Graphsignal
Graphsignal is a production-scale inference profiling platform that helps engineers optimize AI performance across models, engines, GPUs, and other accelerators.
Graphsignal
What is Graphsignal?
Graphsignal vs Similar AI Tools
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How to use Graphsignal?
- 1Sign up for a free account at graphsignal.com and get your API key.
- 2Install Graphsignal using uv or pip: 'uv tool install graphsignal[cu12]' or 'pip install graphsignal[cu12]'.
- 3Set the GRAPHSIGNAL_API_KEY environment variable.
- 4Wrap your launch command with 'graphsignal-run': e.g., 'graphsignal-run vllm serve <model> --port 8001'.
- 5Log in to Graphsignal to monitor and analyze your application.
Graphsignal Key Features
- Continuous high-resolution profiling timelines
- LLM generation tracing with per-step timing and token throughput
- System-level metrics for CPUs, GPUs, and accelerators
- Error monitoring for device-level failures and inference errors
- Inference telemetry for AI agents
Graphsignal Use Cases
- Monitor vLLM, PyTorch, and SGLang inference performance
- Identify bottlenecks in GPU utilization
- Debug and optimize AI agent inference pipelines
Graphsignal Pricing & Free Credits
Graphsignal currently operates on a Free model.
This tool is completely free to use
Graphsignal Pros & Cons
Pros
- Low overhead on production performance
- Easy integration with major inference frameworks
- Provides both operation-level and system-level insights
Cons
- Requires outbound connection to api.graphsignal.com
- Limited to supported inference frameworks
What is Graphsignal best for?
- AI engineers optimizing inference performance
- ML teams monitoring production models
- Developers building AI agents
Graphsignal FAQ
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