abm.dev vs UpLead
If you’re choosing where your account data comes from, you’ll likely weigh abm.dev against UpLead. Both put verified B2B contact data in front of you, but 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 building a list in a web app.
A great rep once knew every account. Now your agents do.
The short version
UpLead is a B2B contact database with real-time email verification, used through a web app to build verified prospect lists — sold on a subscription or credit basis to sales teams. You search the database, filter to your criteria, and export a verified list. Its center of gravity is coverage: a large pool of contacts you can prospect into. For a rep building a list to work, 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 browsed in a UI.
Same neighborhood. Different reader. One was built for a rep filtering a list in a web 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 fits. An agent can’t. It needs the data to carry its own evidence. 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 row pulled from a maintained database. A coverage-first product optimizes for the size of that database — how many contacts you can reach. abm.dev optimizes for live, cited, scored values resolved per call.
And it favors quality over quantity. The usual pitch is the size of the database — millions of contacts to filter. 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, set aside 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 list.
This is the heart of the difference. A database-first product is designed to show a contact to a person filtering a list. 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.” No fabricated facts. No silent fallbacks.
Ten sources, one call
abm.dev enriches from LinkedIn and ten-plus providers 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 that account, rather than returning a generic blob 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 web-app product is reached primarily through its interface — search, filter, verify, export. Excellent if a person is the one building the list, 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, credits never expire, and all sources are included. That fits spiky, automated workloads — an agent that enriches a thousand accounts this week and none the next. From about €0.29 per enrichment; see the pricing page (/#pricing) for the latest. Contact databases like UpLead have generally been sold on a subscription or credit basis tied to seats and list volume — confirm UpLead’s current features and pricing on UpLead’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. There’s also a free playground.
Side by side
| abm.dev | UpLead | |
|---|---|---|
| Built for | AI agents and pipelines | Sales teams building prospect lists |
| Primary interface | Enrichment API + agent-native discovery (llms.txt, agent-tools.json, openapi.json) | Web app for search, filter, and export (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) | Maintained contact database with email verification (broadly known) |
| Sources per call | LinkedIn + ten-plus, reconciled into eighty-nine fields | Not asserted here |
| Pricing model | Per-enrichment, no subscription, credits never expire | Subscription/credit, sold to sales teams (broadly known) |
Where a cell says “not asserted here,” that’s deliberate — those are claims we won’t invent about another product. Confirm UpLead’s current features and pricing on UpLead’s own site.
When each one fits
Reach for UpLead ifa person is building the list. If you want to search a broad contact database, filter to your criteria, verify the emails, and export a clean prospect list to work — a mature web app for sales teams who live in that motion — that’s its strength. We describe UpLead only in broadly-known terms; confirm its current features and pricing on UpLead’s own site.
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 human list-building.
Most ABM doesn’t fail on strategy. It fails on data and tooling — enrichment that’s scattered across vendors, exported once and going quiet, and built for a web app instead of the agents and pipelines teams actually run now. That’s the gap abm.dev was built for.
Looking for a UpLead alternative?
An UpLead alternative search is usually about depth: list-building gives you names and emails; campaigns increasingly need the full, defensible story per record. abm.dev returns 89 canonical fields with citations and confidence on each — per-record pricing, no subscription cliff.
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