Data Enrichment API: What Actually Matters in 2026
Feature matrices run forty rows. Three questions decide the outcome: match method, refresh cadence, and what the pricing punishes.
A data enrichment API takes a partial identifier (an email, a phone number, a name plus an address) and returns the fuller record attached to it. That's the whole job. Everything else is implementation detail, and the differences that actually matter between providers come down to three questions: is matching deterministic or probabilistic, how often does the underlying data refresh, and does the pricing punish you for calling it? Exact Match resolves against 250M+ verified U.S. consumer profiles using deterministic matching, refreshes daily, and runs on one flat Unlimited plan with unlimited credits. The callable surface is small: entity_resolve, entity_enrich, entity_relations, entity_traits, and a bulk row endpoint for whole files.
Strip it back to three questions
Enrichment vendor comparison has gotten far more complicated than the underlying problem deserves. Feature matrices run forty rows. Almost none of those rows change an outcome.
Answer these three and you've done 90% of the evaluation.
1. Deterministic or probabilistic matching
Probabilistic matching says "these two records are statistically similar enough to be the same person." Deterministic matching says "these two records agree on identity anchors we can verify."
Exact Match is deterministic: every match is verified against multiple identity anchors (name, email, phone, address) rather than statistical guessing.
Why this matters more than it sounds: a wrong probabilistic match doesn't look wrong. It looks like a record. It has a name, a plausible address, a phone number in the right area code. It sails through every QA check you'd think to run, then quietly poisons a segment, an attribution model, and eventually somebody's quarterly number. You don't find out from the data. You find out from a confused reply.
The practical follow-up question for any vendor: what happens on a partial match? A provider that returns something for every row is telling you something about its threshold.
2. How often the data refreshes
Enrichment is a snapshot business. The value of a snapshot is a function of its age.
Exact Match's underlying data refreshes daily, which is the specific reason outreach doesn't pile up against records that went stale months ago. If a vendor won't state a cadence, treat that as the answer. And ask what "refresh" covers: the whole graph, or the subset that changed?
3. What the pricing punishes
This is the one people evaluate last and regret first. Sourced shapes, for contrast:
- Apollo.io is per-seat and credit-metered.
- Seamless.ai charges per search action, so cost tracks usage unpredictably.
- UpLead is per-credit, limited to B2B contact verification.
- Hunter.io is per-search and email-finder-only.
- People Data Labs is API-only with unpublished enterprise pricing and annual commitments.
- Clay is an orchestration layer: technical configuration plus variable multi-provider credit costs, with no native consumer behavioral data of its own.
Exact Match is one flat plan, quoted on a short consultation, with every product and feature included and unlimited credits (overage is structurally impossible). API and MCP access defaults to 30 requests per minute, raisable by custom agreement.
Metered pricing doesn't just cost money. It changes behavior. Teams on per-credit plans stop backfilling old records, stop re-enriching quarterly, and stop testing segments they aren't already confident about, all of which are the exact activities that make enrichment pay for itself. There's a fuller version of that math in data enrichment ROI.
The endpoints you'll actually call
The resolution and enrichment surface:
entity_resolve: turn a partial identifier into a resolved entityentity_enrich: expand a resolved entity into a full profileentity_relations: pull relationships between entitiesentity_traits: query traits, including cross-entity queries that join B2B professional or employer filters with B2C consumer behavioral data at the identity level rather than merging two result sets afterward
The segmentation surface:
trait_searchandtrait_getfor finding and reading traits across 80,000+ targeting clustersgroup_entities_by_traitfor bucketingcalculate_trait_liftfor scoring which traits actually differentiate a segment
For bulk work, resolve_and_enrich_rows processes an entire list in one call using signed upload and download URLs, so you're not looping a per-row endpoint 400,000 times against a 30-per-minute limit. There's also geocoding for address-to-geo resolution when you need radius targeting.
Output goes through async export jobs with CRM-specific column templates: list templates, get a template, fall back to a default, poll for status. On the operational side you can read balance, usage history with by-module breakdowns, subscription info, billing history and audit logs, which matters more than it sounds the first time finance asks which team burned the month.
If you're an agency or a reseller
Subaccounts are the feature to look at. They're self-service scoped child identities under one parent API key: each downstream client gets its own isolated fair-share rate limit and export concurrency, real data isolation between subaccounts, and no visibility into billing or credits.
The reason that's structurally different from "just use separate API keys" is the fair-share part. One client running a 2M-row backfill can't consume the rate budget the rest of your book is relying on that afternoon.
Driving the same API from Claude instead of a backend
The full data and billing surface is exposed as a native MCP server with Clerk OAuth 2.0 and API-key auth, so the same tools run inside Claude or any MCP client. There's a Claude Code plugin (exactmatch-data-scientist) for in-IDE audience research, and a Slack bot agent for conversational queries and exports.
A workflow worth trying: prototype the query conversationally until you know exactly which traits and thresholds you want, then write it once as real code. Iterating on segment definitions in a chat window is faster than iterating in a deploy cycle, and the query you end up shipping is usually the fifth one, not the first.
The integration checklist
Rate limit first: 30 requests per minute by default. Design around it, or ask for a raise before launch rather than during it. Use resolve_and_enrich_rows for anything bulk. Send every identity anchor you hold, not just the one your schema calls primary, deterministic matching rewards more anchors. Poll export status instead of sleeping on a fixed timer. And log which anchor produced each match, so that when a segment underperforms you can tell a data problem from a targeting problem.
One thing we can't hand you: a published match rate for enrichment specifically. Exact Match's public numbers cover Site ID's 25-40% identification range for verified human visitors, which measures a different thing entirely and isn't a substitute for an enrichment match rate. Ask for it against your own sample file, and ask what the denominator is.
Related reading: the consumer data API overview covers the query side, the Data Enrichment solution page covers what ships in the box, and if you only need one field back at a time, phone append covers the narrower single-field version of this same matching layer.
Frequently Asked Questions
What's the difference between a data enrichment API and an identity resolution API?
Resolution answers "who is this?": it turns a partial identifier into a single verified entity. Enrichment answers "what else do we know about them?", it expands that entity into a full profile. In practice you call them together, which is why entity_resolve and entity_enrich are separate tools and resolve_and_enrich_rows does both in one pass for bulk files. For the wider question of picking a vendor rather than just the API layer, see our b2c marketing data provider guide. If what you're resolving is a temporary in-market state rather than a static profile, see buyer intent signals api, and for how that state gets scored in real time rather than pulled as a batch, see real time purchase intent signals api. For how resolution and enrichment combine with visitor identification on one graph, see visitor id plus enrichment platform. To run the same resolve-then-enrich sequence from Claude, see claude data enrichment tool. For handing audience building itself to an agent, see ai agent audience data api. If you're comparing what each call costs across vendors, see cheapest consumer data api.
How many identifiers do I need to send for a match?
More is better, and the reason is mechanical rather than a matter of degree. Deterministic matching verifies against anchors (name, email, phone, address) so each additional anchor you supply is another thing that can confirm or rule out a candidate. A single weak anchor gives the matcher very little to verify against.
Can I use one account for multiple clients?
Yes, through subaccounts. Each scoped child identity sits under one parent API key with its own fair-share rate limit and export concurrency, genuine data isolation between subaccounts, and billing and credit information hidden from them. That's the intended setup for agencies and resellers rather than issuing separate top-level accounts.
Does the flat plan really have no overage?
The Unlimited plan includes every product, every feature and unlimited credits, so there's no credit balance to exceed. The constraint is throughput, not volume: API and MCP access defaults to 30 requests per minute, and that ceiling is raisable by custom agreement. Plan for the rate limit; you don't need to plan for a credit bill.
Get Started: Unlimited
One plan, everything included: every product, every feature, and unlimited credits. Schedule a consultation and we will build pricing around your needs.