45

Out of 100

N/A

Post-money

N/A

All rounds

45/100

2023

1-50 employees

March 2026

Ollama is an open-source tool that enables developers and technical users to download, run, and interact with large language models locally on their own hardware with a single command-line instruction. The project supports a growing library of open models including Llama, Mistral, Gemma, and Code Llama, running them through a local API server compatible with the OpenAI API interface.\n\nOllama has

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Unknown

Founder & CEO

StageBootstrapped
Employees1-50
Country🇺🇸 United States

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Bootstrapped · No public funding round data available yet.

Frequently Asked Questions

What is Ollama's valuation?
Ollama's valuation is not publicly disclosed.
Who invested in Ollama?
Investor information for Ollama is not publicly available at this time.
When did Ollama last raise funding?
No public funding round data is currently available for Ollama.
How many employees does Ollama have?
Ollama has approximately 1-50 employees.
What does Ollama do?
Ollama is an open-source tool that enables developers and technical users to download, run, and interact with large language models locally on their own hardware with a single command-line instruction. The project supports a growing library of open models including Llama, Mistral, Gemma, and Code Llama, running them through a local API server compatible with the OpenAI API interface.\n\nOllama has accumulated hundreds of thousands of installations and tens of thousands of GitHub stars, becoming the default tool for developers who want to run LLMs without sending data to cloud providers. The project has attracted integrations from dozens of downstream tools and IDEs that use its local API as a privacy-preserving alternative to OpenAI and Anthropic endpoints.\n\nLocal AI inference is growing in importance as privacy regulations tighten, enterprise data governance policies mature, and the cost of cloud inference accumulates at scale. Ollama holds a commanding mindshare position among developers exploring local model deployment, and its broad model library and clean API interface make it the entry point for most developers entering the local LLM ecosystem. The absence of external funding reflects a community-driven development model where adoption precedes commercialization.