Ollama

Ollama

Freemium

Build with open models, on your computer and in the cloud.

AI Infrastructure
AI Runtime & Serving

Published 27 September 2026 · Last updated 28 September 2026

Scores

Popularity5/5

The most widely used local LLM runtime by a clear margin, with 178K+ GitHub stars, 52 million monthly downloads, and 2.5 billion+ cumulative downloads.

Learning Curve1/5

A single install command and a single command to pull and run a model make it the lowest-friction way to try a local LLM, with no configuration required to get started.

Flexibility5/5

100+ supported models, an OpenAI-compatible API for drop-in tooling reuse, and both local and cloud execution modes give it very broad applicability across workflows.

Performance4/5

Automatic hardware tuning and support for the latest open-weight models keep it competitive for local inference, though it is not purpose-built for high-throughput production serving the way dedicated inference servers are.

Portability5/5

MIT-licensed, runs on macOS, Windows, and Linux across Apple Silicon, NVIDIA, and AMD hardware, and models are entirely self-hosted with no forced cloud dependency.

About Ollama

Ollama is the most-used local LLM runtime, letting developers download and serve open-weight models (Llama, Qwen, DeepSeek, GLM, MiniMax, gpt-oss, Gemma, and 100+ others) with a single command, automatically tuned for whatever hardware it's running on, Apple Silicon, an NVIDIA GPU, or a Linux server with AMD ROCm. It exposes a REST API compatible with the OpenAI Chat Completions format, so existing tooling built against a cloud provider can point at a local Ollama instance with minimal code changes. Tool calling, structured outputs, and vision capabilities work out of the box for models that support them.

Because it runs entirely on the user's own hardware by default, models never leave the machine and nothing is used to train Ollama's own systems, a meaningful privacy and cost advantage for local development, testing, and inference-heavy prototyping. For workloads that outgrow local hardware, Ollama Cloud extends the same CLI and API to hosted models running on Ollama's own infrastructure (across US, Europe, and Singapore regions), so a developer can move a workload from a laptop to the cloud without switching tools.

The project is MIT-licensed and open source, with one of the largest communities in the open-weight LLM space. Local usage is entirely free; Ollama Cloud adds paid Pro, Max, Team, and Enterprise plans priced by included usage credits, with pay-as-you-go model pricing on top of each plan's monthly allowance.

Key Features

  • One-command download and serving of 100+ open-weight models (Llama, Qwen, DeepSeek, GLM, MiniMax, gpt-oss, Gemma)
  • OpenAI-compatible REST API, drop-in for existing tooling built against cloud providers
  • Automatic hardware tuning across Apple Silicon, NVIDIA GPUs, and AMD ROCm
  • Tool calling, structured outputs, and vision support for compatible models
  • Fully local by default, models never leave the machine
  • Ollama Cloud extends the same CLI/API to hosted models across US, Europe, and Singapore
  • CLI, REST API, and desktop apps

Pros

  • Free, unlimited local usage with zero per-token cost, the default way most developers first try open-weight models
  • OpenAI-compatible API makes swapping a cloud provider for local inference close to a one-line change in existing code
  • Massive model catalogue (100+ models) updated quickly as new open-weight releases ship
  • Ollama Cloud provides a clean upgrade path to larger models without switching tools or APIs
  • Huge, active community (178K+ GitHub stars, 2.5B+ downloads) means broad compatibility and fast bug fixes

Cons

  • Local performance is bounded by the user's own hardware, larger models require real GPU memory to run well
  • Cloud pricing is usage-based on top of the monthly plan credits, so heavy workloads can exceed the included allowance
  • Cloud model hosting is primarily in the United States, with overflow to Europe and Singapore, latency-sensitive workloads outside North America should test first
  • Not a production inference-serving platform on its own for high-throughput workloads, tools like vLLM are typically paired in for that

Ollama Pricing

Freemium
FreeFree
  • · Run models locally with unlimited usage
  • · Starter usage credits for cloud models
  • · Add credits to unlock all cloud models
  • · No service fees
Pro$20/monthly
  • · $60 of usage credits per month
  • · Access to larger pro models
  • · Run multiple models concurrently
Max$100/monthly
  • · $300 of usage credits per month
  • · Early access to the newest models
  • · 10 concurrent requests
Team$500/monthly
  • · Unlimited users
  • · $1,000 of shared usage credits per month
  • · Centralized billing and administration
  • · Priority support
EnterpriseContact sales
  • · Model access controls and cost budgets per user
  • · Private Slack channel with dedicated support
  • · Custom security questionnaires

Tech Stacks with Ollama

Self-Hosted AI with Ollama and Open WebUI

Infrastructure

A private, ChatGPT-style assistant on hardware you own. Ollama runs open-weight models such as Google Gemma or Qwen, and Open WebUI gives them a chat interface in the browser with accounts, document search, and model management. Both run in Docker, and nothing leaves your machine.

Database:
LLM:
Vector Database add-on:
Hosting add-on:
Model Aggregator add-on:
Tunnel add-on:
Reverse Proxy add-on:
Self-Hosted PaaS add-on:

Local LLM Dev Stack

Developer

A coding agent that works against a model on your own hardware. A local runtime such as Ollama or LM Studio serves an open-weight coding model such as Qwen or Mistral's Devstral, and an agent such as OpenCode or Cline does the editing. No code leaves the machine and there is no per-token bill.

Model Inference:
Coding Agent:
LLM:
Version Control:
Server add-on:
Remote Access add-on:
Terminal add-on:
Code Review add-on:
Model Aggregator add-on:
CI/CD add-on:

OpenClaw Self-Hosted

Infrastructure

Run OpenClaw, the open-source personal AI agent, in Docker on your own server or home hardware. It answers in the messaging apps you already use, thinks with a model such as Claude or OpenAI, and stays closed to the internet, with its dashboard reached over an SSH tunnel.

LLM:
Deploy on:
Model Inference add-on:
Model Aggregator add-on:
Tunnel add-on:
Reverse Proxy add-on:
Self-Hosted PaaS add-on:

Tools Related to Ollama

Works well with Ollama(12)

Ollama can run on a Raspberry Pi 5 with enough RAM to serve small open-weight models locally, a common lightweight self-hosted AI setup for developers who already run other services on a Pi.

Unsloth exports fine-tuned models to GGUF for local serving in Ollama, a common last step after training.

Ollama can pull and run DeepSeek's open-weight models locally with a single command, exposing them through its OpenAI-compatible API.

MiMo runs locally through Ollama via community GGUF quantizations, no cloud API required.

Ollama can pull and run GLM's open-weight models locally with a single command, exposing them through its OpenAI-compatible API.

Ollama can pull and run Qwen's open-weight models locally with a single command, exposing them through its OpenAI-compatible API.

Integrates with Ollama(4)

Langflow includes an Ollama component for wiring locally served open models into a flow.

Dify's official Ollama model provider plugin connects a self-hosted Dify to models served locally by Ollama, so workflows, knowledge bases, and agents run without a hosted model API.

LlamaIndex's llama-index-llms-ollama package points its query engines and agents at models served locally by Ollama, for RAG that stays on your own hardware.

AutoGPT includes Ollama as a built-in model provider, so a self-hosted install can run workflow AI blocks on locally served open models with no model API key.

Alternatives to Ollama(4)

LM Studio leads with a graphical app for finding, downloading, and chatting with local models; Ollama is open source and CLI- and server-first, which suits scripts and headless machines.

Ollama adds a model library, a pull command, and a managed background server on top of the same GGML engine family; llama.cpp is the engine itself, with every quantization, sampling, and GPU-offload setting exposed. Pick Ollama for convenience, llama.cpp for control and the newest model support.

vLLM is a datacenter serving engine that batches many concurrent requests across GPUs; Ollama runs a model on a laptop or single server with one command. Use Ollama for local and small-scale use, vLLM once many users hit the same model.

Groq serves open models on its own LPU hardware as a hosted API with very low latency; Ollama runs open-weight models locally on your own machine. Groq for fast hosted inference, Ollama for private, offline, free local use.

Tags

Open SourceSelf-hostableFree TierAI-powered

Details

Maintained
Yes
Primary language
Go
Domain
ML / AI
GitHub stars
182k
Stars updated
2026-09-23