Ollama vs vLLM compared with benchmark data at 1 and 50 concurrent users. A decision framework to know when Ollama's simplicity becomes a liability and vLLM's throughput justifies the overhead.

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Ollama excels for prototyping and low-concurrency local LLM development with its simple setup and developer experience, but struggles under concurrent load due to sequential request handling. vLLM uses PagedAttention and continuous batching to deliver 20x higher throughput at 50 concurrent users, making it the production choice when serving 10+ concurrent users. The transition point is 5-15 concurrent users depending on latency requirements. Both expose OpenAI-compatible APIs, enabling straightforward migration by changing base URL and model name configuration.

Ollama vs vLLM: When to Scale Your Local AI Stack

What Ollama Actually Does (and Does Well)

What vLLM Actually Does (and Why It Exists)

The Benchmark: Single User vs. 50 Concurrent Users

The Transition Point: A Decision Framework for Startups

Migration Path: Ollama to vLLM Without Rewriting Your App