Gentrace

Automatically captures and logs all LLM API calls, responses, and metadata in a centralized system. Creates detailed execution traces that show the complete flow of data through generative AI applications.

Maintains a version history of all prompts used in production, allowing teams to track changes, compare versions, and rollback to previous prompts. Enables systematic experimentation with different prompt formulations.

Tracks and monitors applications that use multiple LLM models in sequence or parallel. Provides visibility into how requests flow through different models and where bottlenecks occur.

Analyzes historical LLM request data to identify patterns and suggest improvements to prompts. May recommend changes based on quality metrics, cost, or latency optimization.

Enables side-by-side testing of different LLM models, prompts, and configurations against the same inputs. Automatically tracks performance metrics and statistical significance to determine which variant performs better.

Monitors and aggregates costs across all LLM API calls, breaking down expenses by model, prompt, user, or other dimensions. Provides visibility into spending patterns and cost optimization opportunities.

Assesses the quality of LLM outputs against defined criteria and metrics. Supports both automated evaluation (using rubrics or reference answers) and manual annotation workflows.

Tracks response times and performance metrics for LLM requests, identifying bottlenecks and performance degradation. Provides insights into which models, prompts, or configurations are slowest.

Automatically identifies failed LLM requests and categorizes failure patterns. Surfaces common error types and their root causes to help teams debug issues systematically.

Validates that new prompts, models, or configurations are safe to deploy to production by running them against test datasets and comparing results to baseline performance.

Provides visual dashboards and analytics interfaces to explore LLM application performance across multiple dimensions. Enables filtering, sorting, and drilling down into specific requests or time periods.

Enables automated testing of LLM applications against predefined test cases to ensure that changes don't introduce regressions. Compares new outputs against expected results or baseline outputs.