A bulk data enrichment API completes or refreshes many records at once by matching them against a data provider and returning verified fields in batches, rather than one record per request. It is what turns a list of thousands of thin leads or a stale CRM export into clean, current data without a person touching each row, the foundation of CRM data quality.
This guide is bulk-specific and tool-oriented. It covers how bulk enrichment differs from single-record and search calls, what to evaluate at volume, and five APIs worth knowing, each strong for a different kind of team. For the wider concept of enrichment and how the data gets built, see our guide to the data enrichment API.
What a Bulk Data Enrichment API Does
A bulk data enrichment API takes a set of records you already hold, a list of companies, a CRM export, a batch of form fills, and returns enriched fields for all of them in one operation. You send an array of records or submit a job, and the API matches each one against its dataset and hands back verified company firmographics, contact details, technographics, or intent signals.
The reason it exists is throughput. Enriching records one API call at a time works for a handful, but it collapses at the scale go-to-market teams actually operate: tens of thousands of accounts, a full CRM refresh across Salesforce or HubSpot, a nightly sync. Bulk endpoints and asynchronous jobs are built for that volume, either by accepting many records per call or by letting you submit a job and collect the results when it finishes.
How a provider handles that volume is where the tools diverge, and it is the practical question this guide answers.
Bulk Enrichment Versus Single-Record Versus Search
Three call types get confused, and picking the wrong one wastes credits and time.
Single-record enrichment completes one record per request. It suits real-time moments, a form submission that needs enriching before routing, where latency matters more than throughput.
Bulk enrichment completes many records per request or per job. It suits backfills, list cleanup, and scheduled refreshes that fight data decay across a known set of records.
Search finds records you do not have yet, based on filters like industry, title, or location. It is a different operation entirely.
The distinction that trips teams up most is bulk versus search. Bulk enrichment works on records you already know and want to complete. Search discovers new records that match criteria. A common and efficient pattern uses both in sequence: search to find and rank candidates cheaply, since search is often free or low-cost, then enrich in bulk only the records worth keeping. Running a wide search and enriching everything it returns is how credits disappear.
What to Look For in a Bulk Enrichment API
At volume, a handful of specs decide whether an API is efficient or painful. These are the rows worth comparing before you commit.
Batch size per call. How many records one request accepts, from 10 at the low end to 100 or more. Larger batches mean fewer calls and simpler code.
Sync versus async. Synchronous APIs return results in the response and cap batch size to stay fast. Asynchronous bulk jobs accept huge volumes, run in the background, and notify you or let you poll when done, which is what you want for hundreds of thousands of records.
Credit model. Whether you pay per record attempted or only per record matched, and whether re-enriching a record you already pulled costs again. This determines the true cost of a large job.
Rate limits. Requests per second or per minute, and whether bulk calls are throttled below single-record calls. This sets how fast a job can actually run.
Match rate and freshness. How well the provider resolves your records to the right entity and its coverage on your specific records, and whether the data is verified and current or a stale snapshot, since poor data quality turns every unmatched or outdated record into a wasted credit.
The Best Bulk Data Enrichment APIs in 2026
Each of these wins a different use case. The right one depends on your data, your volume, and whether you want a pure API or a platform around it.
ZoomInfo: Best for verified data at scale
ZoomInfo is an all-in-one AI GTM platform. Its API enriches records against a maintained data foundation of 100M+ companies and 600M+ professionals, with verification built into how the data is sourced as a B2B data provider. For bulk work, the standard Enrich API handles up to 25 records per call, and dedicated Bulk endpoints run large jobs asynchronously, so a backfill of hundreds of thousands of CRM records submits as a job and returns without managing calls one at a time.
Key features: verified firmographics, contact and email data, phone numbers, org charts, technographics, and intent signals; search-then-enrich pattern; entitlement-aware responses; access via API, MCP server, and CLI.
Bulk specifics: up to 25 records per standard enrich call; dedicated asynchronous Bulk endpoints for large jobs, with Preview jobs (no credit charged) to identify records and Redeem jobs to retrieve them; one credit per new record within a 12-month Records Under Management window.
Best for: teams that need verified, governed data across a large database and care more about accuracy and coverage than raw price per record.
The Preview and Redeem split is what sets the cost model apart: because search and preview cost nothing, you see what a job would return before committing, then pay only for the records you redeem, once each within the 12-month window. On a job of hundreds of thousands of records, cost tracks the records you actually use rather than the size of the job you ran.
Apollo: Best for all-in-one outbound
Apollo pairs a large contact database with sequencing and CRM sync, so enrichment lives inside the same tool reps use to run outbound. Its bulk people enrichment endpoint handles up to 10 records per call, with bulk requests throttled to half the per-minute rate of single-record calls.
Key features: contact and company enrichment, built-in sequencing, CRM sync, and an optional provider waterfall; strong fit for teams already running outbound in Apollo.
Bulk specifics: up to 10 people per bulk call; bulk throttled to 50% of the single-record rate limit; credits vary by the data returned.
Best for: sales teams that want data and outbound execution in one platform rather than a standalone enrichment API.
We cover the endpoint surface in depth in our Apollo API review, and the head-to-head is in the Apollo vs ZoomInfo comparison.
Clay: Best for multi-provider coverage
Clay is not a data provider but a waterfall orchestration layer over 75+ sources, including ZoomInfo, Apollo, and People Data Labs, wrapped in a spreadsheet-style interface. You load a list, add enrichment columns, and Clay runs each record through providers in a waterfall until one returns a match, so you pay for coverage rather than a single vendor's blind spots.
Key features: waterfall enrichment across dozens of providers, spreadsheet UI, AI enrichment columns, CRM and sequence integrations, and signal-triggered workflows.
Bulk specifics: not a single per-call endpoint but a table-based orchestration model; pay per successful match; credits climb quickly with many columns or providers.
Best for: RevOps teams whose problem is coverage across varied data types, and who want to combine providers rather than commit to one.
The trade-off is cost and complexity at high volume: when the job is one data type across many records, a single dedicated API is usually cheaper than a multi-provider waterfall. The Clay vs ZoomInfo comparison covers where each fits.
People Data Labs: Best for developers and datasets
People Data Labs is a dataset-first provider built for developers who want to work with data programmatically rather than through a UI. Its bulk enrichment accepts up to 100 records per call, for both people and companies, which makes large backfills efficient for engineering teams comfortable in code.
Key features: large person and company datasets, flexible schema, developer-oriented documentation, and dataset licensing options beyond per-call enrichment.
Bulk specifics: up to 100 records per bulk call, for both the Bulk Person and Bulk Company Enrichment endpoints; billed per successful match, up to 100 credits per call; strong fit for programmatic, high-volume pipelines.
Best for: data teams and developers building enrichment into their own product or data warehouse.
We go deeper in our People Data Labs review, and break down the cost in the People Data Labs pricing guide.
Crustdata: Best for real-time freshness
Crustdata resolves and enriches records from 15+ sources indexed from the public web, with the option to pull data live at query time or from a refreshed database. Its company enrichment supports batch requests up to 25 records, and it charges credits only when data is returned.
Key features: real-time or database enrichment modes, 15+ sources resolved into a unified schema, and an MCP server for AI clients.
Bulk specifics: up to 25 records per company enrichment batch; credits charged only on returned data; real-time mode for the freshest results.
Best for: builders who want the freshest possible data pulled live from the web at query time.
Bulk Enrichment API Comparison
The table lines up the specs that matter at volume. Clay sits apart because it orchestrates other providers rather than enriching from its own dataset.
Provider | Batch size per call | Data source | Credit model | Best for |
ZoomInfo | 25, plus async Bulk API for large jobs | Verified, maintained database | Per new record, 12-month window | Verified data at scale |
Apollo | 10 people | Own contact database | Per record, varies by data | All-in-one outbound |
Clay | Orchestration layer, not a single endpoint | 75+ providers via waterfall | Per provider call in the waterfall | Multi-provider coverage |
People Data Labs | 100 records | Own dataset | Per successful match, up to 100 per call | Developers and datasets |
Crustdata | 25 companies | 15+ web sources, real-time or database | Only when data returned | Real-time freshness |
Getting Started
You can run a bulk enrichment job without building the full OAuth flow first.
Create an application in GTM.ai as a self-serve customer, or in the ZoomInfo Developer Portal as an enterprise customer. Generate a token with Test API Access, then run a search-then-enrich workflow on a small batch before pointing a job at your full list.
Frequently Asked Questions
What is a bulk data enrichment API?
A bulk data enrichment API completes or refreshes many records in one operation by matching them against a data provider and returning verified fields in batches. It is built for volume, enriching a whole list or database rather than one record per call.
How is bulk enrichment different from search?
Bulk enrichment works on records you already have and fills in or updates their fields. Search finds new records that match filter criteria. A common pattern uses search to discover and rank candidates cheaply, then bulk enrichment to complete only the records worth keeping.
How many records can you enrich in one call?
It depends on the provider. Apollo handles up to 10 people per bulk call, ZoomInfo and Crustdata up to 25, and People Data Labs up to 100 records. For very large jobs, some providers like ZoomInfo offer an asynchronous Bulk API that processes hundreds of thousands of records as a background job.
How is bulk enrichment billed?
Most providers bill by credit, but the model varies. Some charge per record attempted, others only when a record is matched and data is returned. Providers may also avoid charging twice for re-enriching a record within a set window, which matters for the true cost of recurring jobs.
Should you use a single API or a waterfall tool like Clay?
Use a single dedicated API when the job is one data type across many records, since it is simpler and cheaper at volume. Use a waterfall tool when coverage across varied data types is the problem and combining multiple providers meaningfully raises your match rate.
