abm.dev vs Lusha
If you’re choosing where to get contact and account data, you’ll likely weigh abm.dev against Lusha. They live in the same broad category, but they’re aimed at different readers. This page lays out the difference honestly, framed for the thing that’s changed: the buyer is increasingly an agent, not a person logging into a tool.
A great rep once knew every account. Now your agents do.
The short version
Lusha is a well-known B2B contact-data provider — business emails and phone numbers — offered through a web app and a browser extension. It’s popular with individual sales reps and small teams, and it’s sold on a subscription or credit basis. It’s a prospecting tool people log into: pull up a profile, grab the contact, work the list. For a rep building pipeline by hand, 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 a UI.
Same neighborhood. Different reader. One was built for a rep with the extension open in a browser tab. 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 worth trusting. 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 a 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.
This is the heart of the difference. A tool you log into is designed to show a contact 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.” A value is cited or it isn’t 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. 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. Ours is the opposite. You get fewer values, but every one carries its source, a confidence score from zero to one, and a selection_reason — and a value is returned only if it can be cited. No padding a record to look complete. No fabricated facts. 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 per-source bills, no per-field charges, no stitching vendors together by hand.
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.
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, all reachable over plain REST. One call does it: POST /api/v2/enrichments. There’s an MCP server too, at https://mcp.abm.dev/mcp (in Claude: Settings then Connectors then Custom, and paste the URL), 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. The integration target is the agent, not the human operator.
A web app with a browser extension is reached primarily through that app and that extension — excellent if a person is driving it, 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. Packs start at about twenty-nine cents per enrichment: thirty for €2.89, one hundred for €9.29, five hundred for €36.99 (best value), two thousand for €119.99. That fits spiky, automated workloads — an agent that enriches a thousand accounts this week and none the next. Contact-data tools in Lusha’s category have generally been sold on a subscription or credit basis; check Lusha’s own docs for current 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 | Lusha | |
|---|---|---|
| Built for | AI agents and pipelines | Sales reps and small teams (broadly known) |
| Primary interface | Enrichment API + agent-native discovery (llms.txt, agent-tools.json, openapi.json) + MCP | Web app + browser extension (broadly known) |
| Per-field citations | Yes — source on each field | Not asserted here / see Lusha’s own docs |
| Per-field confidence | Yes — confidence zero to one | Not asserted here / see Lusha’s own docs |
| Selection reason | Yes — why each value was chosen | Not asserted here / see Lusha’s own docs |
| Sources per call | Ten sources, reconciled into eighty-nine fields | Not asserted here / see Lusha’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, credits never expire | Subscription/credit (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 Lusha’s own documentation.
When each one fits
Reach for Lusha ifa person is doing the prospecting. If reps want to pull business emails and phone numbers from a web app or a browser extension while they work a list, that’s a mature, familiar experience built for exactly that — a human in the loop, clicking through profiles.
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 from ten sources in one call, discoverable and callable over REST and MCP without glue, and priced per call 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 scattered across vendors, locked behind an app, and built for people instead of the agents and pipelines teams actually run now. That’s the gap abm.dev was built for.
Looking for a Lusha alternative?
A Lusha alternative search usually starts when lookups need to happen programmatically — extension-and-seat workflows don’t scale into pipelines. abm.dev is built API-first: the depth of a full enrichment (not just phone and email) on every call, citations attached, priced per record.
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