Sharing a simple Python script to benchmark LLM inference latency across different providers
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Benchmark LLM inference latency across providers using a simple Python script to inform production traffic decisions
Action Steps
- Install the required Python libraries, including requests and time
- Set up API keys for different LLM providers
- Run the Python script to send identical prompts to each provider
- Measure time-to-first-token and total generation time for each provider
- Compare the results to determine the optimal provider for production traffic
Who Needs to Know This
DevOps and AI engineers can use this script to evaluate and compare LLM providers for optimal performance. This is useful for teams deciding where to route production traffic for their AI applications.
Key Insight
💡 Measuring LLM inference latency is crucial for optimal production traffic routing
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Benchmark LLM inference latency across providers with a simple Python script!
Key Takeaways
Benchmark LLM inference latency across providers using a simple Python script to inform production traffic decisions
Full Article
Was tinkering with some latency measurements lately and wanted to share a quick Python snippet that might help others evaluating inference endpoints. The goal was simple: send identical prompts to different providers and measure time-to-first-token and total generation time. Nothing fancy, but useful when you're trying to decide where to route production traffic. Here's the setup I used with the DeepSeek-V4-Pro model: <pre class
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