tensorzero

It combines an LLM gateway (for routing, caching, and load balancing across providers), observability (structured logging of every inference call with inputs, outputs, latency, and cost), and a data flywheel that automatically uses production data to fine-tune and evaluate models creating a closed loop between deployment performance and model improvement.

The gateway component handles multi-provider routing with fallback logic, semantic caching to reduce redundant inference costs, and A/B testing between model versions.

All traffic is logged with full context to a local database, enabling systematic analysis of which prompts, models, and configurations produce the best outcomes for specific tasks.

The optimization layer then uses this accumulated production data to run automated fine-tuning jobs, prompt optimization, and model selection experiments capabilities that are typically only available to organizations with dedicated ML platform teams.

TensorZero is open-source under the Apache 2.0 license and designed to be self-hosted within an organization's own infrastructure, ensuring that production AI traffic and associated business data remain private.

It is built in Rust for performance and reliability, with a Python SDK for integration and a web UI for observability dashboards and experiment management.

The platform targets engineering teams that want to move beyond prototype-quality AI integrations toward systematically measured and improving production systems, without building the optimization infrastructure from scratch.