What is a company lookalike API?
A company lookalike API — also called a similar-companies API— takes one seed company and returns other companies that resemble it. Teams use it to expand an Ideal Customer Profile, build prospecting lists, or find competitors and comparables programmatically, without manual research. The hard part isn't getting a list back — it's whether the companies on it are actually relevant. That's what this benchmark measures.
What "lookalike" means for companies
A lookalike is a company that resembles a seed account on the dimensions that matter for go-to-market: product and market, company size and stage, technology stack, and business model. A lookalike API automates the question "find me more companies like this one" — the programmatic version of "our best customer looks like X; who else looks like X?" The output is only useful if the returned companies are genuinely similar, which is a measurable quality, not a marketing claim.
How each provider implements lookalikes — and where each is strongest
There is no single method. Each provider builds "similar" differently, which is why their results — and their strengths — diverge. The five approaches on this benchmark:
| Provider | How it finds lookalikes | Strongest at (measured) | Precision@100 |
|---|---|---|---|
| OpenFunnel | embeddings over a company index | Best for long-list relevance (Precision@100) | 69.8% |
| Parallel | an agentic research API; lookalikes via Entity Search | Strongest at long-list relevance (Precision@100) (#2 of 5) | 56.5% |
| Ocean.io | AI-driven lookalike search across a global company graph | Strongest at long-list relevance (Precision@100) (#3 of 5) | 48.6% |
| Exa | neural web search with a 'similar to this URL' endpoint | Strongest at top-of-list precision (Precision@10) (#3 of 5) | 25.8% |
| PredictLeads | similar companies via shared tech / news / jobs co-signals | Best for top-of-list precision (Precision@10) | 19.4% |
The takeaway: a provider built on a company graph optimizes for coverage; one built on neural web search or co-signals can nail the first few results but thin out over a long list; an embedding index tends to hold relevance deeper. No single approach wins every axis — see the full matrix and methodology →
Where each provider shines, by industry
We read the per-industry results as patterns, not precise rankings — the seed set is focused, and only B2B SaaS and fintechhave enough seeds to call confidently. Aggregating each vendor's per-seed relevance by the seed company's industry, the shape that emerges:
| Provider | What the per-industry results suggest |
|---|---|
| OpenFunnel | Leads no single best-sampled industry, but is top-two in both (B2B SaaS and fintech) and outright leads Dev tools, Home services, Hospitality, Industrial, and Logistics — the broadest, most even coverage, and the safe pick when your ICP spans sectors. |
| Parallel | Leads B2B SaaS — its best-sampled win and tops E-commerce and Healthtech. Beyond that, strongest in Home services and Fintech. |
| Ocean.io | Leads Fintech — its best-sampled win. Beyond that, strongest in Home services and Hospitality. |
| Exa | Tops no industry on long-list relevance. Its relatively best sectors are Home services and Hospitality, but it trails the category leaders everywhere — built for the first handful of results, not the full hundred. |
| PredictLeads | Tops no industry on long-list relevance, and posts the best top-10 precision on the whole board. Its relatively best sectors are Industrial and Hospitality, but it trails the category leaders everywhere — built for the first handful of results, not the full hundred. |
The bottom line: relevance varies more by sector than any single headline number implies — pick by the industries you actually sell into, then verify on your own seeds. See the full per-seed matrix →
How to evaluate a lookalike API
Vendor claims are not comparable — each tests on its own data. The only fair test gives every provider the same seed companies and the same input, then scores how many of the companies each one returns are actually relevant. Four things to look at:
| What to check | Why it matters |
|---|---|
| Top-of-list quality | Are the first ~10 results genuinely similar? Matters if you act on a short list. |
| Long-list quality | Are the first ~100 still relevant? A provider can win the top-10 and collapse here. |
| Coverage | How many relevant companies does it return at all? Thin coverage caps your TAM. |
| Cost per relevant company | Cheap-but-irrelevant is expensive. Normalize cost by relevant results, not raw calls. |
The short version:there is no single "best" lookalike API — top-of-list quality, long-list quality, coverage, and cost favor different providers. Decide on the axis that matches your workflow, then verify the numbers yourself. Compare all five on the live benchmark →