UpTrain is an LLMOps infrastructure platform for teams building and scaling products powered by large language models. It focuses on evaluating quality, monitoring behavior in production, and systematically improving LLM applications over time.
LLM evaluation and experimentation
UpTrain helps teams define response-quality metrics, compare prompts and models, and run A/B experiments. It supports checks for accuracy, robustness, and regressions, and shows how changes in prompt chains affect final outcomes.
- Configure evaluation metrics for LLM outputs
- Compare prompts and model variants
- Run A/B tests and track regressions
Production observability and control
The system collects request/response logs, builds dashboards, and helps detect quality degradation and anomalies. This makes it easier to understand real-world performance under load and identify problematic scenarios faster.
- Centralized logging for LLM interactions
- Dashboards for monitoring quality trends
- Alerts and analysis for anomalies and degradation
Fit into existing infrastructure
UpTrain connects to existing LLM services via API and can be embedded into development pipelines. It also supports deployment in a secure private cloud for enterprise data and security requirements.
- API-based integration with current LLM services
- Works within existing development workflows
- Private cloud deployment options
