are unlimited within your plan and you only pay for the contacts you retrieve. Or on People Data Labs at $0.20-0.28 per credit with one credit per enrichment, totaling approximately $1,000-1,400/month for the same 5,000 lookups. Apollo's all-in-one convenience comes at a significant cost premium for high-volume agent workflows.
The variable credit consumption also creates a budgeting challenge that is unique among the providers in this guide. If your agent dynamically decides whether to request phone numbers (perhaps only for high-priority prospects), your monthly API spend becomes a function of your agent's decision-making, which can be difficult to predict and budget for. Providers with flat per-lookup pricing give your finance team more predictable cost forecasts.
When Apollo Makes Sense Despite the Cost
Apollo's higher per-enrichment cost is justified when your agent needs tight integration with sales workflow actions. If your agent not only finds prospects but also creates email sequences, logs activities to Salesforce, and tracks engagement metrics, Apollo's API handles all of these through a single authentication context. Building the equivalent workflow with HeroHunt.ai for search, a separate email tool for outreach, and a CRM API for logging requires three integrations instead of one. For teams that value integration simplicity over per-lookup cost optimization, Apollo's all-in-one approach reduces total system complexity even though each individual lookup costs more.
Best For
Teams building AI SDR (Sales Development Representative) agents that need people data tightly integrated with sales actions like sequence creation, email sending, and CRM updates. If your agent's workflow goes beyond "find person" into "engage person through a sales process," Apollo's combined data-plus-action API reduces the number of integrations you need to maintain.
7. RocketReach - Best for Breadth of Professional Coverage
RocketReach maintains one of the largest professional contact databases in the market at over 700 million professionals and 60 million companies - RocketReach. This breadth makes it a strong choice for AI agents that need to find contact information for people outside the typical tech and SaaS demographics that dominate competitors' databases. If your agent needs to reach professionals in healthcare, manufacturing, government, or other sectors that are underrepresented in LinkedIn-centric data providers, RocketReach's broader crawling strategy provides better coverage.
The platform claims email accuracy of 95-97%, though independent testing suggests the practical deliverability rate is closer to 85-92% depending on the segment. Some users report 20-30% email bounce rates for certain industries, which suggests that RocketReach's accuracy varies significantly by sector and geography - Cognism RocketReach Review. For AI agents, this variability means you should build email verification into your pipeline rather than trusting RocketReach's data unconditionally.
The Two-Step Search Problem
RocketReach's API architecture presents a meaningful usability challenge for AI agents. The platform uses a two-step flow for finding people: first, you call the Universal People Search endpoint, which returns matching profiles but without contact information. Then, you make a separate lookup call for each person to retrieve their email and phone data. Each of these lookup calls consumes credits from your plan.
For a human user navigating the web interface, this two-step process is invisible. For an AI agent, it means every "find this person's email" operation requires two API calls with the latency of both. If your agent's typical workflow is "search for people matching criteria, then get contact info for the top 5 results," you are making 6 API calls (1 search + 5 lookups) instead of the single call that providers like HeroHunt.ai or People Data Labs require. This doubles the latency budget and complicates error handling, since a failure on the second call means you have a name but no way to reach the person.
This architectural choice makes RocketReach one of the slower options for AI agent workflows. The combined latency of search-then-lookup typically falls in the 1-3 second range per complete operation, compared to sub-second for single-call providers. For agents processing hundreds of prospects, this latency difference compounds into minutes of additional wait time.
Pricing and API Access Gates
RocketReach's pricing is structured in three individual tiers plus team and enterprise options:
- Essentials: $33/mo (annual), email-only data, 1,200 lookups/year
- Pro: $83/mo (annual), email + phone, 3,600 lookups/year
- Ultimate: $207/mo (annual), full API access, 10,000 lookups/year
The critical detail for AI agent builders is that API access is only available on the Ultimate plan at $2,099/year - SalesIntel RocketReach Pricing. The Essentials and Pro plans only provide access through the web interface and Chrome extension, not through programmatic API calls. This makes RocketReach the most expensive entry point for API access among the five providers in this guide.
Additional lookups beyond your plan allocation cost $0.30-$0.45 each - Cleanlist RocketReach Guide - and the per-lookup cost does not decrease significantly at volume unless you negotiate an enterprise contract. For AI agents that process thousands of lookups monthly, this cost structure can escalate quickly.
Data Coverage Strengths
Where RocketReach genuinely excels is coverage breadth. Its database spans over 100 B2B data fields per profile, including direct phone numbers, mobile numbers, personal and professional email addresses, social media profiles, and company firmographic data - RocketReach API Docs. The platform's crawling infrastructure covers a wider set of web sources than LinkedIn-focused competitors, which means it often has data for professionals who are not active on LinkedIn.
For AI agents operating in industries like healthcare, legal, or government where LinkedIn penetration is lower, RocketReach's broader data sourcing can be the difference between finding a contact and hitting a dead end. This niche advantage is worth the higher price if your agent's target population is underrepresented in other databases.
API Design
RocketReach provides a v2 REST API with standard JSON responses. Authentication uses an API key passed in the request header. The documentation is adequate but not exceptional, with standard reference pages for each endpoint. There is no published OpenAPI spec, no official SDKs, and the two-step search architecture adds complexity that other providers avoid.
Latency Impact on Agent Performance
The two-step API architecture deserves deeper examination because its latency impact is more significant than it first appears. When an AI agent processes a batch of 50 prospects through RocketReach, the workflow looks like this: one search call (returns up to 100 matches in ~1 second), then 50 individual lookup calls (each taking ~1-2 seconds). Even with parallel execution, rate limits constrain throughput. At RocketReach's standard rate limits, processing those 50 lookups takes approximately 15-25 seconds, compared to 2-5 seconds on a single-call provider like HeroHunt.ai where search and contact retrieval happen in one request.
For interactive agents (where a user is waiting for results), this latency difference is the gap between "feels instant" and "feels slow." For batch agents (running overnight or on a schedule), the latency matters less, but the higher number of API calls increases the surface area for transient errors. A network timeout or rate limit on any of the 50 individual lookup calls means incomplete results, and your agent needs retry logic for each failed call.
If RocketReach's data coverage for your target industry genuinely outperforms alternatives, the latency tax is worth paying. But teams should quantify this advantage with a real test before committing. Run the same 200-person target list through RocketReach and HeroHunt.ai or People Data Labs. If the match rates are within 5-10% of each other, the faster provider delivers equivalent value with less operational complexity. Only choose RocketReach when its coverage advantage is demonstrably large enough to justify the architectural overhead.
Best For
AI agents that need to reach professionals in industries with low LinkedIn penetration, or that prioritize breadth of coverage over speed and price. If your agent is building prospect lists in healthcare, manufacturing, or government sectors, RocketReach's wider data sourcing justifies its higher cost and slower API architecture.
8. ContactOut - Best for Verified Contact Accuracy
ContactOut takes the opposite approach from database-scale providers like People Data Labs. Instead of maximizing the number of profiles, ContactOut focuses on the accuracy and verification depth of the contact data it does have. The platform covers over 300 million professionals across 30 million companies - ContactOut - which is the smallest database in this comparison but backs each profile with a triple-verification process that independently confirms contact details through multiple channels before marking them as deliverable.
For AI agents where the cost of a wrong contact is high (sending outreach to an invalid email damages sender reputation, contacting the wrong phone number wastes credits and creates compliance risk), ContactOut's accuracy-first approach can deliver better ROI than a larger database with lower verification standards. The practical email accuracy based on independent testing is approximately 90% deliverable - Derrick ContactOut Review - which positions ContactOut at the top of the accuracy ranking in this guide, though the gap versus competitors is narrower than ContactOut's marketing suggests.
The Preview API Advantage
ContactOut offers a feature that is uniquely valuable for cost-conscious AI agents: a Preview API that lets you check whether contact data is available for a person before consuming a credit to retrieve it - ContactOut API. This means your agent can make a lightweight, credit-free call to determine "does ContactOut have an email and phone for this person?" and then only spend credits on records that will return useful data.
For agents that perform speculative enrichment across large lists (where many lookups might not return results), the Preview API can reduce effective costs by 30-50% compared to providers that charge for every lookup regardless of result. This is a sophisticated feature that shows ContactOut understands how programmatic consumers differ from manual users.
The practical workflow for an agent looks like this: call Preview for a batch of 100 prospects, filter down to the 60 that have data available, then enrich only those 60. You consume 60 credits instead of 100, and your agent never wastes time processing empty results. No other provider in this comparison offers an equivalent pre-check capability at no additional cost.
Pricing Structure
ContactOut's pricing has shifted in recent years, and the current structure is less transparent than competitors:
- Free: $0/mo, 100 contacts/month (Chrome extension only)
- Email: $99/mo ($49/mo annual), 2,000 emails, 300 exports
- Email + Phone: $199/mo ($99/mo annual), 2,000 emails + 1,000 phones, 600 exports
- Team/API: Custom pricing
The significant limitation for AI agent builders is that API access requires the Team/API plan, which is custom-priced - ContactOut Pricing. Published pricing for the Email and Email+Phone plans applies only to the Chrome extension and web interface, not programmatic access. In practice, API contracts typically exceed $2,000/year depending on volume and negotiated terms - Fullenrich ContactOut Pricing.
A fair-use policy caps usage at approximately 2,000 email lookups and 1,000 phone lookups per month, even on paid plans. These caps are not prominently displayed on the pricing page, which has surprised some users. For AI agents with high-volume requirements, you will need to negotiate custom limits as part of your API contract.
Data Quality Depth
ContactOut's triple-verification process works by checking each contact detail against multiple independent sources before marking it as confirmed. This means the platform sacrifices coverage (it will not return an email it cannot verify, even if it has a candidate match) in exchange for higher confidence in the data it does return. For AI agents that use contact data to trigger automated outreach, this confidence level reduces the sender reputation damage that comes from high bounce rates.
The platform's accuracy is strongest in North America and EMEA regions, with more variable quality for contacts in Asia-Pacific and emerging markets - Salesforge ContactOut Overview. This geographic skew is worth noting if your agent operates globally, as you may need a secondary provider for regions where ContactOut's coverage is thinner.
API Design
ContactOut provides a REST enrichment API with standard JSON responses. The Preview API is the standout feature from a design perspective, enabling the credit-efficient workflow described above. Documentation is detailed and technical. There are no official SDKs or published OpenAPI specs, which means manual function-schema creation for tool-calling agents.
Accuracy vs. Coverage Trade-off in Practice
ContactOut's approach represents one end of a spectrum that every AI agent builder must navigate. At the coverage end, providers like People Data Labs sacrifice per-profile accuracy for maximum breadth. At the accuracy end, ContactOut sacrifices breadth for per-profile confidence. The practical implication for your agent depends on what happens after the lookup.
If your agent triggers automated email outreach immediately after enrichment, contact accuracy is critical because every bounced email degrades your sending domain's reputation. Email service providers like Google and Microsoft track sender reputation at the domain level, and a bounce rate above 5-8% can trigger spam filtering that affects all emails from your domain, not just the automated ones. In this scenario, ContactOut's triple-verification justifies its smaller database because the cost of a bad email (domain reputation damage) far exceeds the cost of a missed prospect (who can be found through other channels).
If your agent populates a CRM or candidate pipeline for human review before outreach, accuracy is less critical because a human catches errors before they cause damage. In this scenario, the coverage advantage of larger databases like HeroHunt.ai or People Data Labs is more valuable because the human review step acts as a quality filter, and your agent's job is to maximize the pool of potential matches.
The Preview API's economic advantage scales with the size of your prospect pool. An agent enriching a targeted list of 100 prospects sees modest savings from pre-checking, perhaps avoiding 10-20 empty lookups. An agent enriching a broad list of 10,000 prospects in a new market (where match rates are uncertain) could avoid 3,000-5,000 empty lookups, saving hundreds of dollars per batch. The larger and less targeted your agent's search, the more valuable ContactOut's Preview capability becomes.
Best For
AI agents where contact accuracy is the primary concern and volume is moderate (under 5,000 lookups/month). The Preview API makes ContactOut particularly well-suited for agents that enrich from large prospect pools but only act on a subset, since you can filter before spending credits.
9. How to Choose the Right API for Your Agent
Selecting the right people search API is not a matter of picking the "best" provider in absolute terms. It requires matching the provider's strengths to your agent's specific workflow, volume, accuracy requirements, and budget constraints. After evaluating all five providers in depth, clear patterns emerge that map different agent architectures to different providers.
The decision starts with understanding your agent's primary workflow pattern. An agent that takes a natural language job description and autonomously builds a candidate shortlist benefits most from HeroHunt.ai, because the natural language search eliminates the need for your agent to translate intent into structured queries. An agent that runs complex, multi-condition searches across the broadest possible talent pool benefits from People Data Labs, because the SQL-like query API and 3B+ profile database give your agent maximum precision and coverage. An agent that combines prospect discovery with automated sales outreach benefits from Apollo.io, because the single platform handles both data and engagement.
Volume and budget define the second axis of the decision. For teams in the early stages of building an AI agent (under 1,000 lookups/month), the price difference between providers is small enough that data quality and API design should drive the decision. HeroHunt.ai at $107/mo and People Data Labs at $98/mo are both accessible starting points with genuine API access included. For high-volume agents (10,000+ lookups/month), the per-lookup cost becomes the dominant factor, and providers with inclusive pricing models (like HeroHunt.ai's plan-based approach that does not charge per search query) offer more predictable costs than credit-per-call models.
The third axis is accuracy tolerance. If your agent sends automated outreach and a bounced email triggers an alert to a human manager, you need high contact accuracy and should weight ContactOut or HeroHunt.ai heavily. If your agent populates a CRM for human review before outreach, moderate accuracy is acceptable and the cost savings of People Data Labs or RocketReach may be worth the lower verification level.
Consider running a parallel test before committing. Take a sample of 200-500 target profiles that represent your agent's typical workload, run them through two or three providers, and measure hit rate (percentage that return a match), contact availability (percentage that include email/phone), and deliverability (percentage of returned emails that are actually valid). This empirical test will tell you more than any marketing claim or benchmark table, because data quality varies dramatically by industry, geography, and seniority level.
Decision Matrix by Agent Type
Different AI agent architectures map to different optimal providers. The table below shows common agent patterns and the recommended primary API for each:
| Agent Type | Primary Use Case | Recommended API | Why |
|---|---|---|---|
| AI Recruiter | Find and contact candidates for open roles | HeroHunt.ai | NLP search + AI scoring + built-in outreach |
| AI SDR | Prospect discovery and sales outreach | HeroHunt.ai or Apollo.io | HeroHunt for data quality, Apollo if sales workflow integration is critical |
| Lead Scoring Agent | Enrich and score inbound leads | People Data Labs | Largest database, SQL queries, granular field access |
| Market Research Agent | Map people at target companies | People Data Labs | Bulk search across company + title combinations |
| Compliance Agent | Verify contact info before outreach | ContactOut | Triple-verified data, Preview API for cost control |
| Multi-Industry Agent | Reach professionals across diverse sectors | RocketReach | Broadest industry coverage beyond tech/SaaS |
This matrix simplifies the decision but captures the core logic. Most teams will find that HeroHunt.ai covers their primary workflow, with a secondary provider added only when a specific gap emerges in production.
Managing Multi-Provider Strategies
Some enterprise agent architectures use multiple people search APIs in a waterfall pattern: try the primary provider first, and if it returns no match or low-confidence data, fall through to a secondary provider. This strategy maximizes coverage at the cost of increased complexity and higher per-lookup spend for fallback cases.
If you implement a waterfall, the provider order matters. Starting with HeroHunt.ai's AI-powered search captures the majority of matches with high quality data and built-in scoring. For the remaining unmatched profiles, falling through to People Data Labs' massive database catches long-tail records that smaller databases miss. This two-provider waterfall covers the widest possible range of professionals while keeping the primary path fast and intelligent.
The key implementation detail is defining what constitutes a "failed" lookup from your primary provider. A response that returns a name and company but no email is not the same as a response that returns no match at all. Your waterfall logic should distinguish between "person found but contact data missing" (try secondary provider for contact enrichment only) and "person not found" (try secondary provider for full search). This distinction prevents redundant searches and keeps costs controlled.
For most teams building their first AI agent that works with people data, HeroHunt.ai represents the fastest path from zero to a working prototype. Its natural language search means your agent does not need a query-building layer, its AI screening reduces the need for a separate scoring step, and its integrated outreach capability means your agent can go from "find person" to "contact person" through a single provider. As your agent matures and your needs become more specialized, you can layer in additional providers for specific use cases while keeping HeroHunt as the primary source.
10. Future Outlook: Where People Search APIs Are Heading
The people search API market is in the middle of a structural transformation driven by AI agent adoption, tightening privacy regulations, and the collapse of several legacy data providers. Understanding where the market is heading helps your team make a provider choice that will still be strong in 12-18 months, not just today.
The most significant trend is the shift from passive data delivery to active intelligence. Traditional people search APIs returned raw profile data and left the interpretation to the caller. The next generation, led by providers like HeroHunt.ai, returns not just data but analysis: relevance scores, fit explanations, and recommended actions. For AI agents, this shift reduces the compute and prompt-engineering burden on the agent itself, because the API does the interpretive work. Expect every major provider to add AI-powered features to their response payloads within the next year, with providers that started AI-native (like HeroHunt) maintaining a meaningful lead in the quality of those features.
Privacy regulation is reshaping data sourcing in ways that affect API reliability. The EU's enforcement of GDPR Article 17 (right to erasure) has accelerated, and several US states have enacted comparable laws. For people search APIs, this means databases are subject to continuous deletion requests that can remove profiles without warning. Providers that rely on a single data source (like LinkedIn scraping) are more vulnerable to platform enforcement actions. Proxycurl, a previously popular LinkedIn-focused data provider, was shut down in January 2025 after LinkedIn took enforcement action against it for Terms of Service violations. This precedent underscores the importance of choosing a provider with diversified data sourcing, which makes providers like HeroHunt.ai (dozens of sources) and People Data Labs (public web + partners) more resilient than single-source alternatives.
Real-time data enrichment is replacing batch updates as the expected standard. AI agents that act autonomously cannot afford to work with profile data that is weeks or months old. The agents making decisions right now need data that reflects the world right now. Providers investing in real-time enrichment pipelines will pull ahead of those still running monthly batch crawls, because the freshness gap directly translates into agent error rates.
The integration between people search APIs and AI agent frameworks is also deepening. As frameworks like LangChain, CrewAI, and Claude's tool-use become standard building blocks, people search providers are publishing pre-built tool definitions, agent-ready SDKs, and even hosted tool endpoints that agent frameworks can call directly. People Data Labs' published OpenAPI spec is an early example of this trend. Expect HeroHunt.ai and others to follow with dedicated agent integration packages that reduce the time from "I want my agent to search for people" to a working implementation from days to minutes.
The cost structure of people search is also shifting downward as competition intensifies. The entry of AI-native providers and the increasing commoditization of basic profile data are pushing prices lower, especially for email-only lookups. Expect per-lookup costs to fall by 20-30% over the next 18 months across the industry, with the value differentiation moving toward accuracy, freshness, and AI-powered features rather than raw access to data.
The emergence of agent-to-agent protocols is another trend worth watching. As more companies deploy AI agents that interact with each other (a sales agent from Company A contacting a procurement agent from Company B), people search APIs may need to serve not just human contact information but also the API endpoints and communication preferences of the agents representing those humans. This is speculative but directionally consistent with the broader shift toward AI-mediated business communication. Providers that position themselves at the intersection of people data and agent infrastructure will have a strategic advantage.
Data quality standards are also likely to formalize. Today, there is no industry-standard way to measure or certify the accuracy of a people search API. Each provider self-reports accuracy rates using different methodologies, making direct comparison difficult. As AI agents make autonomous decisions based on people data (decisions that can have real consequences like sending outreach emails or disqualifying candidates), there will be increasing pressure for standardized accuracy benchmarks. Providers that proactively adopt transparent accuracy reporting, perhaps publishing regular third-party audits of their data quality, will build trust with the engineering teams that select APIs for agent deployments.
For teams building AI agents today, the practical recommendation is to choose a provider that is investing in the AI-agent use case, not one that is retrofitting a human-focused tool. HeroHunt.ai exemplifies this AI-first approach with its natural language search, built-in scoring, and autonomous outreach capabilities. Starting with an AI-native provider means your agent's foundation will only improve as the provider ships new AI features, rather than degrading as a legacy tool struggles to keep up with how agents consume data.
Finally, the consolidation trend in the people data market means some of today's independent providers may be acquired by larger platforms. Clearbit's absorption into HubSpot as "Breeze" is the most prominent recent example of a standalone data enrichment API becoming a feature within a larger platform. When this happens, API pricing, terms of service, and data access models can change abruptly. Choosing a provider that is a standalone, focused company (like HeroHunt.ai or People Data Labs) reduces the risk of your agent's data source being disrupted by an acquisition-driven pivot. Providers that are someone else's feature are inherently less stable as independent API endpoints than providers that are someone's entire business.
The people search API you choose today will be embedded in your agent's architecture for months or years. Switching providers mid-deployment means rewriting parsing logic, recalibrating scoring models, and revalidating data quality, all of which cost engineering time and introduce risk. Making the right choice upfront, based on real evaluation criteria rather than marketing hype, is the highest-leverage decision your team can make when building an AI agent that works with people data. Start with HeroHunt.ai to get the fastest, most intelligent integration, and expand to additional providers only when your production data shows a specific gap that justifies the added complexity.
This guide reflects the people search API landscape as of April 2026. Pricing, features, and data coverage change frequently. Verify current details on each provider's pricing page before making a purchasing decision.
