Databar.ai
No-code enrichment platform that routes waterfalls across 160+ data providers and sends an AI agent to the open web when none of them has the answer.
RecommendedLast reviewed September 17, 2026
What Databar.ai actually is.
Databar runs enrichment through more than 160 data providers from inside one workspace: firmographics, funding and hiring signals, tech stack detection, and traffic data, without a separate subscription for each source. A waterfall tries providers in sequence and only spends a credit on the one that actually returns an answer, so a miss on the first source costs nothing. When none of the 160-plus providers has an answer, an AI Research Agent reads the open web itself and comes back with a structured result and source citations instead of a blank cell.
Every table doubles as three things: a spreadsheet a person edits by hand, an API endpoint another system can call, and an MCP server an agent can query directly. Two-way sync into HubSpot, Pipedrive, and Attio keeps enriched records flowing back into a CRM without an export step, and workflows chain triggers and conditions so a team can rerun the same logic instead of rebuilding it. The catch: unlimited custom HTTP APIs and bring-your-own-key providers sit behind the $495/month Scale tier, so a team wiring in many proprietary APIs will hit Build's 5-custom-API ceiling fast.
What it does
- Aggregates enrichment data from 160+ providers, including People Data Labs, Hunter.io, BuiltWith, and Diffbot, inside one workspace.
- Runs waterfall enrichment that tries providers in sequence and only spends credits on the ones that actually return data.
- Sends an AI Research Agent onto the open web when no connected provider has an answer, and returns the result with source citations.
- Filters companies and people by criteria other databases don't track, such as funding by investor, hiring by function, or tech mentioned in job posts.
- Tracks funding rounds, hiring spikes, job changes, and technology adoption as ongoing signals.
- Syncs two-way with HubSpot, Pipedrive, and Attio, and pushes records out to Salesforce and outreach platforms.
- Exposes every table as a spreadsheet, an API endpoint, and an MCP server for direct agent access.
The credits meter results, not attempts, but the workflow ceiling still meters the plan.
Where it sits in the pipeline.
- AI & Intelligence
- Data & Enrichment
- Scraping & Custom Data
- Email Infrastructure & Deliverability
- Outreach & Sending
- LinkedIn & Multichannel
- Signals & Intent
- Calls & Meetings
- CRM & Pipeline Ops
Runs well with
The tools we'd run next to Databar.ai, and what each pairing covers.
Pricing, without the fiction.
From the Databar.ai pricing page · verified September 17, 2026
- 5,000 credits per month
- CRM and email integrations, bring your own API keys
- Up to 5 custom HTTP APIs, batch enrich up to 10k rows
- 3 workspace editors, 5 simultaneous requests
- Standard queue priority, 20MB CSV uploads
- 50,000 credits per month
- Everything in Build, plus unlimited HTTP APIs and bring-your-own keys
- Custom rate limits, turbo queue and dedicated infrastructure
- Unlimited team members, 10 workspace editors
- 50 simultaneous requests, priority queue tier, 100MB CSV uploads
- Custom credits per month
- Everything in Scale, plus personal support
- Schedulers that run every minute, hour, or day
- Access to the Databar SDK
- Unlimited workspace editors, simultaneous requests, batch enrichments, and CSV uploads
Databar meters results, not attempts: the waterfall tries providers in sequence and only spends a credit when one of them actually returns data. Build gets a team to 5,000 credits a month for $99, but caps custom HTTP APIs at 5, bring-your-own-key providers, 3 workspace editors, and 5 simultaneous requests. Scale, at $495/month billed monthly (annual knocks 12% off), removes the API cap entirely, adds a turbo queue and dedicated infrastructure, and raises editors to 10 and concurrent requests to 50, which is the real reason it carries Databar's own 'Most popular' tag. Enterprise drops the credit ceiling and per-editor limits altogether but requires a custom quote, with no dollar figure published. The 100-credit trial is small: enough to test a workflow, not to run a real campaign.
Choose it, or skip it.
Choose it if
- GTM teams and agencies running enrichment waterfalls across many data providers who want one credit pool and one bill instead of a dozen point subscriptions.
- Teams that want an AI research fallback for the gaps none of the 160+ connected providers can fill, returned with source citations instead of a blank cell.
- Builders who want an enriched table to double as a spreadsheet, an API endpoint, and an MCP server without a separate export step.
- Teams syncing into HubSpot, Pipedrive, or Attio who want enrichment to write back automatically instead of round-tripping a CSV.
Skip it if
- Small teams that want to run a real campaign on the free trial; 100 credits per workspace covers a test, not ongoing use.
- Teams that need more than 5 custom HTTP APIs or unlimited bring-your-own-key providers without moving up to the $495/month Scale tier.
- Shops that need a purpose-built workflow and table-orchestration tool for multi-step pipelines rather than a broad provider aggregator.
If not Databar.ai, then what.
- ClayFree tier (100 credits/mo) + paid plans from $167/mo (Launch); enterprise custom quote
Pick this for deeper table orchestration and a larger base of published enrichment waterfalls, if a specific pipeline needs more workflow depth than Databar's builder covers.
- ApifyFree tier ($5/mo usage credit) then paid plans from $29/mo (Starter), usage-based overage billing
Pick this when the job is custom web scraping at scale, actors, proxies, and scheduled runs, rather than a broad enrichment-provider aggregator.
- PhantomBusterfrom ~$56-69/mo (Starter plan), 14-day free trial, a limited free tier (2h execution, 5 slots, capped credits); annual billing gets ~20% off
Pick this for LinkedIn-specific scraping and automation, a specialty Databar's 160-provider network doesn't focus on.
Asked in real client work.
How much does Databar cost?
Build starts at $99/month for 5,000 credits. Scale is $495/month for 50,000 credits (annual billing saves 12%), and Enterprise is a custom quote with custom credits. A 100-credit trial is available per workspace before any of that.
What actually forces an upgrade from Build to Scale?
Build caps custom HTTP APIs at 5 and workspace editors at 3, with standard queue priority. Scale removes the API cap entirely, adds a turbo queue and dedicated infrastructure, and raises editors to 10 and simultaneous requests to 50, which is why Databar marks it 'Most popular.'
Do failed enrichment attempts cost credits?
No. The waterfall tries data providers in sequence and only spends a credit when one of them actually returns a result, per Databar's own pricing page.
Can an AI agent pull data from Databar directly?
Yes. Every table can act as an API endpoint or an MCP server connection, and Databar publishes API docs at docs.databar.ai, so an agent can call a table or trigger a workflow without going through the UI.
What happens when none of the 160+ providers has the answer?
The AI Research Agent searches the open web itself and returns a structured result with source citations instead of leaving the field blank.