82 AI Inference Companies in 2026
AI inference companies run trained models in production: inference clouds and APIs for open and custom models, serverless GPUs, model-serving engines and chips built to answer prompts fast and cheap.
- Companies
- 82
- In LeadWave's database, November 2026
- Total funding raised
- $11.3B
- Across 30 funded companies
- Median headcount
- 48
- Of 47 with a known headcount
- Median year founded
- 2019
- Of 58 with a known year
The largest AI Inference companies
The top 30 of 82, strongest matches first, then by size. Headcount is the latest known figure.
| Company | Website | Employees | Headcount | Funding | CEO | |
|---|---|---|---|---|---|---|
| 1 | together.ai | 201–500 | 450 | $1.3B | Ce Zhang | |
| 2 | sambanova.ai | 201–500 | 400 | $2.5B | Rodrigo Liang | |
| 3 | multiversecomputing.com | 201–500 | 260 | $868.2M | Enrique Lizaso Olmos | |
| 4 | fal.ai | — | 114 | $637.5M | Gorkem Yurtseven | |
| 5 | inworld.ai | 51–200 | 85 | $132M | Kylan Gibbs | |
| 6 | replicate.com | — | 49 | — | Zhizhen Zhang | |
| 7 | openrouter.ai | 11–50 | 42 | — | Alex Atallah | |
| 8 | dria.co | 11–50 | 18 | $9M | Kerim Kaya | |
| 9 | pipeshift.com | — | 17 | — | Pranav Reddy | |
| 10 | inferact.ai | — | 15 | — | Simon Mo | |
| 11 | baseten.co | 11–50 | 12 | Under $1M | Tuhin Srivastava | |
| 12 | bentoml.ai | 1–10 | 6 | — | Chaoyu Yang | |
| 13 | fireworks.ai | — | — | — | — | — |
| 14 | groq.com | — | — | — | — | — |
| 15 | modal.com | — | — | — | Erik Bernhardsson | |
| 16 | cerebras.ai | — | — | — | — | Andrew Feldman |
| 17 | deepinfra.com | — | — | — | Nikola Borisov | |
| 18 | appliedcompute.com | — | — | — | — | — |
| 19 | etched.com | — | — | — | — | — |
| 20 | featherless.ai | — | — | — | Eugene Cheah | |
| 21 | firmus.co | — | — | — | — | — |
| 22 | fluidstack.io | — | — | — | — | — |
| 23 | gimletlabs.ai | — | — | — | — | — |
| 24 | hyperbolic.ai | — | — | — | Jasper Zhang | |
| 25 | novita.ai | — | — | — | — | |
| 26 | nscale.com | — | — | — | Josh Payne | |
| 27 | primeintellect.ai | — | — | — | — | — |
| 28 | river.ai | — | — | — | — | — |
| 29 | runpod.io | — | — | — | Zhen Lu | |
| 30 | digitalocean.com | 501–1,000 | 840 | $456M | Yancey Spruill |
AI Inference companies at a glance
- 75% are headquartered in United States, followed by Italy.
- The largest is Together AI, with 450 employees.
- 30 have disclosed funding, $11.3B in total; 10 raised $100M or more.
- Half were founded before 2019, and 38% in 2020 or later.
- United States48 · 75%
- Italy4 · 6%
- France3 · 5%
- United Kingdom3 · 5%
- Germany1 · 2%
- India1 · 2%
- 1–1014 · 26%
- 11–5020 · 37%
- 51–20010 · 19%
- 201–5008 · 15%
- 501–1,0002 · 4%
- 1,001–5,0000 · 0%
- 5,001–10,0000 · 0%
- 10,000+0 · 0%
- Before 20000 · 0%
- 2000–20092 · 3%
- 2010–201410 · 17%
- 2015–201924 · 41%
- 2020–202214 · 24%
- 2023 or later8 · 14%
- Under $1M2 · 6%
- $1M–5M7 · 23%
- $5M–10M2 · 6%
- $10M–50M7 · 23%
- $50M–100M2 · 6%
- $100M–500M3 · 10%
- $500M–1B4 · 13%
- $1B+4 · 13%
How this list was built
A company is on this list if LeadWave tags it as AI inference, or its description or website presents inference APIs, model serving or inference hardware.
The data comes from LeadWave's database of SaaS and AI companies, last updated in November 2026. Companies that share a website are counted once. Headcount, funding and CEO come from public sources, so some fields are blank for smaller companies.
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Frequently asked questions
LeadWave's database lists 82 AI Inference companies as of November 2026. This page shows the 30 largest; the full list, with CEO contacts, is part of our 15,000+ SaaS companies list.
By headcount, Together AI, with 450 employees, followed by SambaNova and Multiverse Computing.
United States: 75% of those with a known headquarters, followed by Italy at 6%.
SambaNova ($2.5B), Together AI ($1.3B) and Multiverse Computing ($868.2M), by total disclosed funding.
LeadWave's AI companies list covers 9,000+ AI companies and startups, with CEO emails, LinkedIn profiles, funding and 25+ data points in one CSV. It's on the List of AI Companies page.
A model is trained once, on huge clusters; inference happens every time someone uses it, millions of times a day. That makes speed per request and cost per token what matter, so companies build inference clouds, serving software and chips tuned for it.