LM Studio vs Ollama

When to pick LM Studio

• You want to browse, download and chat with models without touching a terminal: the built-in catalog and chat interface make LM Studio the friendlier first local LLM app.
• You are on an Apple Silicon Mac and want the Apple MLX backend alongside llama.cpp, or you want to compare several open-weight models side by side before standardizing on one.
• You want optional cloud models with Zero Data Retention on US-hosted infrastructure: Bionic+ at $20/month adds Kimi K3, GLM 5.3 and DeepSeek V4 Flash to the same app.

When to pick Ollama

• You want an open-source runtime you can audit and script: Ollama’s core is MIT-licensed, installs in one line and runs models by name from the CLI.
• You are wiring local models into coding assistants, agents or your own apps and want a REST API with official Python and JavaScript libraries.
• You want cloud inference priced in plain dollars: Pro at $20/month (or $200/year) includes $60 of usage credits, Max at $100/month includes $300, and a Team plan at $500/month covers unlimited users.

Bottom line

For running models on your own hardware, both are free with no per-token cost, run on Windows, macOS and Linux, and expose OpenAI-compatible endpoints, so the choice comes down to interface and openness. LM Studio is the better default for people who want a polished graphical app and Apple MLX support. Ollama is the better default for developers who want an MIT-licensed runtime, a scriptable CLI and the broader integration ecosystem. The paid cloud plans start at the same $20/month, but Ollama states exactly how much inference each plan buys, while LM Studio leads on Zero Data Retention for its hosted models. Many developers install both.