OpenInference
OpenInference is developing an open-source observability platform that brings structured tracing and evaluation capabilities to AI applications. The platform reshapes how models are organized—including attention mechanisms, context selection, and weight storage—to run efficiently across different hardware accelerators beyond GPUs. The company shares its engineering progress openly as it builds the platform.
- Developer Tools
- Artificial Intelligence
- Data & Analytics
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Developers building AI applications often face significant challenges in monitoring, debugging, and evaluating their systems' performance. The lack of standardized observability tooling for the full AI stack makes it difficult to trace issues, assess model quality, and understand system behavior in production environments.
Solution
OpenInference is actively developing an open-source platform that brings structured observability and evaluation capabilities to AI applications. The platform provides tooling that enables developers to trace model inference, analyze system behavior, and evaluate AI outputs in a systematic way. Additionally, OpenInference reshapes models to fit any accelerator by modifying how attention is organized, how context is selected, and how weights are stored, addressing performance gaps on non-GPU hardware. By sharing engineering knowledge and progress openly, OpenInference is building a collaborative ecosystem where developers can contribute to and benefit from standardized AI observability practices. The project focuses on making advanced evaluation and tracing accessible to teams building production AI systems.
Target Audience
The primary audience is AI engineers, machine learning practitioners, and software development teams who need observability, tracing, and evaluation tools for their AI applications, as well as teams deploying models on non-GPU accelerators.
Features
- Open-source observability framework designed specifically for AI and machine learning applications
- Tracing capabilities for monitoring model behavior and understanding inference pipelines
- Evaluation tooling that helps assess the quality and performance of AI outputs
- Model reshaping technology that optimizes attention organization, context selection, and weight storage for compatibility with any accelerator
- Community-driven development with engineering progress shared transparently