abm.dev vs Cognism
If you’re choosing a contact-data layer for account-based marketing, you’ll likely weigh abm.dev against Cognism. They solve a similar problem from opposite ends. This page lays out the difference honestly, framed for the thing that’s changed: the buyer is increasingly an agent, not a person working a list in an app.
A great rep once knew every account. Now your agents do.
The short version
Cognism is a B2B sales-intelligence and contact-data platform, known for strong European coverage and phone-verified mobile numbers, with an emphasis on GDPR and compliance. It’s sold to sales and marketing teams on a subscription basis and used primarily through its app and CRM integrations. Its center of gravity is the seller working a list — and for outbound teams who live there, that’s a real strength.
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, not worked in a UI.
Same category. Different reader. One was built for a rep in a sales app. One was built for an autonomous agent loop.
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. So the questions that matter shift.
Per-field citations, confidence, and selection reasons
abm.dev returns research with provenance attached at the field level: each value carries where it came from, a confidence score from zero to one, and why it was chosen (its selection_reason). Emails go further, with a verification level and confidence of their own. An agent can branch on that — trust the high-confidence email, re-check the title it doesn’t rate, ignore the source it doesn’t trust. 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. An app-first product is designed to show a value to a person. An agent-first product is designed to defenda value to a program — 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.
Ten sources, one call
abm.dev resolves 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 per-source bills, no stitching three vendors together by hand, no reconciling conflicting records yourself.
It’s also goal-aware: pass a goal_override — the ICP or persona you’re hunting — and it shapes and scores the result for that goal, rather than returning a generic blob you then have to interpret.
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. Where coverage-first vendors optimise for the size of a maintained database, abm.dev optimises for live, cited, scored values.
The usual pitch is the size of the database — how many records it holds. 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. You get less, and you can trust all of it.
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 a plain REST API. One call does it: POST https://api.abm.dev/api/v2/enrichments, accepting either an x-api-key or a bearer token. There’s also an MCP server at https://mcp.abm.dev/mcp (tools include enrich_entity and get_enrichment_status). A Claude, OpenAI, LangChain, CrewAI, Cursor, or Windsurf agent can find the tools and call them with little glue. The integration target is the agent, not the human operator.
An app-and-CRM product is reached primarily through its app and its CRM integrations — excellent if your team lives in that app, less so if your “user” is a headless pipeline running at three in the morning.
Pricing shaped for programmatic use
abm.dev is per-enrichment: you pay for the calls you make, with no subscription or seat to carry, and credits never expire. All ten sources are included — no per-source bills, no per-field charges. It starts at about twenty-nine euro cents per enrichment, with packs from thirty enrichments for €2.89 up to two thousand for €119.99 (five hundred at €36.99 is the best value). That fits spiky, automated workloads — an agent that enriches a thousand accounts this week and none the next. Sales-intelligence platforms in the Cognism category have generally been sold on a subscription basis; check Cognism’s own pricing for specifics, as we don’t quote competitor pricing here.
The playground is free, and abm.dev is in open beta with around twenty dollars in free credits for every new account (code LAUNCHCODES) — enough to enrich a real list and judge it yourself.
Side by side
| abm.dev | Cognism | |
|---|---|---|
| Built for | AI agents and pipelines | Sales and marketing teams (broadly known) |
| Primary interface | Enrichment API + agent-native discovery (llms.txt, agent-tools.json, openapi.json) + MCP | App + CRM integrations (broadly known) |
| Per-field citations | Yes — source on each field | Not asserted here — see Cognism’s own docs |
| Per-field confidence | Yes — score from zero to one | Not asserted here — see Cognism’s own docs |
| Selection reason | Yes — why each value was chosen | Not asserted here — see Cognism’s own docs |
| Sources per call | Ten, reconciled into eighty-nine fields | Not asserted here — see Cognism’s own docs |
| Data model | Live resolution per call; quality over quantity (cited, scored) | Maintained contact database (broadly known) |
| Pricing model | Per-enrichment, no subscription | Subscription (broadly known) |
Where a cell says “not asserted here,” that’s deliberate — those are claims we won’t invent about another product. Confirm them against Cognism’s own documentation.
When each one fits
Reach for Cognism ifyour team is running human-led outbound and wants strong European coverage, phone-verified mobile numbers, and a compliance-minded contact database right inside the app and CRM your reps already work in. For sellers operating in a sales-intelligence app, that’s a mature, well-fitted experience.
Reach for abm.dev ifyour “user” is an agent. If you’re building GTM automations that need data which can defend itself — every field sourced, scored, and explained — discoverable and callable without glue, and priced per call rather than per seat. Built for autonomous agent loops, not human list-working.
Most ABM doesn’t fail on strategy. It fails on data and tooling — enrichment that’s scattered across vendors and built for apps instead of the agents and pipelines teams actually run now. That’s the gap abm.dev was built for.
Looking for a Cognism alternative?
People weighing a Cognism alternative are often after the same compliance-conscious data — minus the seat-based sales-intelligence wrapper. abm.dev is the API version of that job: GDPR-aware, per-record pricing, every field cited, and consumable by agents as easily as by reps.
Try it: bring a LinkedIn URL or a name plus company and watch it come back enriched. The playground is free, and open beta ships around twenty dollars in free credits — guides at abm.dev/resources.
Questions? Contact support