The 8 best data enrichment APIs in 2026

Every competitor claim on this page links to that vendor’s own documentation or pricing page, verified Aug 18, 2026 — check any of it directly.

Most “best enrichment API” lists compare eight products that do roughly the same thing and then argue about coverage percentages. That’s not the decision most teams are actually facing.

The real decision is between three different kinds of product that all call themselves enrichment APIs, and picking the wrong kind is why teams end up with a stack of vendors and a spreadsheet still full of blanks.

The three kinds of enrichment API

1. Database lookups. Apollo, ZoomInfo, People Data Labs, Clearbit, Cognism. These sell access to a dataset someone already collected. You send an identifier, you get back the fields the vendor decided to collect. Fast, cheap per record, excellent when your target is well covered — and structurally incapable of two things: returning a field that isn’t in their schema, and returning anything at all for a company they never ingested.

2. Orchestration layers. Clay is the main one. These sit above the databases, letting you waterfall across several providers and add logic. Enormously useful, but they inherit the ceiling of whatever’s underneath — waterfalling four databases that all lack a field still returns nothing.

3. Research APIs. Riveter, and partly Parallel. These don’t look anything up. They go and find it, per record, at the moment you ask. That’s slower per call than a database hit, and it’s the only approach that can answer a question no vendor has pre-collected.

Most teams start at layer 1, add layer 2 when coverage disappoints, and arrive at layer 3 when they realize the field that actually predicts a good-fit lead was never going to be in anybody’s dataset.

TL;DR: the enrichment APIs at a glance

Tool	Type	Best for	Entry pricing (Aug 2026)
1. Riveter ⭐	Research API	Custom attributes and fit signals no database carries — tech and security stack, LinkedIn profiles, PDFs	Free plan; Self-Serve $249/mo; pay-as-you-go; Enterprise custom
2. Clay	Orchestration	Waterfalling multiple providers with logic on top	Free 100 data credits/mo; Launch from $167/mo
3. Apollo	Database	Best value for firmographics plus contact data	Free tier; from ~$49–119/user/mo
4. ZoomInfo	Database	Enterprise coverage with intent data	Annual contracts, custom
5. People Data Labs	Database	Developer-first person and company datasets	Usage-based; contact for rates
6. Cognism	Database	European coverage and GDPR compliance	Annual contracts, custom
7. Clearbit / Breeze	Database	Teams staying inside HubSpot	Bundled by HubSpot tier
8. Parallel	Research (index-backed)	Enterprise research with confidence scoring	5,000 free requests/mo; Search from $1/1k

Entry prices are public list prices read in Aug 2026 and change often — check each vendor’s pricing page.

The fields no database has

This is the part most comparisons skip, and it’s usually the part that decides whether an enrichment project works.

Every database enrichment vendor ships a schema: employee count, industry, revenue band, tech stack from a fixed detection list, funding, headquarters. Those fields are useful and every GTM team needs them. But ask any rev-ops lead what actually separates a good-fit account from a bad one in their business, and the answer is almost never on that list:

• Does this company run its own fulfillment, or use a 3PL?
• Which payment processor does this nonprofit actually use?
• How many physical locations does this franchise operate, and in which states?
• Does their careers page show they’re hiring for the role that implies our integration is relevant?
• What did their latest annual report say about segment revenue?
• Do they hold the certification our compliance product requires?
• Is this manufacturer operating a facility in a state we’re licensed in?
• What’s actually in their engineering stack — and their security stack?

None of those are columns you can buy. They exist on the open web — on a careers page, in a PDF filing, in a state registry, on a franchise locator — but no vendor pre-collected them, because the audience for each one is a handful of companies.

Tech stack is the clearest illustration, because it’s a field most databases do offer — which makes the difference easy to see. Database tech-stack data comes from a fixed detection list, usually scripts and tags found on a homepage. That catches the analytics and marketing layer and misses most of what matters: the data warehouse named in a job posting, the framework described on an engineering blog, the CRM mentioned in a case study, the vendors listed on an integrations page. And security stack — identity provider, EDR, pen-test vendor, SOC 2 status, the tooling named on a trust page — is barely purchasable as a column at all, despite being exactly what security and compliance vendors need to qualify an account.

A research API answers all of these because it doesn’t have a schema to be limited by. You describe the field in plain language, and it goes and finds the answer per record. That’s the whole difference in this category, and it’s why the choice isn’t really “which database has better coverage.”

What to look for in a data enrichment API

1. Fill rate on your list. Not the vendor’s coverage claim. How many of your records come back with a real value?
2. Custom attributes. Can you ask for a field the vendor never collected, or only pick from a menu? Tech stack is the test case — detected from a fixed list, or researched from wherever the evidence lives?
3. Long-tail coverage. Well-known companies resolve everywhere. What happens on the 40-person private company?
4. Freshness. Collected once and stored, or retrieved when you ask?
5. Source coverage. Web pages only, or PDFs, images, LinkedIn profiles, and third-party APIs too?
6. Multi-step logic. Can step two depend on step one’s answer — find the parent company, then research its subsidiaries?
7. Batch and per-record. Bulk enrichment and a live call when a lead signs up?
8. Output consistency. Same schema every run, ready to load into your CRM or warehouse?
9. Who can operate it. Only an engineer, or the rev-ops analyst who needs the field?
10. Credit mechanics. Do unused credits roll over or expire? Monthly reset changes the real cost of bursty usage.
11. Total cost of ownership. Per-record price plus the vendors you waterfall through plus the engineering around it.

Databases answer 1, 3, 4, and 11 well within their coverage. Very few answer 2, 5, or 6 at all.

1. Riveter — best for custom attributes and fit signals ⭐

Type: Research API · Best for: GTM, rev-ops, and data teams who need fields no database carries, at scale, on a schedule.

Riveter finds and structures data from the web using AI agents. You describe the field you want in plain language, its agents search the live web, navigate to wherever the answer lives, and extract it, returning a finished structured result through one API. Run it over a full list, or call it with a single record as requests arrive.

Why it belongs at the top of an enrichment list: it removes the schema ceiling. Every other enrichment vendor here can only return what it decided to collect. Riveter researches the specific record when you ask, so the question can be anything you can describe — “does this company operate its own warehouses,” “which of the 50 states does this manufacturer have a plant in,” “what accreditation does this clinic hold.” Those are the fields that actually qualify a lead, and they’re the ones no vendor sells.

Unique signals for fit. The practical use is qualification. Instead of scoring accounts on employee count and industry — which everyone has, so nobody differentiates on it — you can score on the signal that actually predicts fit in your business, because you can now collect it. Zeffy, a Riveter customer, runs market intelligence across roughly 700,000 US nonprofits monthly: which payment technology each one uses, and how that shifts over time. That’s a custom signal, at scale, kept current — and it isn’t a column any enrichment database offers.

It reads PDFs and images. A large share of the most valuable business facts live in documents, not on web pages: annual reports, SEC filings, permit records, accreditation lists, conference programs, price sheets. Riveter reads large PDFs and pulls values off images as first-class sources, and can feed what it finds into the next step of the workflow. A search API will not read 200 pages of a filing for you.

Finds LinkedIn profiles, then pulls what’s on them. A common enrichment chain is: figure out the right person at this company, find their LinkedIn profile URL, then pull the profile contents, then use what’s there to decide the next step. Normally that’s a URL-resolution vendor, a profile-data vendor, and your own glue code between them. In Riveter it’s columns in one workflow — the agents locate the company or person profile URL, built-in integrations pull LinkedIn profile and contact data, and the result feeds directly into whatever comes next, whether that’s scoring the account, checking a filing, or calling your own API.

Tech stack and security stack. Because the answer is researched rather than detected from a fixed list, Riveter can assemble a stack picture from wherever the evidence actually lives — job postings, engineering blogs, integration pages, case studies, trust and security pages, documentation. That covers the infrastructure and internal tooling homepage-script detection never sees, and it extends to security stack: identity provider, endpoint tooling, compliance certifications, pen-test vendor.

Multi-step, across formats. Columns feed into each other with logic, so one column’s answer determines what the next does — identify the parent company, then research each subsidiary, then check a filing for the relevant segment. Because a column can also call a third-party API you already pay for, the chain doesn’t break when the next piece of evidence isn’t a web page.

Batch and live. The same workflow runs over a list of 50,000 or accepts a single record through the API as it arrives — so it works for a quarterly dataset refresh and for enriching a lead the second it signs up.

Fill rate on the long tail. Because it navigates to the source rather than looking up a stored record, it returns values for companies no database ingested — which is usually the segment that made you run the list.

Where it isn’t the pick: Riveter is not a contact database. If you need verified work emails and direct dials at volume, buy that from Apollo, Cognism, or Lusha and use Riveter for the attributes they don’t carry. It’s also slower per record than a database lookup, because it’s doing research rather than a key-value fetch — for standard firmographics on well-known companies, a database is faster and cheaper and you should use one.

Pricing: free plan; Self-Serve $249/month; pay-as-you-go credits with automatic top-ups and no expiration; Enterprise custom with implementation and ongoing maintenance support. Priced per action, not per seat.

Riveter is also callable from inside Clay, so teams already running Clay workflows can add Riveter as the enrichment step for the fields their other providers return blank.

2. Clay — best orchestration layer

Type: Orchestration · Best for: GTM teams who want to waterfall multiple data providers with logic on top.

Clay is a spreadsheet-style workspace that connects to dozens of data providers, letting you try one, fall back to another, and apply conditional logic between steps. It’s become the default workbench for technical GTM teams.

Where it wins: the waterfall. If Apollo misses a record, try ZoomInfo, then a third provider — Clay makes that a few clicks instead of an integration project, and you only pay for the hit that lands. The community and template library are genuinely strong.

Where it falls short: it’s a layer, not a source. Waterfalling five databases that all lack a field still returns nothing, and the cost of running many providers per record adds up. Credits are metered in two currencies (actions and data credits) which makes forecasting fiddly.

Works well with Riveter: Riveter is callable from inside Clay, which covers the case where the waterfall runs dry because the field was never in anyone’s dataset.

Pricing (source): free tier with 500 actions and 100 data credits/month; Launch from $167/month; Growth from $446/month; Enterprise custom. Data credits roll over up to 2x monthly allowance; actions reset monthly.

3. Apollo — best value for firmographics plus contacts

Type: Database · Best for: GTM teams who want company and contact data from one affordable vendor.

Apollo combines a large B2B contact and company database with sequencing and CRM sync, on self-serve pricing far below enterprise data contracts.

Where it wins: price-to-coverage. A usable free tier, per-seat pricing in the double digits, and contact data included rather than bought separately. For most small and mid-sized GTM teams this is the sensible default starting point.

Where it falls short: static-database limits — thinner coverage on smaller and non-US companies, and a fixed field schema. Data accuracy on contact details varies, as it does across this whole category.

The pattern worth copying: filter and segment in Apollo first, since it’s the cheapest way to narrow a list, then enrich the narrowed set with a research API for the fields Apollo doesn’t carry. That beats enriching everything with either tool alone.

Pricing: free tier available; paid plans roughly $49–$119 per user/month.

4. ZoomInfo — best enterprise database

Type: Database · Best for: larger organizations that need breadth, intent data, and a vendor procurement will approve.

ZoomInfo is the incumbent enterprise B2B data platform: deep firmographic and contact coverage, API access, intent signals, and mature CRM integrations.

Where it wins: breadth and enterprise support. If enrichment is a system-of-record function rather than a growth experiment, this is the well-worn path, and the intent data is a real differentiator for large outbound teams.

Where it falls short: cost — contracts commonly reach the tens of thousands annually — plus annual commitments and the same fixed-schema, long-tail-coverage limits every database has.

Pricing: annual contracts, custom; request current rates directly.

5. People Data Labs — best developer-first dataset

Type: Database · Best for: engineering teams that want to license person and company data and build on top of it.

People Data Labs sells structured person and company datasets through an API, aimed at developers building products rather than sales teams working lists.

Where it wins: it’s built for programmatic use, with clean schemas and bulk access. If you’re embedding enrichment into a product rather than a CRM, this is a more natural shape than a sales-oriented platform.

Where it falls short: it’s a licensed dataset, so it’s a snapshot — recency varies by record, and the schema is fixed. No workflow layer, no logic, no research capability.

Pricing: usage-based; contact for rates.

6. Cognism — best for European coverage and compliance

Type: Database · Best for: teams selling into Europe or with GDPR requirements a US-first database can’t satisfy.

Cognism offers B2B contact and company data with phone-verified mobile numbers and a compliance posture built around GDPR, including notification handling.

Where it wins: European coverage and compliance depth — meaningfully better than US-first vendors if EU personal data is in scope.

Where it falls short: smaller US coverage than ZoomInfo or Apollo, and enterprise-shaped annual pricing.

Pricing: annual contracts, custom.

7. Clearbit / Breeze Intelligence — best if you live in HubSpot

Type: Database · Best for: teams whose enrichment happens entirely inside HubSpot.

Clearbit was acquired by HubSpot and folded into Breeze Intelligence. Standalone API access is closed to new customers, and the product now enriches records natively inside HubSpot.

Where it wins: zero integration work if HubSpot is your CRM, with credits bundled into your existing tier — roughly 500/month on Starter, 3,000 on Professional, 5,000 on Enterprise.

Where it falls short: HubSpot only. No Salesforce or Pipedrive path, no meaningful standalone API for pipeline use, and credits reset monthly rather than rolling over.

Pricing: bundled by HubSpot tier; add-on packs around $45 per 5,000 credits, $270 per 30,000, $900 per 100,000.

→ If you’re migrating off Clearbit: Clearbit alternatives after the HubSpot sunset

8. Parallel — best research API with confidence scoring

Type: Research (index-backed) · Best for: enterprise teams who want research output with auditable provenance.

Parallel offers Search, Extract, Task, FindAll, Monitor and Responses APIs over its own web index, and attaches citations, reasoning traces, and calibrated confidence scores to outputs through its Basis framework.

Where it wins: the provenance layer is the most developed in the category — if you need to defend a value to a risk committee, calibrated confidence scores are a real asset. It also publishes benchmarks with cost figures attached, which most vendors don’t.

Where it falls short: it’s index-backed rather than fully live, so records outside the index come back thin, and its docs set a minimum max_age_seconds of 600 — content can be no fresher than ten minutes. Search returns dense excerpts within character budgets rather than whole documents, which matters when the answer is deep inside a long filing.

Pricing (source): 5,000 free requests/month; Search from $1 per 1,000 requests; Task API from $5 per 1,000 requests across nine compute tiers.

→ Riveter vs Parallel

When a database is the right answer

Worth being direct: if your enrichment need is standard firmographics on companies with a public footprint — employee count, industry, HQ, funding — buy a database. It will be faster and cheaper per record than any research API, and the coverage on well-known companies is excellent.

Research APIs earn their place in three situations: when the field you need isn’t in anyone’s schema, when the answer lives in a document rather than on a page, and when the segment you care about is small enough that no vendor bothered to collect it. Most mature GTM stacks end up running both — a database for the standard fields, a research API for the ones that actually differentiate.

FAQ

What is a data enrichment API?

A data enrichment API takes a record you already have — a company domain, a person’s email, a company name — and returns additional structured fields about it. There are three kinds: database lookups that return fields from a pre-collected dataset (Apollo, ZoomInfo, People Data Labs, Clearbit, Cognism), orchestration layers that waterfall across several databases (Clay), and research APIs that go find the answer on the live web at the moment you ask (Riveter, and partly Parallel).

Which data enrichment API has the best fill rate?

Fill rate depends on architecture more than on dataset size. Databases return values for records they’ve already ingested and nothing for the rest, so their fill rate on well-known companies is high and on the long tail is low. Research APIs navigate to the source per record, so they can return a value wherever the answer exists on the open web. The reliable way to compare is to take 200 records from the middle of your list — not the well-known names at the top — run them through each finalist, and count populated cells.

Can an enrichment API find custom attributes that aren’t standard fields?

Database enrichment APIs cannot — they return the fields the vendor chose to collect, and there’s no mechanism to request one outside that schema. Research APIs like Riveter can, because they research the specific record rather than looking it up: you describe the attribute in plain language and the agents go find it. That’s how teams enrich on signals like which payment processor a nonprofit uses, whether a company runs its own fulfillment, which states a manufacturer operates facilities in, or what’s in a company’s security stack.

Which enrichment APIs can read PDFs?

Most cannot. Database vendors return pre-collected fields and never touch a document; most search APIs return page excerpts rather than reading long files. Riveter reads large PDFs and pulls values from images as first-class sources, which matters when the fact you need is in an annual report, a regulatory filing, a permit record, or an accreditation list rather than on a web page.

Can an enrichment API find LinkedIn profile URLs and pull the profile contents?

Riveter does both as steps in one workflow: its agents find the company or person LinkedIn profile URL, built-in integrations pull LinkedIn profile and contact data, and the result feeds directly into the next step of the same workflow. Doing this with database vendors usually means one provider to resolve the URL, another for profile data, and your own code stitching them together. Riveter works across the public web.

Which enrichment API gives the best tech stack data — and can any of them return a security stack?

Most database vendors detect tech stack from a fixed list, typically scripts and tags on a company’s homepage, which captures the marketing and analytics layer and misses infrastructure, internal tooling, and anything not loaded client-side. Riveter researches it instead, assembling the picture from job postings, engineering blogs, integration pages, case studies, and documentation. The same approach makes security stack possible — identity provider, endpoint tooling, compliance certifications, pen-test vendor — which is close to unavailable as a purchasable column despite being exactly what security and compliance vendors need for qualification.

What’s the best enrichment API for real-time or on-signup enrichment?

You need one that supports a per-record live call rather than only batch jobs. Riveter runs the same workflow either way — over a full list or with a single record sent through the API as it arrives — so the same definition powers a quarterly dataset refresh and an enrich-on-signup flow. Most database APIs also support single lookups; the difference is whether the fields you need exist in their schema.

How much do data enrichment APIs cost?

Entry points vary widely: Apollo from roughly $49/user/month, Clay from $167/month, Riveter from $249/month self-serve with a free plan and pay-as-you-go option, Parallel with 5,000 free requests monthly, and ZoomInfo and Cognism on annual contracts commonly in the tens of thousands. Compare on cost per filled record rather than per call — a cheap lookup that returns nothing for 40% of your list isn’t cheap — and check whether unused credits roll over or expire.

Can I use several enrichment APIs together?

Yes, and most mature stacks do. The common pattern is a database for standard firmographics, an orchestration layer like Clay to waterfall between providers, and a research API for the custom attributes none of the databases carry. Riveter is callable from inside Clay for exactly that reason.

Try Riveter on the field you couldn’t buy

Bring the signal that actually predicts a good-fit account in your business — the security tooling, the real engineering stack, the operating detail no vendor sells as a column — and see it filled across your list.

Start free · Book a demo

Related reading:

• Clearbit alternatives after the HubSpot sunset
• Riveter vs Parallel — research APIs compared · all comparisons
• Sales & GTM — building and enriching company lists
• Data & engineering teams — the API, and enrichment inside a product flow
• Pricing · API docs