databar.ai

# Databar

> Databar (databar.ai) is the data layer for go-to-market teams and AI agents. It connects 160+ integrations, including 100+ data providers, in one workspace: search for companies, people and jobs, enrich records with emails, phones, firmographics, technographics and signals, run waterfall enrichment across several providers, and research the web with AI. It is also the place where that data is centralized and automated: tables hold data from any source, flows chain enrichments with branching and cost previews, automations run on schedules, triggers and conditions, and exporters write the results back to your CRM, spreadsheet or outreach tool. Everything is available in a spreadsheet-style app, a REST API, a Python SDK, a CLI, a hosted MCP server, a native n8n node, a native Attio app and a Chrome extension. Pricing is credit-based and you only pay for requests that return data.

## About Databar

Databar was founded in 2021 by David Abaev. It is bootstrapped and has grown on customer revenue rather than outside funding, which keeps the roadmap tied to what customers ask for. It replaces a stack of separate data subscriptions with one subscription and one set of credentials. Two numbers describe the catalog and they mean different things:

- 160+ integrations: everything Databar can connect to, including data providers, CRMs, outreach sequencers, spreadsheets, AI models, web scrapers, webhooks and your own HTTP APIs
- 100+ data providers: the subset of integrations that return enrichment data, such as contact finders, email verifiers, company and people databases, intent and signal sources, technographics, scrapers and AI models

Every data provider can be used with Databar's own credentials, so an agent or a user never needs an account with the provider. Requests are paid in credits, and a request that returns no data or errors is not charged. For providers that support it, you can also bring your own API key; requests made with your own key cost 0 credits.

Proof points: 1,000+ customers, rated 5.0 on G2 and 5 out of 5 on Product Hunt, 160+ integrations, 100+ data provider partners. Published customer results include OnHires cutting research time by 80%, Shary lifting reply rates 3.1x, and Pryzma automating two lead-generation roles; the [case studies](https://databar.ai/company/our-customers) have the details.

Security and data handling: a SOC 2 audit is in progress as of September 2026. Data handling, retention and GDPR terms are described in the [privacy policy](https://databar.ai/privacy-policy).

Support: info@databar.ai. Sign up: https://databar.ai/registration (14-day full-product trial with 100 credits; no data, no charge).

## Main site

- [Home](https://databar.ai): Platform overview, use cases and key features
- [Integrations catalog](https://databar.ai/integrations): Browse the 160+ integrations and 100+ data providers, with the enrichments each one offers
- [Templates](https://databar.ai/explore/templates): Pre-built tables and workflows for common GTM use cases
- [Pricing](https://databar.ai/pricing): Plans, credits, billing rules and FAQ, summarized below
- [Blog: The Column](https://databar.ai/blog): Guides, playbooks, comparisons and product announcements
- [Sign up](https://databar.ai/registration): Start the 14-day trial
- [About Databar](https://databar.ai/company/about-us): Team, story and values
- [Customers](https://databar.ai/company/our-customers): Case studies and results
- [Privacy policy](https://databar.ai/privacy-policy): Data handling and GDPR
- [Terms of service](https://databar.ai/terms-of-service): Service terms

## Beyond data: centralize and automate

Databar is not only a place to buy data. It is where the data lives and where the work runs.

- Centralize: a table is the working record; rows arrive from CSV imports, CRM syncs, incoming webhooks, web scrapers, prospecting searches or an agent, and every enrichment adds columns to the same row, so one record carries the company, the contacts, the signals and the history of what was run
- Enrich in sequence: waterfalls try several providers per field in the order you set and stop at the first verified result, so coverage goes up and misses cost nothing
- Automate: flows chain enrichments on a visual canvas, branch on results, and show the cost before a run; automations run on a schedule, on triggers such as a new or changed record or an incoming webhook, and only on rows that pass your run conditions
- Monitor: scheduled reruns track changes such as job moves, funding, hiring, tech-stack changes and website updates, so lists and CRM records stay current instead of decaying
- Act: exporters write enriched fields back to your CRM with two-way sync, push contacts to sequencers, update spreadsheets or call any endpoint through custom exporters, and every run keeps a log of what was written
- Extend: any HTTP API can be added as a custom enrichment or a custom exporter at no credit cost, and incoming webhooks accept rows from any tool that can make a request
- Operate from anywhere: the same tables, waterfalls and flows are driven from the app, the REST API, the Python SDK, the CLI, the hosted MCP server, a native n8n node, a native Attio app and the Chrome extension

## Product

- [Waterfall enrichments](https://databar.ai/product/waterfall-enrichments): Try several providers in sequence per field and stop at the first verified result, in the provider order you set; typically up to 7x more verified contacts than any single provider, depending on your list
- [Flows](https://databar.ai/product/flows): Visual canvas for multi-step pipelines that chain enrichments, branch on results, preview the cost before running, and run on tables, schedules, signals or via API, with a full log per run
- [Databar MCP server](https://databar.ai/product/databar-mcp): Hosted MCP server that lets Claude, Cursor or any MCP client build tables, run enrichments and waterfalls, and push to your CRM
- [API, CLI and SDK](https://databar.ai/product/api-cli): One REST API, a CLI and a Python SDK for every enrichment in the catalog
- [AI Research Agent](https://databar.ai/product/ai-research-agent): Describe what you need in plain English and the agent reads the live web and returns structured, cited columns for thousands of companies or people
- [Chrome extension](https://databar.ai/product/chrome-extension): Free forever; collect data from any page into a Databar table in one click, import ranges from Google Sheets, then enrich and export from Databar (overview below)

## Solutions

- [CRM enrichment](https://databar.ai/solutions/crm-enrichment): Keep HubSpot, Salesforce, Pipedrive and Attio records complete and fresh on a schedule, with controlled two-way sync and full logs
- [For GTM engineers](https://databar.ai/solutions/gtm-engineers): Tables, waterfalls, flows and an API in one engine, with per-step costs and run logs
- [For RevOps](https://databar.ai/solutions/revops): Enrich, dedupe and refresh CRM records automatically, with an audit trail for every field written
- [For agencies](https://databar.ai/solutions/agencies): Build a playbook once and run it per client in separate workspaces
- [Inbound enrichment](https://databar.ai/solutions/inbound-enrichment): Enrich every signup or form fill in real time, score it against your ICP and route it
- [Outbound](https://databar.ai/solutions/outbound): Verified emails and phones before you send, buying signals in every row, and AI personalization at scale

## Chrome extension

A free browser extension, on every plan and with no card to install: open any list-style page, click once, and the rows land in a Databar table ready to enrich, export or sync. It also imports ranges copied from Google Sheets. Collecting costs no credits; only the enrichments you run afterwards do.

- [Chrome extension](https://databar.ai/product/chrome-extension): How it works and the questions people ask before installing

## Quickstart for agents

Four ways to run the first enrichment. All of them use the same catalog and the same credits. The example below is real and was run before publishing: enrichment 10 is "Verify emails with Emailable", it takes one parameter named email, and it costs 1 credit per address checked. Replace alice@example.com with the address you want to verify; the example address itself returns a completed task with state undeliverable.

MCP: connect https://mcp.databar.ai/mcp through OAuth (Claude Connectors, Cursor, or any remote MCP client). Then search the catalog with search_enrichments, read the required parameters and the credit price with get_enrichment_details, run it with run_enrichment, and poll the returned task until it completes. Bulk, waterfall and table tools follow the same pattern.

REST:

```
curl https://api.databar.ai/v1/user/me -H "x-apikey: YOUR_API_KEY"
curl "https://api.databar.ai/v1/enrichments/?q=verify" -H "x-apikey: YOUR_API_KEY"
curl https://api.databar.ai/v1/enrichments/10 -H "x-apikey: YOUR_API_KEY"
curl -X POST https://api.databar.ai/v1/enrichments/10/run -H "x-apikey: YOUR_API_KEY" -H "Content-Type: application/json" -d '{"params": {"email": "alice@example.com"}}'
curl https://api.databar.ai/v1/tasks/TASK_ID -H "x-apikey: YOUR_API_KEY"
```

The run call returns a task id and status "processing"; the task call returns status "completed" with the result under data, plus credits_spent.

Python SDK:

```
pip install databar
export DATABAR_API_KEY=your-key-here
python -c "from databar import DatabarClient; c = DatabarClient(); print(c.run_enrichment_sync(10, {'email': 'alice@example.com'}))"
```

CLI:

```
pip install databar
databar login --api-key your-key-here
databar enrich list --query "verify"
databar enrich get 10
databar enrich run 10 --params '{"email": "alice@example.com"}'
```

Rules that keep runs cheap and correct:

- Read the enrichment details before running; every enrichment lists its credit price and required inputs
- Run a handful of rows first and check the output before processing a full list
- A request that returns no data or an error is not charged, so gaps in coverage cost nothing
- Verify emails before pushing contacts to a sequencer
- Where you already have a provider key, add it to Databar and the request costs 0 credits

## Documentation

Full product docs live at [docs.databar.ai](https://docs.databar.ai). Every docs page is also available as Markdown by appending .md to its URL, as in the links below.

### Getting started

- [What is Databar?](https://docs.databar.ai/product-guide/what-is-databar.md): Platform overview covering the UI, API, SDK, CLI and MCP
- [REST API quickstart](https://docs.databar.ai/quickstart-rest.md): Make your first API call in under 5 minutes
- [Authorization and API keys](https://docs.databar.ai/product-guide/authorization.md): How authentication works for Databar and for data providers, including bring-your-own keys
- [Credits and billing](https://docs.databar.ai/product-guide/credits-and-billing.md): How credits work, rollover, add-ons and what is not charged

### Core product features

- [Tables](https://docs.databar.ai/product-guide/tables-overview.md): Create, configure and work with spreadsheet-style tables
- [Enrichments](https://docs.databar.ai/product-guide/enrichments.md): Populate tables with data from third-party providers
- [Waterfalls](https://docs.databar.ai/product-guide/waterfalls.md): Chain multiple providers with automatic fallback for maximum coverage
- [AI Researcher](https://docs.databar.ai/product-guide/ai-researcher.md): AI agents that research and enrich data from across the web
- [AI prompts](https://docs.databar.ai/product-guide/ai-prompts.md): Generate and reuse AI prompt templates across your workspace
- [Automations](https://docs.databar.ai/product-guide/automations.md): Schedule and automate enrichment runs
- [Run conditions](https://docs.databar.ai/product-guide/run-conditions.md): Control which rows get enriched with conditional logic
- [Exporters](https://docs.databar.ai/product-guide/exporters.md): Push data to CRMs and other destinations
- [Webhooks](https://docs.databar.ai/product-guide/webhooks.md): Send data into Databar tables from any external service
- [Import data](https://docs.databar.ai/product-guide/import-data.md): Bring data in via CSV, integrations or webhooks
- [Custom HTTP APIs](https://docs.databar.ai/product-guide/custom-apis.md): Add your own REST APIs as custom enrichments or exporters, at no credit cost
- [Chrome extension](https://docs.databar.ai/product-guide/chrome-extension.md): Collect data from any website directly into Databar
- [n8n integration](https://docs.databar.ai/product-guide/n8n-integration.md): Use the native Databar node inside n8n workflows
- [Columns](https://docs.databar.ai/product-guide/columns.md): Column types, management and grouping
- [Excel formulas](https://docs.databar.ai/product-guide/formulas.md): Use familiar spreadsheet formulas in Databar tables
- [JQ formulas](https://docs.databar.ai/product-guide/jq-formulas.md): Parse, filter and manipulate JSON fields
- [JSON expander](https://docs.databar.ai/product-guide/json-expander.md): Extract values from JSON columns into separate columns
- [Table lookup](https://docs.databar.ai/product-guide/table-lookup.md): Pull matching values from another table, like VLOOKUP
- [Merge columns](https://docs.databar.ai/product-guide/merge-columns.md): Combine columns with fallback logic
- [Deduplication](https://docs.databar.ai/product-guide/deduplication.md): Remove duplicate rows manually or automatically
- [Debug requests](https://docs.databar.ai/product-guide/debug-requests.md): Understand enrichment statuses and troubleshoot errors
- [Invite your team](https://docs.databar.ai/product-guide/invite-your-team.md): Add team members to collaborate on tables and workflows
- [Workspace settings](https://docs.databar.ai/product-guide/workspace-settings.md): Configure workspace and preferences

## MCP server for AI agents

The hosted server is at https://mcp.databar.ai/mcp and connects through OAuth: in Claude use the Connectors UI, and in Cursor or other MCP clients add the URL as a remote MCP server. It exposes 60+ tools across enrichments, waterfalls, tables, rows, exporters, folders and account balance, plus agent skills that teach an assistant complete workflows.

- [MCP server overview](https://docs.databar.ai/mcp-server.md): Connect AI assistants to Databar's enrichment API
- [MCP configuration](https://docs.databar.ai/mcp-configuration.md): Safe mode, caching and data retention
- [Available MCP tools](https://docs.databar.ai/mcp-tools.md): The complete tool list with parameters
- [Agent skills](https://docs.databar.ai/mcp-skills.md): Pre-built workflow skills for agents
- [Skill: single enrichment](https://docs.databar.ai/mcp-skill-enrichment.md): Look up a person, company, email or phone number
- [Skill: bulk enrichment](https://docs.databar.ai/mcp-skill-bulk.md): Enrich up to 100 records in one operation
- [Skill: waterfall enrichment](https://docs.databar.ai/mcp-skill-waterfall.md): Try multiple providers in sequence via MCP
- [Skill: table-driven enrichment](https://docs.databar.ai/mcp-skill-table.md): Create a table, insert rows, run enrichment and get a shareable link

## Developer tools

The REST API base URL is https://api.databar.ai/v1 and requests authenticate with an API key in the x-apikey header. Enrichment runs are asynchronous: a run returns a task id, and you poll the task for the result.

- [Developer guide](https://docs.databar.ai/developer-guides.md): Enrich, transform and manage data via API, SDK, CLI or MCP
- [REST API reference](https://docs.databar.ai/api-reference/introduction.md): Full endpoint reference
- [OpenAPI specification](https://docs.databar.ai/api-reference/openapi.json): Machine-readable spec
- [Python SDK](https://docs.databar.ai/python-sdk.md): Install with pip install databar, then enrich from Python
- [CLI reference](https://docs.databar.ai/cli.md): Run enrichments, manage tables and automate workflows from the terminal

### Developer guides

- [Enrich a list of leads](https://docs.databar.ai/guides/enrich-leads.md): Company data, emails and phone numbers for a batch of leads
- [Table enrichment pipeline](https://docs.databar.ai/guides/table-enrichment-pipeline.md): Create a table, add rows, attach an enrichment and run it via API
- [Waterfall email finder](https://docs.databar.ai/guides/waterfall-email-finder.md): Verified emails by trying multiple providers with automatic fallback

## Key use cases

- Email enrichment: find verified work emails for a list of contacts with a waterfall across Hunter.io, Findymail, LeadMagic, Prospeo, Upcell and others, then verify with Emailable or Bouncer
- Phone enrichment: find mobile and direct-dial numbers from LinkedIn URLs or name plus company, with a waterfall across several phone providers
- CRM enrichment: scheduled jobs that keep HubSpot, Salesforce, Pipedrive or Attio records complete with fresh firmographics, job titles and contact data, written back through two-way sync
- List building from scratch: build prospect lists from intent signals such as job postings, funding events, tech stack, recent hires and website traffic
- Outbound personalization: pull recent news, LinkedIn activity or company milestones per contact to generate personalized outreach at scale
- Inbound lead scoring: research and score new signups or call bookings automatically with AI enrichment and run conditions, then route them
- AI agent tooling: use Databar as the data layer inside Claude, ChatGPT, Cursor or any MCP client to look up companies, find emails and run waterfalls; OpenAI, Anthropic, Google Gemini and Perplexity models are also available as enrichments inside tables
- Tech stack targeting: identify companies using specific technologies via BuiltWith, Wappalyzer or TheirStack and build targeted lists
- Lookalike prospecting: find companies similar to your best customers with Ocean.io or Diffbot company profiles
- Web research and scraping: run the AI Researcher, Firecrawl, Serper or Databar's own scrapers to turn any page or search into structured columns
- Bring your own data and APIs: send rows into a table through an incoming webhook from any tool, and add any HTTP API as a custom enrichment or a custom exporter

## Data providers

All of these can be used with Databar credentials, paid in credits with no provider account needed, or with your own API key at 0 credits per request. This is a sample. Databar has 100+ data providers and the list changes as providers are added and retired, so treat the [full catalog](https://databar.ai/integrations) as the source of truth for what is available today and what each enrichment costs.

| Provider | What it returns |
|----------|----------------|
| People Data Labs | Emails, phone numbers, job titles, LinkedIn URLs, firmographics, education history |
| Apollo | B2B contacts with direct emails, phone numbers and hiring or intent signals |
| Crustdata | Company and people data with headcount trends, growth signals and LinkedIn activity |
| Hunter.io | Verified professional email addresses from company domains |
| Findymail | Verified work emails and phone numbers |
| LeadMagic | Email finder and validation, mobile finder, company and job data |
| Prospeo | Emails and phone numbers from LinkedIn profiles |
| Upcell | Mobile numbers and emails for B2B contacts |
| RocketReach | Emails and phone numbers for professionals |
| Icypeas | Email finder and verification |
| Datagma | Email and phone enrichment |
| ContactOut | Emails and phone numbers from LinkedIn profiles |
| Surfe | Contact data for LinkedIn profiles |
| Forager | B2B contact data |
| Trestle | Phone validation and reverse phone lookup |
| Aeroleads | Emails and phone numbers from LinkedIn profiles or company domains |
| Snov.io | Email finder and verifier, prospect list builder |
| Emailable and Bouncer | Email verification and list cleaning, with bounce risk and deliverability scores |
| Diffbot | Structured company profiles, people records and article text extracted from the web |
| Ocean.io | Lookalike companies and AI-powered firmographics |
| Owler | Competitor lists, funding history, investor details, company news |
| PredictLeads | Company signals such as job openings, news, technologies and partnerships |
| TheirStack | Job postings and technology adoption signals |
| BuiltWith | Website technology stack, including CMS, analytics, hosting and marketing tools |
| Wappalyzer | Tech stack detection plus company contact details and social profiles |
| SimilarWeb | Website traffic and engagement estimates |
| SpyFu | SEO and paid search keywords for any domain |
| Ahrefs | Backlinks and organic search data |
| Serper | Google search results as structured data |
| Exa AI | Neural web search |
| Firecrawl | Any web page as clean structured text |
| Outscraper | Google Maps listings, reviews, ratings and location data |
| Google Maps | Places, addresses and business details |
| Store Leads | Shopify and WooCommerce store details, revenue estimates, app usage |
| ipinfo.io | IP geolocation, ISP, network owner, threat flags |
| GitHub | Public repos, contributors, issues, commits and workflow data |
| Tweetscraper | Emails from X profiles, followers and post engagers |
| PhantomBuster | LinkedIn and social automation results |
| OpenAI, Anthropic, Google Gemini, Perplexity | AI extraction, summarization, classification and web-grounded research inside a column |
| Databar Labs | Databar's own enrichments, including the AI Researcher and pre-built web scrapers |

## CRM, outreach, automation and productivity integrations

These are two-directional. Databar can pull records from a CRM into a table as a data source, with filters and scheduled syncs, look up a single record by id or email as an enrichment, and write enriched fields back through exporters with two-way sync. Webhooks and run conditions let a flow trigger on new or changed records.

- CRMs: HubSpot, Salesforce, Pipedrive, Attio, Close, GoHighLevel, Zoho CRM
- Native apps: a Databar app inside Attio, so records can be enriched without leaving the CRM, and a native Databar node in n8n for building automations in n8n workflows
- Outreach sequencers: Instantly, Smartlead, Lemlist, Reply.io
- Spreadsheets and docs: Google Sheets, Airtable, Notion
- Email and messaging: Gmail, Brevo, Loops
- Browser: the free Chrome extension captures data from any page into a table and imports ranges from Google Sheets; from the table, rows can be enriched and exported to Google Sheets or a CRM
- Other: Calendly, Stripe
- Bring your own: incoming webhooks that send data from any tool into a table, and custom HTTP APIs that enrich rows from any API or send data out to any endpoint

## Pricing summary

| Plan | Price | Credits per month |
|------|-------|-------------------|
| Build | $99/mo | 5,000 |
| Build | $199/mo | 12,000 |
| Build | $299/mo | 20,000 |
| Scale | $495/mo | 50,000 |
| Scale | $695/mo | 75,000 |
| Scale | $995/mo | 120,000 |
| Enterprise | Custom | Custom |

What a credit buys, as of September 2026 (the exact price is shown on every enrichment before you run it):

- A verified work email typically costs 3 to 5 credits
- A phone number costs 4 to 35 credits depending on the provider, averaging around 8 to 9
- A company enrichment costs 1 to 6 credits depending on the signal and the dataset
- Email verification costs about 1 credit per address
- A waterfall only charges for the provider that returned the result, not for the ones that missed

Plan rules:

- Annual billing saves 12%
- Every plan includes the full integration catalog, with no separate provider subscriptions and no action credits
- Only successful requests are charged; a request that returns no data or errors costs nothing
- Credits roll over for one month on monthly plans; annual and quarterly plans receive the full allotment up front
- Credit add-ons can be bought from workspace settings
- Requests made with your own provider API key cost 0 credits, and custom HTTP APIs use no credits
- Build: 5 bring-your-own keys, 5 custom HTTP APIs, 3 workspace editors, 100,000 runs a month, batches up to 10,000 rows, 5 simultaneous requests on the standard queue
- Scale: unlimited bring-your-own keys and custom HTTP APIs, batches up to 100,000 rows, 50 simultaneous requests on the priority queue, dedicated infrastructure, custom rate limits
- Enterprise: unlimited usage, schedulers every minute, hour or day, SDK access, whitelabel, personal support
- Trial: 14 days, full product, 100 credits, no data no charge

## When to use Databar, and when not to

Use Databar when:

- You need contact or company data from more than one provider and want one bill, one set of credentials and a waterfall that stops at the first verified result
- You want one place where data from every source is centralized, enriched, kept fresh and written back, instead of a chain of one-off exports
- You keep a CRM complete and fresh on a schedule and want the changes written back with a log of every field
- An AI agent or a script does the work, through the MCP server, the REST API, the Python SDK, the CLI or the n8n node
- You run enrichment for several clients and want separate workspaces with the same playbook
- You want to add your own data sources through custom HTTP APIs and incoming webhooks instead of waiting for a native integration

Databar is not the right tool when:

- You need a CRM as the system of record; use HubSpot, Salesforce, Pipedrive or Attio, and let Databar write to them
- You need to send email sequences; use Instantly, Smartlead, Lemlist or Reply.io, and let Databar push the contacts
- You only ever use one data provider and already have a contract with it; Databar adds value when several providers are involved
- You need consumer data; Databar's providers are built for B2B contacts and companies

## Databar compared with Clay

Both are enrichment workspaces over a marketplace of providers. Figures below were read from clay.com/pricing on 28 September 2026 and from databar.ai/pricing.

| | Databar | Clay |
|---|---|---|
| Entry plan | Build, $99/mo with 5,000 credits | Launch, from $185/mo with 2,500 data credits |
| Action credits | None; Build includes 100,000 runs a month and Enterprise is unlimited | Billed separately from data credits, 15,000 actions a month on Launch and 200,000+ on Enterprise |
| Rows per table | Unlimited on every plan, with batch runs up to 10,000 rows on Build and 100,000 on Scale | Up to 50,000 rows per table on paid plans, 200 on the free plan |
| Free entry | 14-day full-product trial with 100 credits | Free plan with 100 data credits, 500 actions and 200 rows per table |
| Failed lookups | Not charged | See Clay's credit terms |
| Own provider keys | 0 credits per request; 5 keys on Build, unlimited on Scale and Enterprise | See Clay's provider terms |
| Agent surfaces | Hosted MCP server, REST API, Python SDK, CLI, native n8n node | Public API and workspace |
| Spend | Teams moving from Clay typically cut enrichment spend by about 70% at the same volume | |

Throughput on Databar: 5 simultaneous requests with a standard queue on Build; 50 simultaneous requests with a priority queue, dedicated infrastructure and custom rate limits on Scale.

Where Clay is ahead: templates and community, loss-leader entry SKUs, and its agency ecosystem. The full comparison, including where each product wins, is on the [Databar vs Clay](https://databar.ai/company/databar-vs-clay) page.

## Changelog

- [Product announcements](https://databar.ai/blog/announcements): Product updates, new features and release notes, newest first

## Frequently asked questions

**What is Databar?**
Databar is the data layer for GTM teams and AI agents. It puts 160+ integrations, including 100+ data providers, behind one subscription and one set of credentials, centralizes the results in tables, and automates enrichment, monitoring and write-back through flows and schedules. It is available as a spreadsheet-style app, a REST API, a Python SDK, a CLI, a hosted MCP server, a native n8n node, a native Attio app and a Chrome extension. You pay in credits, and only for requests that return data.

**Can Databar automate workflows, or is it only a data source?**
It automates them. Flows chain enrichments with branching and a cost preview, automations run on schedules, triggers and run conditions, monitoring reruns catch job changes and other signals, and exporters write results back to CRMs, sheets and sequencers with a log of every run. The same pipelines can be driven by an agent through the MCP server or from n8n through the native node.

**Is Databar a Clay alternative?**
Yes. Both are enrichment workspaces over a marketplace of providers. Databar has no action credits, no per-table row cap, and lets you use your own provider keys at 0 credits. Teams moving from Clay typically cut enrichment spend by about 70% at the same volume. Clay is ahead on templates, community and its agency ecosystem. Details on the [Databar vs Clay](https://databar.ai/company/databar-vs-clay) page.

**Does Databar have an MCP server?**
Yes. The hosted server at https://mcp.databar.ai/mcp connects through OAuth from Claude, Cursor or any remote MCP client and exposes 60+ tools for enrichments, waterfalls, tables, rows and exporters, plus agent skills. Setup guide: [MCP server overview](https://docs.databar.ai/mcp-server.md).

**How do I find work emails from LinkedIn URLs with Databar?**
Add the LinkedIn URLs to a table and run an email waterfall, or call the same waterfall through the MCP server, the API, the SDK or the CLI. The waterfall tries providers such as Findymail, LeadMagic, Prospeo, Upcell and Hunter.io in the order you set and stops at the first verified result. Guide: [Waterfall email finder](https://docs.databar.ai/guides/waterfall-email-finder.md).

**What does Databar cost?**
Build starts at $99 a month with 5,000 credits and Scale at $495 a month with 50,000 credits, with larger credit tiers on each plan and custom Enterprise plans. A verified email typically costs 3 to 5 credits, a phone number 4 to 35, a company enrichment 1 to 6. Annual billing saves 12%. A request that returns no data or an error is not charged. There is a 14-day full-product trial with 100 credits. Details on the [pricing page](https://databar.ai/pricing).

**Can I use my own API keys for data providers?**
Yes. For providers that support it, add your own key and each request costs 0 credits. Build includes 5 keys; Scale and Enterprise are unlimited. Any other HTTP API can be added as a custom enrichment or exporter at no credit cost.

**Does Databar sync with my CRM?**
Yes. HubSpot, Salesforce, Pipedrive, Attio, Close, GoHighLevel and Zoho CRM can be used as data sources, as lookups and as export targets, with two-way sync and a log of every field written. Attio users also get a native Databar app inside Attio. Details on the [CRM enrichment](https://databar.ai/solutions/crm-enrichment) page.

**How is Databar different from buying one data provider directly?**
One provider covers a fraction of any list. Databar runs a waterfall across several providers, stops at the first verified result, and charges only for hits, so coverage goes up and the cost of misses goes to zero. A single provider still makes sense if you only need that provider's data and already have a contract with it.

**Is Databar secure and GDPR compliant?**
Data handling, retention and GDPR terms are described in the [privacy policy](https://databar.ai/privacy-policy). A SOC 2 audit is in progress as of September 2026. Security questions: info@databar.ai.

## Optional

Workflow guides from the blog, for readers who want worked examples after the product and docs pages above:

- [MCP vs SDK vs API for GTM workflows](https://databar.ai/blog/article/mcp-vs-sdk-vs-api-when-to-use-which-for-gtm-workflows): When to use which Databar surface
- [Claude Code for GTM teams](https://databar.ai/blog/article/claude-code-for-gtm-teams-the-2026-guide): Agentic GTM workflows built on Databar
- [Python SDK quickstart](https://databar.ai/blog/article/python-sdk-enrichment-developer-quickstart-guide): Enrich from Python, end to end