abm.dev vs Bombora
People reach for this comparison expecting two versions of the same thing. They aren’t. Bombora answers who is in-market right now. abm.dev answers who that account actually is, with every value cited. Different layers of the same motion — which is why this page is less a contest and more a map of where each one sits.
A great rep once knew every account. Now your agents do.
The short version
Bombora is a B2B intent-data company. Its Company Surge data aggregates content-consumption signals across a publisher co-op to indicate which accounts are actively researching a topic, sold to marketing and ABM teams. It’s a signal layer — a way to spot demand before it raises its hand. That’s its center of gravity, and for teams trying to prioritize where to spend attention, it’s a real strength.
abm.dev is account-based marketing enrichment 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 — every value sourced, scored, and explained — built to be called inside your own agents and pipelines, not watched in a UI.
So they don’t really compete. Bombora tells you an account is in-market. abm.dev tells you, with citations, who that account is and how to reach the right person there. One narrows the field. The other does the homework. They sit naturally side by side.
What matters when an agent is the consumer
A human can eyeball a record and sense whether it’s stale. An agent can’t. It needs the data to carry its own evidence. So once an intent signal hands an account to an automated loop, 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 from whenever a record was last crawled. Where coverage-first vendors optimize for the size of a maintained database, abm.dev optimizes for live, cited, scored values.
And it favors quality over quantity. The usual pitch is the size of the database — hundreds of millions of records. Ours is the opposite: 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. 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). An agent can branch on that — trust the high-confidence email, re-verify the title that’s gone quiet, ignore the source it doesn’t rate. 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 what abm.dev does. It’s 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.” No fabricated facts. No silent fallbacks. An intent layer answers a different question — whether an account is showing demand — and we describe Bombora only in broadly-known terms; confirm its current features and pricing on Bombora’s own site.
Ten sources, one call
abm.dev enriches from ten data sources — LinkedIn, Companies House, Perplexity, Tavily, Hunter, and others — behind a single call, aggregated, deduped, reconciled into eighty-nine canonical fields 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: tell it the ICP or persona you’re hunting and it scores and structures the result for thataccount, rather than returning a generic blob you then have to interpret. That’s the natural next step after an intent signal flags an account — turn “this company is researching” into “here is who they are, who to contact, and why each value is trustworthy.”
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.
An intent platform is reached primarily through its own app, feeds, and the platforms it pipes scores into — excellent for sitting in a marketer’s prioritization workflow, less so if your “user” is a headless pipeline running at three in the morning that needs to do the research itself.
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. That fits spiky, automated workloads — an agent that enriches a thousand accounts this week and none the next. Packs start at around twenty-nine cents per enrichment, with all sources included; see the pricing page (/#pricing) for the latest. We describe Bombora’s commercial model only in broadly-known terms — confirm its current features and pricing on Bombora’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.
Side by side
| abm.dev | Bombora | |
|---|---|---|
| Built for | AI agents and pipelines | Marketing and ABM teams (intent signals) |
| Primary interface | Enrichment API + agent-native discovery (llms.txt, agent-tools.json, openapi.json) | Intent-data platform and feeds (broadly known) |
| Data model | Live resolution per call; quality over quantity (cited, scored) | Aggregated intent signals across a publisher co-op (broadly known) |
| Per-field citations | Yes — source on each field | Different layer (intent) — not asserted here |
| Per-field confidence | Yes — confidence scores returned | Different layer (intent) — not asserted here |
| Selection reason | Yes — why each value was chosen | Different layer (intent) — not asserted here |
| Sources per call | Ten sources (incl. LinkedIn), reconciled into eighty-nine fields | Publisher co-op content-consumption signals (broadly known) |
| Pricing model | Per-enrichment, no subscription, credits never expire | See Bombora’s own docs — not asserted here |
Where a cell says “not asserted here,” that’s deliberate — those are claims we won’t invent about another product, and several reflect that Bombora is an intent layer rather than contact enrichment. Confirm them against Bombora’s own documentation.
When each one fits
Reach for Bombora ifthe question you’re trying to answer is which accounts are in-market right now. If you want to surface demand early, prioritize where your team spends attention, and feed intent signals into the platforms your marketers already use, that’s the layer Bombora is built for. abm.dev does not aggregate intent signals, and won’t pretend to.
Reach for abm.dev ifyour “user” is an agent. If you’re building GTM automations that need account and contact 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 dashboard-watching.
And often the answer is both. Bombora flags the account that’s leaning in; abm.dev, called live from your pipeline, comes back with cited research on who they are and who to reach. Most ABM doesn’t fail on strategy. It fails on data and tooling — enrichment that’s stale, scattered across vendors, and built for dashboards instead of the agents and pipelines teams actually run now. That’s the gap abm.dev was built for.
Looking for a Bombora alternative?
A Bombora alternative search often turns out to be a category mix-up worth making explicit: Bombora sells intent signals; abm.dev sells the enriched, cited account and person records you act on once intent exists. If you came looking for richer data on the accounts that are surging, that’s exactly the abm.dev job.
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