Fiber is our first pick for teams building agents that find companies, enrich people, and turn the results into a repeatable workflow. We like its API and MCP access and the attention it gives to estimating and tracking spend. People Data Labs is worth considering for direct data access, while Clay suits teams that want to combine providers and customize the process.
The best choice depends on your market and what you intend to build with the data. A contact database for a sales team and a data service embedded in a customer-facing product have different requirements. These are our preferences by use case, with pricing checked in September 2026.
Our shortlist
| Rank | Provider | Where we’d use it | What to evaluate |
|---|---|---|---|
| 1 | Fiber | Agent-driven search and enrichment | Coverage, operation costs, and spend controls |
| 2 | Clay | Combining providers into custom workflows | Data credits, actions, and workflow ownership |
| 3 | People Data Labs | Embedding person data in a product | Match quality, fields, and permitted usage |
| 4 | Explorium | Business data for agents and GTM workflows | Source coverage and access model |
| 5 | ZoomInfo | Enterprise account intelligence | Segment coverage and contracted API access |
| 6 | Cognism | Phone-led prospecting and European coverage | Contact verification and target-country coverage |
| 7 | Apollo | Data alongside sales execution | Credit allowances and product integration fit |
| 8 | Coresignal | Company, employee, and jobs datasets | Update cadence and historical coverage |
| 9 | Freckle | Reusable enrichment workflows across providers | Provider coverage, output costs, and integration access |
1. Fiber: our pick for agent-driven enrichment
Fiber exposes discovery, enrichment, contact reveal, and audience workflows through API and MCP interfaces. Its product includes natural-language search and live profile fetching. We like having those operations available in one place when the application needs to progress from a company search to a usable contact list.
The useful design detail is cost visibility. Fiber describes previews for audience enrichment and per-call charge metadata. That gives an application information it can use to enforce a budget. The application still needs to implement its own approval and spending policy; access through MCP does not make every call automatically safe or free.
Fiber’s published pricing confirms these standard API plans:
| Plan | Monthly billing | Included credits |
|---|---|---|
| Prospector | $300/month | 15,000/month |
| Growth | $900/month | 50,000/month |
| Enterprise | $2,400/month | 150,000/month |
Annual billing has lower monthly equivalents. The seven-day self-serve trial requires a payment method and converts to the selected paid plan unless canceled. Search, email reveal, and phone reveal consume credits differently, so 15,000 credits should not be advertised as a fixed number of enriched contacts. Bulk search is a separate product with separate pricing.
Our take: Fiber is a good place to start for a product team that wants programmable enrichment and explicit cost accounting. Test it on the actual geography, roles, and company sizes your customers care about.
2. Clay: custom workflows across providers
Clay combines enrichment providers, research, and workflow automation. We would choose it when the process itself needs frequent experimentation: trying a second source after a failed match, applying custom scoring, or changing how research feeds a campaign.
Its pricing separates data credits and actions. It also includes email campaign capabilities, so describing it as enrichment alone would undersell the product. The practical question is who will own and maintain your workflows as they grow.
3. People Data Labs: direct access to person data
People Data Labs belongs on the shortlist when your application needs person data as an input to its own logic. We’d compare the fields returned, match confidence, and cost at the volume you expect.
Headline record counts won’t tell you whether it can resolve the incomplete identities in your signup flow. Use a sample with the same missing fields and ambiguous names your production traffic contains.
4. Explorium: business data for agent workflows
Explorium positions its data around AI agents and GTM use cases. We’d consider it when a workflow needs several kinds of business information rather than contact details alone.
For a customer-facing product, ask specifically about embedding, exports, and redistribution in the proposed agreement. An available API or MCP integration does not by itself establish what you can deliver to end customers.
5. ZoomInfo: account intelligence in an enterprise stack
ZoomInfo combines business data with account intelligence and sales workflows. It is worth evaluating when those capabilities need to work together inside an established revenue organization.
We would ask for a quote tied to the required API access and usage. Unattributed annual-price estimates are a poor basis for comparing an enterprise agreement with a self-serve API plan.
6. Cognism: contact verification and European coverage
Cognism emphasizes phone-verified contact data and European coverage. We’d include it when phone contactability is a major requirement, then test performance in each target country.
The evaluation should include how the provider identifies restricted contacts and updates contact records. Broad compliance language should not replace a review of how your particular workflow will use the data.
7. Apollo: data and sales execution together
Apollo combines data, enrichment, outreach, and workflow automation, and its site also lists MCP access. We like it for teams that want people to prospect and act on the data in the same application.
For embedded use, compare the actual API allowances and permitted usage against a dedicated data service. The application bundle may be useful, but its value depends on whether your team will use it.
8. Coresignal: company, employee, and jobs data
Coresignal is worth considering for market analysis, recruiting products, and company monitoring. We’d put particular emphasis on the available history and refresh behavior when a feature depends on changes over time.
A current employee count and a historical headcount series support different product features. Ask for a sample that demonstrates the exact time dimension you need.
9. Freckle: reusable enrichment workflows
Freckle combines waterfall enrichment, signal monitoring, and reusable GTM workflows, with a CLI aimed at teams working through coding agents. We’d consider it when the job involves coordinating several data providers and reusing the same enrichment process across lists or customers.
Its pricing page lists access to 40+ data providers, a free plan with 250 credits, and a Build plan starting at $99/month that includes HTTP API access and bring-your-own API keys. We would compare the cost per usable output and check the integration features required by the workflow. Like Clay, Freckle belongs here as an orchestration option rather than a single underlying dataset.
How we’d evaluate the data
Run the same representative records through a few candidates. Measure identity match accuracy, field completeness, age of the source data, and cost per usable result. For outreach, also measure deliverability through an appropriate pilot. A returned email address and a useful contact are different outcomes.
MCP can make a provider easier to try with an agent client, but it doesn’t eliminate the work of authentication, permissions, identity resolution, or retries. Our AI SDK and MCP overview describes how we approach agent access to customer systems.
Decide which system owns each field before writing enrichment back. A third-party job title should not silently overwrite a customer’s curated record. See our guide to nested object and field mapping for the integration considerations.
Finally, separate enrichment from customer truth. A data vendor can help identify an account; your customer’s CRM and billing systems tell you about its existing relationship. That distinction is why we connect revenue and billing context to agents.