Best Batch Enrichment APIs in 2026: Compared on Cost, Freshness, and How They Handle Bulk

Database size is the wrong way to choose a batch enrichment API. The question that decides a batch project is what happens when you push tens of thousands of records through an API at once: what it costs, and how out of date the data is when it comes back. The best batch enrichment APIs handle both ends well, because batch enrichment is a trade-off between price at scale and freshness, and every vendor sits somewhere different on that line.

This guide compares the batch enrichment APIs builders actually use, scored on the mechanics that matter for bulk work: whether there is a real bulk endpoint, how big a job can be, what you pay when a lookup misses, and how old the data is by the time it reaches you. The specs come from vendor docs, the pricing from their own pages, and the complaints from teams running these APIs at volume. Where a number could not be verified at the source, it is labeled as such.

What a batch enrichment API actually is

A batch enrichment API takes a list of identifiers (domains, emails, company names, or person profile URLs) and returns enriched records for the whole list, usually through a bulk endpoint or an asynchronous job. It is one of three ways to enrich data at scale, and the three differ mostly on freshness and cost.

The middle row is where most enrichment pipelines live, and where the trade-off is sharpest. A batch API that reads from a cached database is cheap and fast but inherits however out of date that database is. One that can fetch live for the records you flag costs more per record but keeps the important rows up to date. Picking the right one starts with knowing which records actually need to be fresh, then choosing an API that lets you pay for freshness only there.

For a deeper split between the timing models, see our guide on real-time vs. batch enrichment.

How to evaluate a batch enrichment API

Database size barely matters for batch work. These four properties do.

Does it have a real bulk endpoint, and how does the job run?

A true batch API lets you submit many records in one request or one job, rather than forcing you to loop a single-record endpoint. The numbers vary widely. Apollo's bulk people enrichment caps at 10 records per call. People Data Labs allows 100 per bulk request. Coresignal's Bulk Collect runs large batches in a single async job. The job model matters as much as the cap: a synchronous endpoint blocks until results return, while an asynchronous job lets you submit, walk away, and collect results by webhook or by polling for status. For large lists, async with a completion webhook is the difference between a clean overnight run and a script babysitting retries.

What do you pay when a lookup misses?

This is the cost question that listicles skip, and the one practitioners raise first. On a waterfall or a credit model, an empty result can still cost you. As one GTM engineer put it on r/gtmengineering, "you still pay credits on failed lookups depending on how you sequence the providers, so order matters more than people expect." Check whether the API charges on a no-result. Crustdata, for example, does not deduct credits when no data is returned. On a 100,000-record job with a 60% match rate, charge-on-miss billing means paying for 40,000 empty responses.

How fresh is the data at scale?

Bulk data is cheap because it is cached, and cached data drifts. People Data Labs updates its dataset monthly, which a buyer evaluating it on Reddit described bluntly: "found a bunch of contacts with companies they left 6+ months ago. my manager keeps asking why we're paying this much for stale records." For records tied to outreach or routing, a monthly cache can be a problem. The fix is an API that lets you force a live fetch on the rows that matter, instead of trusting the cache for all of them.

What match rate should you actually expect?

Vendor "match rate" claims rarely survive contact with a real list. Practitioners who benchmark report single-source enrichment landing around 55% to 70%. On the PDL thread above, a user measured "around 65-70% on our test sets," and a r/SalesOperations discussion put most vendors at "55-65% data accuracy" with only marginal differences between them. Plan for a waterfall or a second source if you need to clear that ceiling, and benchmark on your own data before committing (covered below).

The best batch enrichment APIs in 2026

The table below summarizes how each API handles bulk work. Details and trade-offs follow.

Crustdata

Crustdata is an API and dataset platform that enriches companies and people from live sources, built for teams putting data into their own products and pipelines rather than browsing it in a dashboard. For batch work, its draw is the ability to run bulk enrichment against a database for cost, then force a live fetch on the specific records that need to be up to date.

Key features:

• Company and people enrichment APIs that accept up to 25 identifiers per call (domain, name, profile URL, or company ID)

• An enrich_realtime flag that fetches live for records not in the database, so you pay the higher real-time rate only where it matters

• A free company identification endpoint for screening a list before you spend credits enriching it

• A Watcher API that pushes job changes, funding, and hiring events by webhook, so records stay up to date after the initial batch

Pros:

• No credits charged when a lookup returns no data, which removes the charge-on-miss penalty on large jobs

• Live-fetch option keeps priority records fresh without paying real-time prices for the whole list

• Delivered as API, bulk dataset, and webhooks, so one provider covers cost-at-scale and freshness

Cons:

• Search endpoints bill per query rather than per record returned, which can surprise teams expecting strict per-record pricing (raised by technical evaluators in our own call research)

• Bulk flat-file coverage in some narrow niches is smaller than specialist data-dump vendors, so very wide one-time pulls may need a second source

Best for: Developers, RevOps, and GTM engineers building enrichment pipelines who want to enrich a large list cheaply from the database while keeping their highest-value records live. Teams replacing an out-of-date monthly-refresh provider will find the live-fetch flag the most direct fix. You can sign up for the free tier with 100 credits to benchmark it on your own list.

People Data Labs

People Data Labs (PDL) is a raw-data API that developers use to build their own enrichment and search products on top of a large person and company dataset. It is API-first with clean documentation, which is why it shows up in so many internal builds.

Key features:

• A Bulk Person Enrichment endpoint that accepts up to 100 records per request

• A person dataset of over 2.4 billion records plus a separate company dataset

• A 1-credit charge per successful (200) record in a bulk call, with no charge on a miss

Pros:

• Large raw dataset and developer-friendly API, well suited to building a custom enrichment layer

• High per-request batch size (100) keeps throughput up on big jobs

• Predictable per-success billing on bulk calls

Cons:

• Data updates ship monthly, so job-change-sensitive records arrive out of date, a complaint echoed on Reddit: "they update their data monthly so by the time you're enriching, some percentage of contacts have already moved"

• The bulk endpoint is synchronous with a 1MB response cap, so very large records can force smaller batches

• Weaker mobile and phone coverage means most teams pair it with a second provider

Best for: Engineering teams building an internal enrichment or search product who want raw data and can handle their own dedup, freshness checks, and waterfall logic. See our People Data Labs alternative comparison if monthly freshness is a blocker.

Coresignal

Coresignal is a bulk data provider with an asynchronous collection API aimed at teams that need large volumes of employee, company, and job-posting data. Its Bulk Collect product is the closest thing on this list to a purpose-built large-job API.

Key features:

• Bulk Collect endpoints that return large batches in a single async job, delivered by webhook or polling

• Coverage of 882M+ employee records and 75M+ company records

• Documented rate limits, including 27 requests per second on Bulk Collect

Pros:

• Large single-job pulls suit one-time bulk builds and warehouse loads

• Async job model with webhook completion fits overnight batch pipelines cleanly

• Transparent, published pricing starting at $0.030 to $0.005 per record on the Premium tier

Cons:

• Bulk Collect returns raw profile data without verified emails or phone numbers, so revenue teams add a contact-data step (noted in third-party reviews on Prospeo)

• Raw data needs meaningful preprocessing and dedup before it is usable, per the same reviews

• The jump from the $49 Starter tier to the $800 Pro tier leaves little middle ground for smaller teams (pricing)

Best for: Data and platform teams doing large one-time or scheduled bulk pulls into a warehouse, who have the engineering capacity to clean and enrich raw profiles downstream. Our Coresignal alternative page covers the contact-data gap in more detail.

Apollo

Apollo is a sales platform with a large contact database and an enrichment API bundled alongside its outreach tools. It is a common starting point because of its size and generous entry pricing.

Key features:

• Bulk people and organization enrichment endpoints, capped at 10 records per call

• A contact and company database Apollo lists at 210M+ contacts and 35M+ companies (its product page cites 230M+ contacts)

• Asynchronous waterfall email and phone enrichment delivered to a webhook when those parameters are enabled

Pros:

• Enrichment bundled with outreach, useful for teams that want one tool for data and sending

• Async waterfall option for email and phone fills gaps the base record misses

• Documented bulk rate limit at 50% of the single-endpoint per-minute rate

Cons:

• The 10-record-per-call cap means batch at scale runs as many sequential calls rather than one large job

• Rate-limit friction (HTTP 429) is a recurring issue for high-volume API users, confirmed in a Make community thread

• Accuracy outside North America and out-of-date data are the most-cited complaints in user reviews

Best for: Sales teams that want enrichment and outreach in one platform and are working with moderate list sizes rather than warehouse-scale jobs. For an API-first comparison, see our Apollo alternative guide.

ZoomInfo

ZoomInfo is an enterprise sales-intelligence platform with a batch enrichment API built for scale. It is the most enterprise-oriented option here, with pricing and access gated behind sales.

Key features:

• A batch enrichment API capped at 25 records per Enrich call, for both companies and contacts

• Documented rate limits and retry guidance: 25 requests per second by default, higher on premium add-ons, with defined handling for 429 and 5xx responses

• A database ZoomInfo lists at 410M+ contact profiles and 203M+ company records

Pros:

• Batch enrichment with documented throughput and retry behavior, suited to scaled jobs

• One credit per record per rolling 12 months, so re-enriching a managed record within the year is free (credit docs)

• Broad North American mid-market and enterprise coverage

Cons:

• API access is an enterprise add-on with no public pricing, so budgeting requires a sales conversation (credit and usage docs)

• Credit consumption runs faster than teams expect once enrichment and refresh streams are active, a recurring cost complaint (summarized on Prospeo)

• Out-of-date contacts that recommend people who have left their roles are a recurring review theme

Best for: Enterprise RevOps and sales teams that already run ZoomInfo and need scaled enrichment inside an existing contract. The async job model is strong, and the main constraint is commercial access rather than the technology.

Cognism

Cognism is a sales-intelligence platform strongest in EMEA, with a data-as-a-service option for teams that want bulk delivery rather than a per-record API. Its batch path is scheduled delivery into a warehouse rather than a synchronous record endpoint.

Key features:

• Data-as-a-service delivery by API or scheduled batch into Snowflake, S3, Google Cloud, Databricks, or SFTP

• A CRM enrichment product in beta for keeping records up to date

• Director-level-and-above contacts refreshed every 30 days

Pros:

• Strong EMEA coverage and verified mobile numbers, an area where US-first providers thin out

• Warehouse batch delivery fits data teams that prefer scheduled loads over live calls

• Credit model charges once per revealed contact, with no charge to re-view

Cons:

• No published pricing or record counts, and both are quote-gated (pricing)

• The batch path is warehouse delivery, so there is no documented synchronous record-batch endpoint for live pipelines

• Weaker US and APAC coverage relative to its European strength, per user reviews summarized on SyncGTM

Best for: Teams whose primary market is Europe and who want bulk data delivered on a schedule into a warehouse. RevOps teams that need verified EMEA mobile numbers will find the strongest fit here.

Clearbit (Breeze Intelligence by HubSpot)

Clearbit was a developer-favorite enrichment API before HubSpot acquired it and folded it into Breeze Intelligence. Today it is a HubSpot-native feature rather than a standalone batch API, which reshapes who it fits.

Key features:

• Bulk enrichment inside the HubSpot UI, capped at 100 records per index-page action

• A dataset HubSpot lists at over 100M company domains and 380M email addresses

• Automatic refresh of enriched records, continuously reviewed and refreshed through HubSpot's data quality process

Pros:

• Clean, consistent data for HubSpot customers with no separate integration to build

• Enrichment is handled natively inside the CRM, with no pipeline to build or maintain

• Strong mid-market and enterprise firmographic coverage

Cons:

• No public enrichment API for new customers since the acquisition, so programmatic batch work outside HubSpot is not available (documented across migration writeups such as Cleanlist)

• Bulk enrichment is limited to 100 records per UI action, which does not suit warehouse-scale jobs

• Like any single-source provider, a bulk run leaves the records it cannot match un-enriched, so teams needing high fill add a second source

Best for: HubSpot customers who want firmographic enrichment handled natively inside the CRM and do not need a programmatic batch API. Teams that need API access at scale will have to look elsewhere.

Clay

Clay is a data orchestration workbench rather than a batch enrichment API. It runs more than 130 third-party providers behind a spreadsheet-style interface and charges credits to route data through them. It belongs on this list because teams reach for it to run bulk enrichment, even though the mechanics are different.

Key features:

• Table-based enrichment of up to 50,000 rows per table, run through marketplace providers

• A dual-credit model: platform "Actions" plus "Data Credits" that start at $0.05 each

• Bring-your-own-API-key support, so you can route to providers you already pay for

Pros:

• Access to many providers in one place, useful for building a waterfall without separate integrations

• Bring-your-own-key keeps the underlying data cost low while Clay handles orchestration

• Flexible for non-standard enrichment logic across rows

Cons:

• Credit burn on failed and waterfall lookups has long been the dominant complaint, enough that Clay's 2026 pricing change stopped charging for failed lookups, so confirm current behavior on your plan

• It is a workbench rather than a REST API, so embedding it directly into a product pipeline is awkward

• A markup sits on top of the providers you are already paying for, which a high-volume operator on r/coldemail sized as 10x or more versus bringing your own keys

Best for: RevOps and growth teams that want a visual way to build and test enrichment waterfalls without writing pipeline code, and who watch credit consumption closely. For an API-first path that embeds in a product, see our Clay alternative comparison.

The pattern that controls credit spend: screen, then enrich

The biggest cost lever in batch enrichment is how many records you enrich at full price in the first place. Enriching a raw 100,000-row list end to end pays full freight for rows that were never going to match or were never worth the spend. The fix is a two-step pattern that screens the list cheaply, then enriches only what passes.

A Claude Code agent with Crustdata's MCP server configured can run this conversationally, or you can call the REST API directly. Both hit the same data, so pick the path that fits your stack.

The screen step uses the free company identification endpoint to resolve and filter a list at no credit cost, so you only spend credits on the records you keep:

Keep only the records that resolve cleanly and match your criteria, then enrich that shorter list. The enrichment call accepts up to 25 identifiers at once and only charges for records that return data:

The response returns the requested fields as structured JSON, ready to write back to your CRM or warehouse. On a list where only 60% of rows are worth enriching, screening first cuts the enrichment bill by close to 40% before a single full-price call runs. New accounts get 100 credits free to test the pattern on a real list.

Keeping batch data fresh without paying real-time prices

The reason batch data goes out of date is that bulk endpoints read from a cache, and caches refresh on a schedule. Paying real-time rates for an entire list to avoid that is wasteful, because most records do not change between runs. The workable middle path is to enrich the bulk of the list from the database and force a live fetch only on the records that need to be up to date.

Crustdata's enrichment API supports this with the enrich_realtime flag, which fetches live for records not already in the database at a higher per-record rate, while the rest of the list draws from the cache at the standard rate. You decide which rows get the live treatment, usually the ones tied to active outreach, routing, or scoring.

For records that need to stay fresh after the batch runs, the Watcher API pushes updates by webhook when a tracked person changes jobs or a company raises funding or starts hiring. Instead of re-running the whole batch to catch changes, you batch once for the baseline and let webhooks deliver the deltas. That keeps a 100,000-record set up to date without a full re-enrichment every month, and it is the structural answer to the out-of-date-data complaint that follows monthly-refresh providers.

How to benchmark a batch enrichment API before you commit

Vendor accuracy claims are marketing, and the only number that counts is the match rate on your own data. Before signing anything, run a blind test, a practice experienced buyers treat as standard.

• Pull a representative sample of 500 to 1,000 records from your real list rather than a clean demo set. Include the messy, international, and long-tail records, since those are where providers diverge.

• Run the sample through each API's free tier or trial and capture the raw output. Most providers, including Crustdata's free tier, give enough credits to test a sample.

• Measure match rate and field fill on the fields you actually use, then verify a slice by hand. Treat email deliverability separately by sending to a verification step, since a returned email has not been confirmed as deliverable.

• Compare cost on the real result, including any charge-on-miss, rather than the headline per-record price.

Expect single-source match rates in the 55% to 70% range, consistent with what practitioners report. If a vendor's blind-test number lands far above that, test a larger sample before you believe it. The benchmark also tells you whether you need a second provider in a waterfall to clear the gaps the first one leaves.

Choosing a batch enrichment API without an engineer

Not every team has an engineer free to wire up an API, and you do not need one to run batch enrichment. Two paths work without writing pipeline code.

The first is an MCP integration. A Claude Code agent or another MCP client pointed at Crustdata's MCP server can screen a list, enrich the matches, and write results to a sheet through plain instructions, with no orchestration code to maintain. It is the closest thing to a no-code path that still hits the live API directly.

The second is a managed workbench like Clay, which gives you a spreadsheet interface over many providers. It trades some cost efficiency for ease of use, so watch credit consumption if your lists are large. For non-technical RevOps teams that want enrichment handled inside the CRM, HubSpot's native Breeze enrichment covers firmographics without any setup, within its 100-record-per-action limit.

The trade-off is straightforward: the more of the pipeline a tool handles for you, the more you pay per record and the less control you have over freshness and cost. Start with the path that matches your team, and move toward the direct API as your volume grows.

Conclusion

The best batch enrichment API for your team is the one that fits where you sit on the cost-versus-freshness line, rather than the one with the biggest database. Run the comparison on the four properties that decide a batch project: a real bulk endpoint and job model, what you pay on a miss, how fresh the data is at scale, and the match rate on your own list.

A few takeaways to act on:

• Screen before you enrich: Filtering a list with a free resolution step before spending credits is the largest cost lever available, often cutting the enrichment bill by a third or more.

• Pay for freshness selectively: Enrich the bulk of a list from the database and force a live fetch only on the records that trigger action, then let webhooks deliver changes instead of re-running the whole batch.

• Benchmark on your data: Blind-test 500 to 1,000 real records before committing, and expect 55% to 70% single-source match rates.

If you are building a pipeline that needs both low cost at scale and fresh data on the records that matter, sign up for Crustdata's free tier with 100 credits and benchmark the screen-then-enrich pattern on your own list. Teams running enterprise-scale volume can book a demo to walk through the architecture.

Frequently asked questions

What is a batch enrichment API?

A batch enrichment API takes a list of identifiers (domains, emails, names, or profile URLs) and returns enriched records for the whole list through a bulk endpoint or an asynchronous job, rather than one record at a time. It sits between live per-record APIs, which are freshest and priciest, and bulk flat-file datasets, which are cheapest and most out of date.

Do I pay for failed enrichment lookups?

It depends on the provider. Some credit and waterfall models charge even when a lookup returns nothing, which on a large job with a 60% match rate means paying for tens of thousands of empty responses. Others, including Crustdata, do not deduct credits when no data is returned. Always check the charge-on-miss policy before running a big batch.

What match rate should I expect from a batch enrichment API?

Practitioners who benchmark on real lists report single-source match rates around 55% to 70%, with only marginal differences between major vendors. To clear that ceiling you generally need a waterfall that falls back to a second provider for the records the first one misses. Test on your own sample rather than trusting a vendor's headline number.

Why is my batch-enriched data out of date?

Bulk endpoints usually read from a cached database that refreshes on a schedule, often monthly, so by the time you enrich, some contacts have already changed jobs. The fix is an API that lets you force a live fetch on the records that need to be up to date, plus webhooks that push changes between batches so you are not re-running the whole list to catch updates.

Is it cheaper to bring my own API keys than to use a credit-based tool?

Often, yes. High-volume operators report that routing through a credit-based workbench can cost many times more than calling underlying providers directly, because of platform markup and credit burn on failed lookups. If you have engineering capacity, calling a batch API directly is usually cheaper at scale, while a managed workbench buys ease of use at a higher per-record cost.

Should I use one enrichment provider or a waterfall of several?

If a single source clears your required match rate on a blind test, one provider keeps things simpler and cheaper. If it leaves too many gaps, a waterfall that falls back to a second source for misses raises coverage, at the cost of more credits spent on failed first attempts. Sequence the cheapest, highest-hit provider first so you pay the premium sources only on the records that need them.