TensorZero
Open-source LLM infrastructure for gateway, observability, optimization, and experimentation
About TensorZero
TensorZero is an open-source LLMOps platform that unifies an LLM gateway, observability, optimization, evaluations, and experimentation into a single stack. Written in Rust for extreme performance (<1ms P99 latency overhead at 10,000 QPS), TensorZero provides a unified API for every major LLM provider including OpenAI, Anthropic, Google, AWS Bedrock, Azure, Mistral, and many more. The platform creates a feedback loop for optimizing LLM applications — turning production data into smarter, faster, and cheaper models through prompt optimization, fine-tuning, reinforcement learning, and distillation. TensorZero Autopilot acts as an automated AI engineer that analyzes observability data, optimizes prompts, sets up evaluations, and runs A/B tests. Backed by $7.3M in seed funding and 11K+ GitHub stars, TensorZero is used by companies from frontier AI startups to the Fortune 50.
Pros & Cons
Pros
- Fully open source (Apache 2.0) and 100% self-hosted — no vendor lock-in
- Extremely fast Rust-based gateway with sub-millisecond latency overhead
- Comprehensive LLMOps platform: gateway + observability + optimization in one stack
- Supports every major LLM provider through unified API
- Unique optimization loop: production feedback drives prompt and model improvements
- GitOps-friendly configuration for infrastructure-as-code workflows
Cons
- Self-hosted only — no managed cloud offering for teams wanting zero ops
- Requires ClickHouse setup for observability data storage
- Autopilot (paid) needed for automated optimization workflows
- Steep learning curve for teams new to LLMOps concepts
- Relatively new product with smaller community vs established alternatives
Key Features
Unified LLM gateway with <1ms P99 latency (written in Rust)
Supports all major LLM providers through a single API
Built-in observability with structured inference traces
A/B testing and traffic routing between model variants
Prompt optimization, fine-tuning, and model distillation
LLM-as-judge evaluations and heuristic benchmarks
Multi-step workflow support with episode-level feedback
ClickHouse-backed analytics for real-time monitoring
TensorZero Autopilot: automated AI engineer for optimization
Production LLM application optimization
Use case
Multi-provider LLM gateway and failover
Use case
A/B testing different models and prompts
Use case
Fine-tuning models on production feedback
Use case
Cost reduction through model distillation
Use case
LLM observability and monitoring
Use case
OpenAI integration
Integration
Anthropic integration
Integration
AWS Bedrock integration
Integration
AWS SageMaker integration
Integration
Azure OpenAI integration
Integration
Google Vertex AI integration
Integration
Google AI Studio (Gemini) integration
Integration
Groq integration
Integration
Mistral integration
Integration
Fireworks integration
Integration
Together AI integration
Integration
OpenRouter integration
Integration
vLLM integration
Integration
xAI integration
Integration
Hyperbolic integration
Integration
ClickHouse integration
Integration
Pricing
- 100% self-hosted and open source (Apache 2.0)
- LLM gateway with <1ms P99 latency
- Built-in observability with ClickHouse
- Prompt and model optimization
- A/B testing and experimentation
- Evaluations and benchmarking
- All LLM provider integrations
- Automated AI engineer for LLM optimization
- Analyzes inference data for error patterns
- Auto-recommends models and strategies
- Generates and refines prompts from feedback
- Drives fine-tuning and distillation workflows
- Sets up evaluations and prevents regressions
- Runs automated A/B tests