LM Studio vs LocalAI

LM Studio

OpenAI-compatible chat completions, responses, completions and embeddings, Anthropic-compatible /v1/messages and a native /api/v1, all on one port. Authentication is off by default, so any local process can call the server.

LocalAI

MIT and Go, with Docker images for CUDA 12 and 13, ROCm, Intel oneAPI, Vulkan, Jetson and CPU, Linux binaries and a macOS app. No authentication by default. Loopback, LAN and VPN binds answer every caller, and keys set by environment variable grant full admin.

LM Studio

1. Send Authorization: Bearer $LM_API_TOKEN when the owner gives you a token. With Require Authentication on, every request needs it
2. Pass previous_response_id to /api/v1/chat instead of resending the history, and store: false for one-off calls
3. List models with GET /api/v1/models before naming one. A named model that's downloaded loads just in time
4. Install the Python SDK pre-release (1.6.0b1) and pass api_token directly. It reads LMSTUDIO_API_TOKEN, not the LM_API_TOKEN the docs name
5. Set allowed_tools on every MCP integration. Without it the model sees every tool on the server

LocalAI

1. Send Authorization: Bearer <key> when the operator has set keys. A 401 means the instance has auth on
2. Read /.well-known/localai.json and /api/instructions first. Both answer without a key and list what this instance can do
3. Back off on 429 and 503 for the Retry-After seconds. A 503 can mean the model is still loading
4. Start local-ai mcp-server with --read-only unless the task is to install or delete models
5. Take model names from /v1/models. Each instance names its own