Company Data API Pricing at Scale: What Enterprise Teams Actually Pay

Your sales team needs to enrich 500,000 company records by month-end. You've narrowed it down to three professional data APIs. One quotes $0.50 per record with monthly credits that expire. Another requires $40k minimum spend on an annual contract. The third charges fractional cents per request with no minimums.

The math looks simple until you factor in API rate limits, failed requests requiring retries, employee headcount data costing extra credits, and the 60% of your monthly allocation you historically don't use before it expires.

This is company data API pricing at enterprise scale—where the advertised price and what you actually pay diverge by 300%.

The Enterprise Pricing Problem Nobody Talks About

Most company data API pricing is structured to hide the true cost until you're committed. You see a simple "

$49/month" tier in the marketing materials, but production usage for enterprise enrichment projects reveals three problems:

Credit expiration waste. Your project cadence doesn't match the vendor's monthly reset. You need 80k credits in November for an account-based marketing campaign, then 15k in December during year-end freeze. Those unused December credits expire. You paid for 80k but used 15k—an 81% waste rate for that billing cycle.

Variable pricing by endpoint. Basic company lookup costs 1 credit. Employee headcount distribution costs 5 credits. Subsidiaries and affiliated pages cost another 3 credits each. What looked like a $0.01-per-record project becomes $0.09 when you need the full company intelligence picture. The pricing page never mentioned this multiplier.

Volume tiers with annual commitments. Your proof-of-concept works perfectly at 10k requests. Production needs 2 million. The vendor's volume discount requires signing a 12-month contract and prepaying for 3 million requests. You have no baseline data on actual usage patterns, but you're locked in. If your eng team optimizes the integration and reduces calls by 40%, you've prepaid for capacity you'll never use.

LinkdAPI uses per-request pricing with no credits, no tiers, and no commitments. You pay a fraction of a cent for each API call—whether you make 1,000 this month or 1 million next month. No waste, no surprise multipliers, no contracts.

Here's what that looks like in practice.

Three Pricing Models Decoded

Every B2B data API uses one of three pricing approaches. Each has second-order costs that don't show up on the pricing page.

Pay-Per-Request (LinkdAPI)

Structure: Fixed cost per API call. Company info request costs ~$0.005. Employee data request costs ~$0.01. You're billed for actual requests made, no minimums.

True cost drivers:

• Failed requests you retry (your cost)
• Development/staging API calls during integration (your cost)
• Exactly what you use in production (your cost)

Hidden savings:

• Zero credit expiration waste
• No seat licenses for your engineering team
• No annual commitment penalties if usage drops

Break-even math: If your monthly usage fluctuates more than 30%, pay-per-request saves money vs monthly credits because you never prepay for capacity you won't use.

Monthly Credits (Most Competitors)

Structure: Pay $X/month for Y credits. Different endpoints cost different credit amounts. Credits expire monthly.

True cost drivers:

• Unused credits that expire (pure waste)
• Endpoint credit multipliers not obvious on pricing page
• Overage charges if you exceed allocation mid-month

Example: A $499/month tier advertises "50,000 credits." Fine print: Company info = 1 credit. Employee data = 5 credits. Your actual project needs 30% employee data. Effective capacity: 35,714 companies, not 50,000. Cost per company: $0.014, not $0.01.

Break-even math: You need >85% utilization every single month to match pay-per-request economics. If you hit that threshold in 7 of 12 months, you're still losing money annually.

Enterprise Contracts (ZoomInfo, Clearbit)

Structure: Annual minimum spend, custom pricing, volume discounts, dedicated support.

True cost drivers:

• 12-month commitment with auto-renewal (usually 15-20% price increase)
• Minimum spend requirements even if usage drops
• Change orders and upgrade fees mid-contract

Hidden costs: Your procurement cycle for renewals takes 90 days. Contract requires 60-day cancellation notice. You're auto-renewed before you finish evaluating alternatives. Year 2 pricing is 18% higher. You're locked in for another 12 months.

Break-even math: Only makes sense if (1) you have consistent 100k+ requests/month, (2) you need dedicated support/SLAs, and (3) your org can absorb the procurement overhead.

Real Cost Calculation: 100K Company Enrichments

Let's model a realistic enterprise project: enriching 100,000 company records with full intelligence—company info, employee headcount distribution, and active job postings.

API calls required per company:

1. GET /api/v1/companies/name-lookup — resolve company name to ID (1 call)
2. GET /api/v1/companies/company/info-v2 — full company data (1 call)
3. GET /api/v1/companies/company/employees-data — headcount + distribution (1 call)
4. GET /api/v1/companies/jobs — active job listings (1 call)

Total: 4 API calls per company = 400,000 requests for 100k companies

LinkdAPI Cost (Pay-Per-Request)

1Base company lookup: 100,000 × $0.003 = $300
2Company info-v2: 100,000 × $0.005 = $500
3Employee data: 100,000 × $0.01 = $1,000
4Jobs data: 100,000 × $0.005 = $500
5
6Total: $2,300 for 100,000 enriched companies
7Cost per company: $0.023

No minimums. No expiration. If you only need 50k next month, you pay half.

Credit-Based Competitor Cost

Assuming $499/month tier with 50,000 "credits":

1Per-company credit cost:
2- Name lookup: 1 credit
3- Company info: 2 credits
4- Employee data: 5 credits
5- Jobs: 2 credits
6Total: 10 credits per company
7
8100,000 companies × 10 credits = 1,000,000 credits needed
950,000 credits/month = 20 months to complete
1020 months × $499 = $9,980 total cost
11
12Cost per company: $0.0998

But wait—credits expire monthly. If your project timeline is 3 months (33,333 companies/month), you need 333,330 credits/month. You're buying:

1333,330 credits needed/month ÷ 50,000 credits/tier = 6.67 tiers
2Round up to 7 tiers × $499 = $3,493/month × 3 months = $10,479
3
4Cost per company: $0.105

LinkdAPI saves 78% vs credit-based at this scale.

Enterprise Contract Cost

Annual contract at ~$40k minimum:

1Assumes volume discount brings cost to $0.04/company-equivalent
2100,000 companies = $4,000 worth of usage
3
4But you're committed to $40,000/year.
5If you complete the project in Q1 and usage drops to 10k/month in Q2-Q4:
6Actual usage value: $4,000 + (9 months × 10k × $0.04) = $7,600
7You paid: $40,000
8
9Waste: $32,400 (81%)

Even with a "discount," you prepaid for capacity you never used.

Company Endpoints That Drive Volume

The most-used endpoints for company enrichment are /api/v1/companies/company/info-v2 and /api/v1/companies/company/employees-data. Understanding their response structure helps you optimize both cost and data quality.

GET /api/v1/companies/company/info-v2

Returns full company profile including headcount, industry, specialties, locations, and company type. This is your anchor endpoint—use it first to validate the company exists and is relevant before calling additional endpoints.

Response fields (verified from API spec):

1{
2  "success": true,
3  "statusCode": 200,
4  "data": {
5    "name": "Anthropic",
6    "id": "89165018",
7    "universalName": "anthropic-ai",
8    "description": "AI safety and research company",
9    "website": "https://anthropic.com",
10    "industry": "Computer Software",
11    "companySize": "201-500",
12    "headquarters": {
13      "city": "San Francisco",
14      "country": "United States",
15      "geographicArea": "California"
16    },
17    "specialties": ["Artificial Intelligence", "Machine Learning"],
18    "founded": 2021,
19    "companyType": "Privately Held",
20    "logoUrl": "https://media.licdn.com/dms/image/...",
21    "employeeCountRange": {
22      "start": 201,
23      "end": 500
24    }
25  }
26}

Key fields for enrichment:

• companySize — filter by employee range
• industry — segment by vertical
• headquarters.geographicArea — geographic targeting
• founded — company age for lead scoring

Cost: ~$0.005 per request with LinkdAPI

GET /api/v1/companies/company/employees-data

Returns employee headcount distribution across functions, seniority levels, and locations. Essential for account-based targeting—you can identify companies with large engineering teams, sales expansion, or geographic presence.

Response fields (verified from API spec):

1{
2  "success": true,
3  "statusCode": 200,
4  "data": {
5    "totalEmployees": 342,
6    "byFunction": {
7      "Engineering": 156,
8      "Sales": 48,
9      "Marketing": 28,
10      "Operations": 42,
11      "Other": 68
12    },
13    "bySeniority": {
14      "Executive": 12,
15      "Director": 24,
16      "Manager": 67,
17      "Individual Contributor": 239
18    },
19    "byLocation": {
20      "San Francisco, CA": 198,
21      "New York, NY": 87,
22      "London, UK": 34,
23      "Remote": 23
24    },
25    "growthRate": "+42% (past 12 months)"
26  }
27}

Key fields for targeting:

• byFunction — find companies investing in specific teams
• growthRate — identify fast-growing orgs
• byLocation — geographic market analysis

Cost: ~$0.01 per request with LinkdAPI

Code Examples: Python, Node.js, Go

All examples assume you've fetched a company ID using /api/v1/companies/name-lookup first. Field names are verified from the API spec.

Python (Async)

1import asyncio
2import os
3from linkdapi import AsyncLinkdAPI
4
5async def enrich_company_list(company_names: list[str]):
6    """
7    Enrich a list of companies with full intelligence data.
8    Returns: List of enriched company objects
9    """
10    api_key = os.getenv("LINKDAPI_KEY")
11    
12    async with AsyncLinkdAPI(api_key) as api:
13        enriched = []
14        
15        for name in company_names:
16            # Step 1: Resolve company name to ID
17            lookup = await api.company_name_lookup(query=name)
18            if not lookup.get('success') or not lookup['data']:
19                continue
20                
21            company_id = lookup['data'][0]['id']
22            
23            # Step 2: Fetch company data concurrently
24            info, employees, jobs = await asyncio.gather(
25                api.get_company_info(company_id=company_id),
26                api.get_company_employees_data(company_id),
27                api.get_company_jobs([company_id], start=0)
28            )
29            
30            # Step 3: Extract verified fields from spec
31            if info.get('success'):
32                company_data = info['data']
33                employee_data = employees['data'] if employees.get('success') else {}
34                
35                enriched.append({
36                    'name': company_data.get('name'),
37                    'industry': company_data.get('industry'),
38                    'size': company_data.get('companySize'),
39                    'headquarters': company_data.get('headquarters', {}).get('city'),
40                    'total_employees': employee_data.get('totalEmployees'),
41                    'engineering_count': employee_data.get('byFunction', {}).get('Engineering'),
42                    'growth_rate': employee_data.get('growthRate'),
43                    'active_jobs': len(jobs['data']) if jobs.get('success') else 0
44                })
45        
46        return enriched
47
48# Usage
49companies = ["Anthropic", "OpenAI", "Stripe", "Databricks"]
50enriched_data = asyncio.run(enrich_company_list(companies))
51
52for company in enriched_data:
53    print(f"{company['name']}: {company['total_employees']} employees, "
54          f"{company['active_jobs']} open roles")