Ollama
Open-source platform for running large language models (Llama, Mistral, Qwen, DeepSeek, Gemma) locally, with the Ollama Cloud tier. 9M active developers in 2026.
Regional availability·3 regions
- United States
- Europe
- Singapore
Description
Ollama is an open-source platform (MIT License) for running and managing large language models locally on the user's machine or in Ollama Cloud. The project first launched on July 7, 2023 and by July 2026 had grown a community of 9 million active developers, becoming the de facto standard for running open-weight models locally.
Ollama is written in Go and uses llama.cpp as the inference backend for local models. It exposes three surfaces: a command-line interface (ollama pull, ollama run, ollama launch), a native desktop app, and a local REST API (port 11434 by default). Official Python and JavaScript clients let developers plug Ollama into any application.
The model library covers open-weight families such as Llama, Gemma, Mistral, Qwen, DeepSeek, gpt-oss, Phi, plus partner models (NVIDIA Nemotron, MiniMax M3, GLM-5.2). Ollama handles weight downloads, quantization, backend selection (CPU, CUDA, ROCm, Metal, Apple MLX) and runtime — the user needs a single command.
Ollama Cloud, launched in 2025, offers paid access to larger models on datacenter-grade hardware (NVIDIA Blackwell), in US, Europe and Singapore regions, with a zero data retention guarantee. Pricing plans: Free (included with an Ollama account), Pro ($20/month — 3 cloud models running in parallel), and Max ($100/month — 10 cloud models in parallel, 5× more usage than Pro).
The platform integrates natively with agentic tools and coding assistants: Claude Code, OpenAI Codex, OpenClaw, GitHub Copilot CLI, Hermes Agent. In March 2026 Ollama added preview support for Apple MLX. Experimental image generation support (Flux) is available on macOS.
The project is developed by Ollama Inc., headquartered in Palo Alto (California), founded by Jeffrey Morgan and Michael Chiang. In July 2026 the company announced a $65M Series A round, raised alongside a milestone of reaching 9 million active developers.
MLOps LifecycleMLOps LifecycleFull model lifecycle: registry, feature store, prompt management, monitoring and human-in-the-loop.
Model Registry
Feature Store
Prompt Management
Monitoring
Human-in-the-Loop
Data & KnowledgeData & Knowledge ManagementData connectors, vector database integration, native vector search and data management (PII, provenance, synthetic data).
ApplicationsAI ApplicationsDomains and use cases this platform is best suited for — from RAG and fine-tuning to scientific research.
SecurityEnterprise SecurityCertifications, access controls and data-protection features essential for corporate deployments and cloud privacy compliance.
Developer EcosystemDeveloper EcosystemDeveloper resources: available SDKs, supported programming languages, and infrastructure features and model-deployment methods.
Pricing & Business ModelPricing & Business ModelBilling models (usage-based, provisioned throughput), resource limits and SLA parameters (uptime, support tiers).
Pricing models
Resource quotas
SLA & Support