abm.dev vs People Data Labs
If you’re wiring person and company data into a pipeline, you’ll likely weigh abm.dev against People Data Labs. Both are API-first, so the honest contrast isn’t whether there’s an API — it’s how the data arrives, and what it carries when it gets there. This page lays that out for the thing that’s changed: the consumer is increasingly an agent, not a person at a dashboard.
A great rep once knew every account. Now your agents do.
The short version
People Data Labs (PDL) is a developer-focused person- and company-data provider. It serves data through an enrichment API and bulk datasets, typically sold on usage or contract terms. It’s API-first and built for engineers — which is genuinely the same starting point as abm.dev, not the opposite end.
abm.dev is account-based marketing for AI agents. The core is an enrichment API: hand it a person or company, get back verified contact data plus deep, synthesized account research — built to be called inside your own agents and pipelines.
Same category. Same front door — an API. The difference is what comes back through it. One returns data. One returns data that can defend itself to a program.
What matters when an agent is the consumer
A human can eyeball a record and sense whether it’s right. An agent can’t. It needs the data to carry its own evidence. When both products expose an API, that’s where the real question lives — not in the endpoint, but in the payload.
Per-field citations, confidence, and selection reasons
abm.dev returns research with provenance attached at the field level. Every value carries three things: its source, a confidence score from zero to one, and a selection_reason — why that value was chosen over the alternatives. There’s a fieldAttribution array of field, value, and confidence with per-source citations, plus an email_verification_level and confidence on emails. An agent can branch on that — trust the high-confidence email, treat a low-confidence title with care, prefer the source it rates. The judgment moves into the loop, where the agent can act on it, instead of staying in a human’s head.
This is the heart of the difference. A raw-data API is designed to deliver a value. An agent-first API is designed to defenda value — to say not just “this is the title” but “this is the title, from this source, this confident, and chosen for this reason.” A value is cited or it is not returned. No fabricated facts. No silent fallbacks.
Live data, quality over quantity
abm.dev resolves each enrichment live, at call time, across its sources — live web research via Perplexity and Tavily, email verification via Hunter, plus LinkedIn, Companies House, and others. The answer reflects the world at the moment you ask, not a snapshot from whenever a record was last crawled. The resolution happens when the agent calls, against the live web.
The usual pitch is the size of the database — hundreds of millions of records. abm.dev’s pitch is the opposite. Fewer values, but every one carries its source, a confidence score from zero to one, and a selection_reason. A value is returned only if it can be cited. No padding a record to look complete. No filler. Where coverage-first vendors optimise for the size of a maintained database, abm.dev optimises for live, cited, scored values. You get less, and you can trust all of it.
Ten sources, one call
abm.dev resolves from ten data sources — LinkedIn, Companies House, Perplexity, Tavily, Hunter, and others — behind a single call, reconciled into eighty-nine canonical fields (forty-three person, forty-six company) plus forty signals. One request, one normalized shape. No stitching three vendors together by hand, no reconciling conflicting records yourself, no per-source bills.
It’s also goal-aware: pass a goal_override — an ICP or persona — and it shapes and scores the result for that goal, rather than returning a generic record you then have to interpret. The reconciliation and the scoring are the product, not an exercise left to the caller.
Agent-native access, by design
abm.dev publishes the surfaces agents use to discover and call tools on their own — /llms.txt, /agent-tools.json, and /openapi.json at the root, reachable over plain REST. There’s also an MCP server at https://mcp.abm.dev/mcp — in Claude, add it under Settings, Connectors, Custom — exposing tools like enrich_entity and get_enrichment_status. A Claude, OpenAI, LangChain, CrewAI, Cursor, Claude Code, or Windsurf agent can find the tools and call them with little glue.
A data API is reached the way most data APIs are — read the docs, write the client, wire the auth. Whether a given provider ships MCP or agent-discovery surfaces is its own claim to make; we don’t assert it here. See People Data Labs’ own docs for what it offers.
Pricing shaped for programmatic use
abm.dev is per-enrichment: you pay for the calls you make, with no subscription and no seat to carry. Credits never expire, and all ten sources are included — no per-source bills, no per-field charges. It starts from about twenty-nine cents (around €0.29) per enrichment, with packs of thirty for €2.89, one hundred for €9.29, five hundred for €36.99 (best value), and two thousand for €119.99. That fits spiky, automated workloads — an agent that enriches a thousand accounts this week and none the next.
Developer-focused data providers are commonly sold on usage or contract terms; the specifics are People Data Labs’ to state, so check its own pricing — we don’t quote competitor pricing here. abm.dev is in open beta, with around twenty dollars in free credits for every new account (code LAUNCHCODES), and the playground is free — enough to enrich a real list and judge it yourself.
Side by side
| abm.dev | People Data Labs | |
|---|---|---|
| Built for | AI agents and pipelines | Developers (general) |
| Primary interface | Enrichment API + MCP + agent-native discovery (llms.txt, agent-tools.json, openapi.json) | Enrichment API + bulk datasets (broadly known) |
| Data model | Live resolution per call; quality over quantity (cited, scored) | Large maintained datasets (broadly known) |
| Per-field citations | Yes — source on each field | Not asserted here |
| Per-field confidence | Yes — confidence scores returned | Not asserted here |
| Selection reason | Yes — why each value was chosen | Not asserted here |
| Sources per call | Ten sources, reconciled into eighty-nine fields | See People Data Labs’ own docs |
| Goal-aware output | Yes — scored/structured to your ICP | Not asserted here |
| Pricing model | Per-enrichment, no subscription, credits never expire | Usage/contract terms (general) |
Where a cell says “not asserted here,” that’s deliberate — those are claims we won’t invent about another product. Confirm them against People Data Labs’ own documentation.
When People Data Labs is the better answer
Reach for People Data Labs if you need broad person- and company-data coverage delivered as raw records — and especially if your use case wants bulk datasets to load and process yourself, under usage or contract terms. If you have the engineering to reconcile, score, and verify the data in your own stack, a flexible raw-data provider is a sound foundation.
When abm.dev is the better answer
Reach for abm.dev if your consumer is an agent. If you’re building GTM automations that need data which can defend itself — every field sourced, scored, and explained — reconciled from ten sources in one call, shaped to your ICP, discoverable and callable over REST or MCP without glue, and priced per enrichment rather than per seat. Built for autonomous agent loops, not human dashboard-watching.
Most ABM doesn’t fail on strategy. It fails on data and tooling — enrichment that’s unreconciled, scattered across vendors, and handed over without evidence the agent can act on. That’s the gap abm.dev was built for.
Looking for a People Data Labs alternative?
A People Data Labs alternative search is usually about the work after the API call: PDL hands you excellent raw records; you still build the synthesis, scoring, and source-tracking. abm.dev does that layer for you — multi-source synthesis with citations and confidence per field — so an agent can consume the answer directly instead of a firehose.
Try it: bring a LinkedIn URL or a name plus company and watch it come back enriched — with a source, a confidence, and a reason on every field. Open beta, around twenty dollars in free credits, and the playground is free — guides at abm.dev/resources.
Questions? Contact support