abm.dev vs Kaspr
If you’re choosing how to get contact data for account-based marketing, you’ll likely weigh abm.dev against Kaspr. They sit in the same category from opposite ends: one is a browser extension a person drives by hand, the other is an enrichment API an agent calls. This page lays out the difference honestly, framed for the thing that’s changed: the buyer is increasingly an agent, not a person clicking through profiles.
A great rep once knew every account. Now your agents do.
The short version
Kaspr is a B2B contact-data tool focused on LinkedIn prospecting — surfacing emails and phone numbers through a browser extension, popular in Europe, on a freemium-plus-subscription model, and part of the Cognism group. Its center of gravity is the LinkedIn workflow: a person browsing profiles, clicking to reveal contact details, building lists by hand. For reps who live in LinkedIn, that’s a real strength.
abm.dev is account-based marketing enrichment built 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 clicked through in an extension.
Same category. Different reader. One was built for a person driving an extension. One was built for an autonomous agent loop.
What matters when an agent is the consumer
A person browsing LinkedIn can eyeball a profile and sense whether the contact details look right. An agent can’t. It needs the data to carry its own evidence, and it needs to fetch it without a human clicking a button. So the questions that matter shift.
Live data, quality over quantity
abm.dev resolves each enrichment live, at the moment you call it — running web research through Perplexity and Tavily, verifying email through Hunter, and reading LinkedIn, Companies House, and others on the spot. The answer reflects the world when you ask, not a snapshot pulled from whenever a record was last collected. The data is resolved at query time, not served from a maintained static database.
And it favours quality over quantity. The result comes back as fewer values, but every one carries its source, a confidence score from zero to one, and the reason it was chosen. A value comes back only if it can be cited — cited or not returned. No padding a record to look complete. You get less, and you can trust all of it.
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, and why it was chosen (its selection_reason). Emails additionally carry a verification level and confidence. 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 person’s head while they scan a profile.
This is the heart of the difference. A click-to-reveal tool is designed to show a contact detail to a person. An agent-first product is designed to defenda value to a program — to say not just “this is the email” but “this is the email, from this source, this confident, and chosen for this reason.” No fabricated facts. No silent fallbacks.
Ten sources, one call
abm.dev enriches from LinkedIn, Companies House, Perplexity, Tavily, Hunter, and others — ten sources behind a single call, aggregated, deduped, and reconciled into eighty-nine canonical fields plus forty signals. One request, one normalized shape. No driving an extension profile by profile, no stitching contact details together by hand.
It’s also goal-aware: tell it the ICP or persona you’re hunting and it scores and structures the result for that goal, rather than returning a generic record you then have to interpret.
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 — and it’s reachable over a plain REST API. There’s also a hosted MCP server at https://mcp.abm.dev/mcp exposing 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. The integration target is the agent, not the human operator.
A browser extension is reached primarily through the browser, by a person clicking on a profile — excellent if your workflow is one rep prospecting in LinkedIn, 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, all sources included, and credits that never expire. That fits spiky, automated workloads — an agent that enriches a thousand accounts this week and none the next. We describe Kaspr only in broadly-known terms — a freemium tier plus subscription — so confirm its current plans and limits on Kaspr’s own site.
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. See the pricing page (/#pricing) for the latest rate.
Side by side
| abm.dev | Kaspr | |
|---|---|---|
| Built for | AI agents and pipelines | Reps prospecting on LinkedIn |
| Primary interface | Enrichment API + agent-native discovery (llms.txt, agent-tools.json, openapi.json) | Browser extension (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 |
| Data model | Live resolution per call; quality over quantity (cited, scored) | Not asserted here |
| Sources per call | LinkedIn + ten, reconciled into eighty-nine fields | LinkedIn-focused contact data (broadly known) |
| Pricing model | Per-enrichment, no subscription, credits never expire | Freemium plus 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 Kaspr’s own documentation.
When each one fits
Reach for Kaspr ifyour workflow is a person prospecting in LinkedIn — browsing profiles and pulling emails and phone numbers as you go, with a free tier to start and a subscription as you scale. For a rep who lives in the LinkedIn tab, that’s a natural fit.
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 — resolved live at call time, discoverable and callable without glue, and priced per call rather than per seat. Built for autonomous agent loops, not a person driving an extension.
Most ABM doesn’t fail on strategy. It fails on data and tooling — contact details gathered by hand, one profile at a time, and built for a person clicking through a browser instead of the agents and pipelines teams actually run now. That’s the gap abm.dev was built for.
Looking for a Kaspr alternative?
A Kaspr alternative search usually marks the jump from extension-based lookups to programmatic scale. abm.dev is that jump: EU-conscious, API-first enrichment with citations on every field — the same job, callable from your pipeline ten thousand times instead of one profile at a time.
Try it: bring a LinkedIn URL or a name plus company and watch it come back enriched. Open beta, around twenty dollars in free credits — guides at abm.dev/resources.
Questions? Contact support