| Feature | Ollama | vLLM |
|---|---|---|
| Category | LLMs & AI Infra | LLMs & AI Infra |
| Sub-category | LLM Serving | LLM Serving |
| Maturity | stable | stable |
| Complexity | beginner | advanced |
| Performance tier | medium | enterprise grade |
| License | MIT | Apache-2.0 |
| License type | permissive | permissive |
| Pricing | fully free | fully free |
| GitHub stars | 110.0K | 45.0K |
| Contributors | 500 | 600 |
| Commit frequency | daily | daily |
| Plugin ecosystem | medium | none |
| Docs quality | good | good |
| Backing org | Ollama Inc | UC Berkeley / vLLM Team |
| Funding model | vc_backed | vc_backed |
| Min RAM | 4 GB | 8 GB |
| Min CPU cores | 2 | 4 |
| Scaling pattern | single_node | horizontal |
| Self-hostable | Yes | Yes |
| K8s native | No | Yes |
| Offline capable | Yes | No |
| Vendor lock-in | none | none |
| Languages | Go, C++ | Python, C++, CUDA |
| API type | REST | REST |
| Protocols | HTTP | HTTP |
| Deployment | binary, docker | pip, docker |
| SDK languages | python, javascript, go, rust | python |
| Team size fit | solo, small, medium | small, medium, enterprise |
| First release | 2023 | 2023 |
| Latest version | — | — |
When to use Ollama
- ✓ Run LLMs locally for private/offline AI
- ✓ Development environment with local AI models
- ✓ Code completion backend for Continue/Tabby
- ✓ Chatbot prototype without API costs
When to use vLLM
- ✓ Serve LLMs in production with high throughput
- ✓ Multi-model serving for AI gateway
- ✓ Batch inference for document processing
- ✓ Low-latency chatbot backend
Ollama anti-patterns
- ✕ Not for high-throughput production serving
- ✕ Single-user optimized not multi-tenant
- ✕ No built-in batching or queuing
- ✕ Needs decent GPU for large models
vLLM anti-patterns
- ✕ Requires GPU - no CPU-only mode
- ✕ Complex setup compared to Ollama
- ✕ Not for single-user local development