TensorZero addresses a fundamental gap in the LLM tooling landscape: most gateway and observability tools treat inference as a stateless request-response cycle, missing the opportunity to learn from production data. TensorZero combines routing, observability, and optimization into a single Rust binary that processes LLM requests with sub-millisecond overhead while building a structured dataset of inputs, outputs, and feedback signals. This data flywheel enables continuous improvement through A/B testing of prompts, dynamic in-context learning that adapts examples based on recent successes, and automated fine-tuning pipelines that produce specialized models from production traffic.
The gateway layer supports all major model providers including OpenAI, Anthropic, Google, AWS Bedrock, and any OpenAI-compatible endpoint. Configuration is declarative through TOML files that define functions, variants, and routing strategies. Each function can have multiple variants — different prompts, models, or parameters — with traffic automatically split for experimentation. Built-in metrics track latency, cost, token usage, and custom evaluation scores, with all data stored in ClickHouse for high-performance analytics. The system handles structured outputs through JSON schemas, tool calling, and multi-step inference chains with full observability at each step.
TensorZero's Rust implementation delivers sub-millisecond P99 latency overhead at sustained throughput exceeding 10,000 queries per second, making it suitable for latency-sensitive production deployments. The platform manages approximately one percent of global LLM API spend and is used by Fortune 10 companies. With over 11,000 GitHub stars, $7.3M in seed funding, and a trajectory that briefly made it the top trending repository on GitHub, TensorZero represents the emerging category of LLM platforms that close the loop between inference and optimization.
