ZoomInfo API Alternative: Pay-per-Lookup B2B Data via API and MCP

If you are looking for a ZoomInfo API alternative, first decide whether you want to replace a platform or a data feed. ZoomInfo is publicly marketed as a go-to-market platform with a workspace on top of its data. NeuralVerge is a set of data sources, reachable over REST or MCP, where you pay per lookup and get structured JSON into your own system. This article compares the two models and says where each wins. ZoomInfo is described only in general terms: prices, contract terms, limits and coverage are not stated here, so check them with ZoomInfo directly.

The short version

• —Verdict: NeuralVerge can replace the data-feed part of a platform, which is searching for people, resolving a work email and researching the company, when a pipeline or an agent is the caller. It does not replace a rep-facing workspace, saved lists or a stored database you browse.
• —Who it suits: teams building enrichment into a service, teams handing first-pass prospecting to an AI agent, and teams whose volume is uneven. Sales teams that work inside a database every day are better served by a platform.
• —What it costs: LinkedIn people search is 100 points per 25 results ($0.10 per block of 25), plus 5 points per profile in full mode, and a search that matches nothing is not billed. Email finder is 10 points per lookup ($10 per 1,000), and misses are not billed. Email validation is 1 point per check ($1 per 1,000).
• —What to compare: the table below describes the category of contract-based B2B databases in general, not ZoomInfo's specific terms.

Where ZoomInfo fits

Fair credit comes first, because it explains why platforms in this category are widely adopted.

What it does well

It is built for the whole go-to-market workflow. As publicly presented, a platform like this puts company data, contact data and the tools to act on them in one place. For a team without sales operations support, not stitching several products together is a real advantage.

It gives reps something to work in. Search filters, saved lists and a place to browse and select results make prospecting something a non-technical person can do. A data-only service gives you none of that.

It is a stored database you can search. A database built and maintained ahead of time supports discovery from criteria across a very large set of records. That is a different job from resolving an identifier you already hold, and it is the job a stored database is designed for.

It offers programmatic access next to the interface. You are not forced into the web app, since ZoomInfo documents programmatic access for its customers. Its endpoints, rate limits and metering were not audited for this article, so read the current documentation before designing around any of them.

Contract versus pay-per-lookup

The difference is less about data and more about how you buy and use it.

With the platform and contract model you agree a package, your team gets access, and the platform becomes the place where prospecting happens. The strengths are predictability, a shared workspace and one vendor relationship. The costs are structural rather than a criticism of any product: you pay for surface area you may not use, the platform tends to become the system of record by default, and its data model and field names become the ones your pipeline has to map to. ZoomInfo's pricing and contract terms are not stated here because they are not published in a form that can be verified and they vary by buyer. Ask for a current quote and read it against your real expected volume.

With pay-per-lookup you hold an account with points. Each call spends points at the rate published on that source's page, and the response reports what the call cost. There is no seat and no workspace. Cost tracks usage, a small experiment costs a small amount, and the data lands directly in your own system. The price is that you build the workflow, storage and interface yourself, and that very large, steady volume deserves a proper cost comparison rather than an assumption that metered is always cheaper.

How NeuralVerge covers the job

For prospecting, three things matter: people search, contact resolution and company context. The mechanism, step by step:

1. Search for people by criteria

LinkedIn people search accepts a free-text query with filters for current or past company, current or past job title, seniority, function, industry, location, years of experience, years at the current company, company headcount, and first or last name. The result is a list of people with names, profile URLs, headlines, current companies and locations. The LinkedIn people search source page is the reference for exact fields, request shape and prices, and the full parameter list is at https://docs.neuralverge.ai.

2. Resolve how to reach them

The search does not return email addresses. Take the name and the company domain into the email finder, which returns a likely work address with a confidence rating. Email validation can then check the address for deliverability signals, and a LinkedIn profile plus email source starts from a profile URL instead.

3. Add company context

Before outreach, someone usually wants to know what the company does and what its public record says. The AI research product answers an open question with sources attached, and AI extraction turns a page or document into JSON you define. This is the step a prospecting database is not built for.

4. Hand the result to your own system

Every call returns a session id and a consistent envelope. Nothing is sent to anyone; your CRM, warehouse, script or agent takes the records from there.

Where each side is the right call

Choose a platform such as ZoomInfo for a sales team that works in a UI every day, for discovery from criteria across a very large stored set, for a large organisation whose value is in fitting existing CRM, marketing and admin integrations, or for steady, high, predictable volume across many users. Compare a real quote with a metered estimate for your volume rather than assuming either wins.

Choose NeuralVerge for the following reasons:

REST and MCP

The same sources are reachable both ways. LinkedIn people search and email finder are available over REST and as the MCP tools run_linkedin_people_search and run_linkedin_email, so an agent can run steps 1 to 3 inside one task using one account. AI research runs over REST only. For connecting the hosted MCP server, see the MCP server for Claude guide.

Here is one REST request to the people search source, taken from its source page. It looks for directors of revenue operations in the United States and caps the run at 25 results.

curl -X POST https://api.neuralverge.ai/functions/v1/run-linkedin-people-search \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "searchQuery": "head of revenue operations",
    "currentJobTitleFilter": ["Head of Revenue Operations"],
    "seniorityLevelFilter": ["Director"],
    "locations": ["United States"],
    "maxResults": 25
  }'

An agent makes the same call as the MCP tool:

{
  "tool": "run_linkedin_people_search",
  "arguments": {
    "searchQuery": "head of revenue operations",
    "seniorityLevelFilter": ["Director"],
    "maxResults": 25
  }
}

The response below is a real captured response from this source, published on its source page. It comes from a different request than the one above, a full-mode search for recruiters at one company in Amsterdam capped at three results, and the person in it is a placeholder, so there is nothing to redact. It is shown for the shape, not the query, and is copied as published.

{
  "session_id": "11111111-1111-1111-1111-111111111111",
  "verification_type": "data_source",
  "kind": "linkedin_people_search",
  "scraperMode": "full",
  "autoQuerySegmentation": false,
  "maxItems": 3,
  "startPage": 2,
  "locations": [
    "Amsterdam"
  ],
  "profileScraperMode": "Full",
  "recentlyChangedJobs": false,
  "recentlyPostedOnLinkedIn": false,
  "searchQuery": "OpenAI recruiter",
  "currentCompanies": [
    "OpenAI"
  ],
  "currentJobTitles": [
    "Recruiter"
  ],
  "human": "# LinkedIn people search\n\n- **Results returned:** 3\n\n### Jane Doe\n\n- **Profile URL:** https://www.linkedin.com/in/jane-doe/\n- **First name:** Jane\n- **Last name:** Doe\n- **Title:** Recruiter\n- **Company:** OpenAI\n- **Location:** Amsterdam\n- **Summary:** Technical recruiting across research and engineering roles.",
  "machine": {
    "total_items": 3,
    "first_item": {
      "firstName": "Jane",
      "lastName": "Doe",
      "profileUrl": "https://www.linkedin.com/in/jane-doe/",
      "headline": "Recruiter at OpenAI",
      "currentCompany": "OpenAI",
      "location": "Amsterdam"
    },
    "items": [
      {
        "firstName": "Jane",
        "lastName": "Doe",
        "profileUrl": "https://www.linkedin.com/in/jane-doe/"
      }
    ]
  },
  "points_per_25_results": 100,
  "points_per_profile": 5,
  "total_points": 115
}

Four things are worth reading off this response:

• —There are two renderings of the same result. The human field is readable text. The machine object is the structured version your code should use.
• —The cost is in the response. 100 points for the block plus 5 for each of the 3 profiles in full mode: 115 points.
• —The list is people, not contact details. There is no email in it, which is why step 2 exists.
• —Parameters echo back. The response repeats the filters that were applied, so a stored result can be traced to the query that produced it.

A prospecting example and moving over

Take Acme Oy (Finland), a fictional company, and the job of finding senior sales people there, reaching one, and learning enough about the company to write a relevant first message.

The same task on each side

In a platform, a rep searches, selects a contact and moves it into a campaign inside one tool, which is quick when a person does the work. With NeuralVerge, a people search returns structured candidates, email finder and validation resolve and check an address, and a research question returns a sourced summary. Nothing sends an email; your script, agent, CRM or a person does. The platform gives a shorter route from search to sent message. The data-only route gives more context and leaves the sending to you.

Moving over in stages

You do not have to switch everything at once. A sensible order, from least to most disruptive:

1. —Run a sample alongside. Take a few hundred of your real target accounts, run them through both, and compare resolved records, field fit and how current the titles are.
2. —Move one automated step. Pick the step that already runs without a human, such as nightly enrichment of new CRM records, and point it at the per-lookup sources.
3. —Give the agent a task. Let an agent build a first-pass list with candidates, addresses and a short cited briefing, and have a person review it.
4. —Decide about the workspace last. Only once the data side is proven should you ask whether your reps still need the platform seats, or fewer of them.

Settle where contacts live before step 2, so you are not paying to keep two copies in sync.

Cost and limits

The people search source is billed in blocks: 100 points per 25 results, and in full mode each returned profile adds 5 points. Its page lists this as $0.10 per block of 25 and says a search that matches nothing is not billed. The later steps are priced on their own pages: email finder at 10 points per lookup, email validation at 1 point per check. Current plan details are on the pricing page.

For the Acme Oy run, the bill is the people search block charge, plus a per-profile charge if full mode is used, plus 10 points per email lookup that finds an address. Spend follows the number of people you pull, not a package size agreed in advance.

Limits worth knowing

A loose query is the expensive mistake. Every parameter on the people search is optional, so it is easy to run an accidentally large search. The source page advises setting maxResults and at least one narrowing filter before the first run. Narrow filters are both cheaper and better.

Not every contact resolves. Email finder returns an address when one can be found and a miss when it cannot. Small companies and generic domains are the hardest. Treat a miss as an expected outcome; the finder source page states that misses are not billed.

There is no interface for reps. NeuralVerge gives you data and research. Lists, review screens and campaign tooling are yours to build or buy.

Personal data carries obligations. People search and contact lookups return personal data. Use them for a legitimate business purpose under your own lawful basis, honour deletion and opt-out requests, and check the acceptable use policy for what is out of bounds. That applies whichever provider you use.

How to decide

• —Which parts of the platform do you use today? If only search and enrichment, a data-only source may cover it. If reps live in the workspace, you are replacing a workflow.
• —Who or what is the caller? A person in a browser, a scheduled job and an agent each favour a different shape.
• —Is enrichment a step in a pipeline? A service that finds people or resolves a contact and passes the result on fits a per-call source better than a seat.
• —Will an agent prospect for you? One account and one response shape reduce the integrations to maintain when an agent searches, resolves contacts and researches the company in one task.
• —Is your volume uneven or experimental? Paying for what you run avoids committing to a package before you know the segment works.
• —Does a good first message need company context? Registry records, reviews or an answer to an open question in the same account save a vendor.
• —What does your volume look like over a year? Estimate calls per month, then compare a real quote with the metered cost of the same calls, including the block charge and the follow-up lookups.
• —Do you need to browse a large stored set? If discovery from criteria across a big database is the job, a platform is built for it.
• —Have you run a real sample? Compare resolved records and field fit on your own accounts, not feature lists, and check current terms for every figure you rely on.

Frequently asked questions

Is NeuralVerge a drop-in replacement for ZoomInfo?

No. ZoomInfo is publicly positioned as a go-to-market platform, and NeuralVerge is a set of data sources plus research and extraction that return structured JSON. NeuralVerge can replace the part of the job that is searching for people and resolving contact details through an API or an agent. It does not replace a rep-facing workspace, saved lists, or any feature of ZoomInfo that depends on a stored database you browse.

How does pay-per-lookup billing differ from a platform contract?

With pay-per-lookup billing you spend points on the calls you make, and the cost of a run follows the size of the run. With a platform contract you typically agree a package up front and use it. Neither is cheaper in the abstract. Pay-per-lookup tends to suit uneven or experimental volume, while a contract can suit steady, high-volume use by many people. Check ZoomInfo's current terms for its side.

Can I search for prospects by title and seniority through the API?

Yes. LinkedIn people search accepts filters such as current job title, seniority, function, company, industry, location and years of experience, and returns matching people with profile URLs, headlines and current companies. It does not return email addresses, so a second call such as email finder resolves a professional address from a name and a company domain.

Does NeuralVerge have a workspace where reps can browse and save lists?

No. NeuralVerge returns structured JSON from sources you call, and what happens next is up to your own system: a CRM, a warehouse, a script or an agent. If your reps need a place to browse candidates, save lists and run campaigns, that is the part of a platform you would keep or replace with other tooling.

Can an AI agent run this workflow end to end?

That is the intended use. Each data source is available as an MCP tool as well as over REST, including the LinkedIn sources. AI research runs over REST. Because every response uses the same envelope and carries a session id, an agent can chain a search, an email lookup and a research question without separate integrations.

Will I get the same people ZoomInfo returns?

Not necessarily, and I would not promise it. The two systems answer from different places, so a sample of your own target accounts run through both is the only reliable way to compare. Look at the fields you will actually use, how many records resolve, and how current the titles are.