What is TensorZero · open-source LLM infrastructure

TensorZero is an open-source LLMOps platform designed to streamline the development and deployment of LLM applications. It offers a unified API gateway, comprehensive observability, robust evaluation tools, prompt and model optimization capabilities, and built-in experimentation features like A/B testing. Unlike fragmented solutions, TensorZero provides a cohesive environment for managing the entire LLM lifecycle. It leverages an automated AI engineer, Autopilot, to analyze LLM performance, set up evaluations, optimize prompts, and run A/B tests. This platform is ideal for AI startups and enterprises seeking to improve LLM performance, reduce costs, and accelerate innovation. TensorZero is used by companies ranging from frontier AI startups to the Fortune 10 and fuels ~1% of global LLM API spend today.

TensorZero · open-source LLM infrastructure 's Core features

Unified LLM Gateway

Provides a single API endpoint for accessing various LLM providers, abstracting away the complexities of different APIs. This reduces vendor lock-in and simplifies switching between models. Achieves <1ms p99 latency, ensuring fast response times for your applications. Supports all major LLM providers, including OpenAI, Anthropic, and Cohere.

LLM Observability

Offers comprehensive monitoring of your LLM systems, including metrics such as latency, cost, and error rates. Enables programmatic monitoring and a user-friendly UI for easy analysis. Integrates with OpenTelemetry for seamless data collection and analysis, allowing for proactive identification and resolution of performance issues.

Automated LLM Evaluation

Automates the evaluation process by setting up benchmarks and preventing regressions. Aligns LLM judges to real-world scenarios. Allows for the creation of custom evaluation metrics and supports various evaluation frameworks. This feature helps ensure the reliability and accuracy of your LLM applications.

Prompt and Model Optimization

Provides tools for optimizing prompts, models, and inference strategies to improve quality, cost, and latency. Recommends models and inference strategies based on performance data. Supports fine-tuning, reinforcement learning, and distillation workflows. This feature helps maximize the efficiency and effectiveness of your LLM deployments.

Built-in A/B Testing

Facilitates A/B testing to validate changes, identify winners, and close the feedback loop. Allows for the deployment of different LLM versions and configurations. Provides real-time performance data and insights to guide optimization efforts. This feature enables data-driven decision-making for continuous improvement.

Open-Source LLMOps Platform

Offers an open-source platform that unifies LLM gateway, observability, evaluation, optimization, and experimentation. This allows for greater flexibility, customization, and control over your LLM infrastructure. The open-source nature fosters community contributions and accelerates innovation.

How to use TensorZero · open-source LLM infrastructure

Access the TensorZero documentation and explore the Quick Start guide to understand the platform's core components and functionalities.,2. Sign up for a free account or explore the open-source options on GitHub to access the platform's features.,3. Integrate the TensorZero SDK into your LLM application to leverage the unified API gateway and observability features.,4. Utilize the evaluation tools to benchmark your LLM's performance and identify areas for improvement.,5. Experiment with prompt optimization and model selection to enhance the quality, cost, and latency of your LLM applications.,6. Deploy your LLM application with built-in A/B testing to validate changes and optimize performance.

Use cases of TensorZero · open-source LLM infrastructure

AI Startup

An AI startup uses TensorZero to quickly build and deploy LLM-powered applications. They leverage the unified API gateway to easily switch between LLM providers, the observability features to monitor performance, and the A/B testing capabilities to optimize prompts and models, accelerating their product development cycle.

Enterprise Company

A large enterprise integrates TensorZero into its existing infrastructure to improve the performance and reduce the cost of its LLM-based applications. They use the platform's observability features to monitor LLM usage, the evaluation tools to benchmark performance, and the optimization features to fine-tune models.

LLM Research

Researchers use TensorZero to experiment with different LLMs and prompt engineering techniques. They leverage the platform's evaluation tools to measure the performance of their models and the A/B testing features to validate their findings. This helps them to advance the state-of-the-art in LLM research.

LLM Application Development

Developers use TensorZero to build and deploy LLM-powered applications. They leverage the platform's unified API gateway to access various LLM providers, the observability features to monitor performance, and the A/B testing capabilities to optimize prompts and models.

Who benefits from TensorZero · open-source LLM infrastructure

AI Startups

AI startups benefit from TensorZero's ability to rapidly prototype, deploy, and iterate on LLM-powered applications. The platform's unified API, observability, and A/B testing features enable them to move quickly and efficiently.

Enterprises

Large enterprises can leverage TensorZero to streamline their LLM infrastructure, reduce costs, and improve performance. The platform's comprehensive features provide the tools needed to manage and optimize LLM deployments at scale.

LLM Developers

Developers building LLM-based applications find TensorZero invaluable for simplifying the development process. The platform's unified API, observability, and optimization tools streamline the entire LLM lifecycle, from development to deployment.

ML Engineers

ML engineers can use TensorZero to monitor, evaluate, and optimize their LLM models. The platform provides tools for A/B testing, prompt engineering, and model selection, enabling data-driven decision-making and continuous improvement.