Deadpipe
Site offlineLLM observability
Details
- Follow on
- @deadpipecom
- Categories
- AIDeveloper ToolsAnalytics & Monitoring
- Target Audience
- DevelopersData ScientistsBackend Developers
- Platforms
- API
About Deadpipe
LLM applications break silently. A model update changes behavior. A prompt tweak causes schema failures. Latency spikes at 3am. And nobody knows until users complain. Every observability tool does logging. Nobody does automatic baseline drift detection with schema validation and change context hashes—in one line of code. Deadpipe fills that gap. With a single context manager, Deadpipe captures 40+ metrics—latency, tokens, costs, schema violations, refusals, and more—then automatically builds statistical baselines. No thresholds to configure. No dashboards to set up. Just wrap your LLM calls, and get alerted when something drifts.
Product Insights
Deadpipe provides LLM observability through a context manager that automates baseline drift detection for latency, costs, and schema violations. It serves developers by capturing over 40 metrics via a single line of code to identify silent application failures.
- Automatic statistical baseline generation without manual threshold configuration.
- Captures over 40 distinct metrics including tokens, costs, and schema violations.
- Single line of code integration via a context manager for rapid deployment.
- Provides change context hashes to track how prompt tweaks impact performance.
Ideal for: Developers, data scientists, and backend developers can use this tool to automate monitoring and alerting for LLM application regressions.
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