Automate model training, evaluation, deployment, and rollback through FloTorch’s customizable workflow engine. Built-in support for asynchronous agents and conditional routing streamlines high complexity LLMOps and FMOps pipelines.

Test models before and after deployment using FloTorch’s evaluation framework—supporting multiple metrics (Response Relevancy, Context Precision, Hallucination Index,Faithfulness, Conciseness, Maliciousness, Context Recall etc. ) and expert-in-the-loop review. Approval gates block underperforming models from progressing through the pipeline.

Access real-time insights into latency, token usage, model drift, prompt completion rates, and more. Easily integrate with Prometheus, Grafana, and your internal observability stack via our metrics SDK.

Enable your models to evolve in production using FloTorch’s active feedback integration and support for continuous fine-tuning strategies. Feedback signals can be routed back into pipelines for dynamic model updates.

Enforce data usage policies, control access to models and datasets, and implement audit logs using FloTorch’s built-in security governance layer. Designed to meet enterprise compliance standards out-of-the-box.

Integrate with data warehouses, vector stores, CI/CD systems, and external LLM providers via our extensible connector system and language-native SDKs (Python, TypeScript). Minimal devops overhead, maximum compatibility.