Open Source LLMs You Can Self-Host: API Wrappers Compared

Want the power of LLMs without the per-token cost? Self-hosting is more accessible than ever.

Top Open Models (July 2026)

|-------|-----------|---------|----------|

Hosting Options

1. RunPod / Modal / Banana

Cost: $0.50-2.00/hour for A100

Setup: Container-based, minimal config

Best for: Flexible, on-demand inference

2. Together AI / DeepInfra / Groq

Cost: Per-token, $0.10-0.60/1M tokens

Setup: OpenAI-compatible API

Best for: Zero-ops, instant deployment

3. vLLM + Your Own GPU

Cost: Hardware only ($0 if you own)

Setup: Docker + vLLM on any NVIDIA GPU

Best for: Maximum control, data privacy

4. Ollama (Local)

Cost: Free

Setup: One command: ollama run llama3.1

Best for: Development, testing, offline use

OpenAI-Compatible Wrappers

Many self-hosted solutions now expose OpenAI-compatible endpoints:

# vLLM, TGI, llama.cpp all support:

curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{"model": "llama3.1", "messages": [{"role": "user", "content": "Hello"}]}'

This means you can use the OpenAI Python/JS SDKs with your self-hosted models — just change the base URL.

When to Self-Host

**Data privacy** is critical

**High volume** makes per-token pricing expensive

**Fine-tuned models** that arent available via API

**Offline/air-gapped** environments

When to Use APIs

**Prototyping and MVPs** (faster to market)

**Variable traffic** (no idle GPU costs)

**Need latest models** (providers update faster)

**Small team** with no DevOps bandwidth