Murnitur is an observability platform for large language models (LLMs) that enables teams to monitor token usage, costs, and latency while providing real-time alerts for performance anomalies. The platform enhances LLM applications by ensuring compliance and safety through features like Murnitur Shield, which protects against harmful inputs and outputs.
Funding
Funding not disclosed
Founders
Product
Problem
Developing and deploying large language model (LLM) applications requires careful monitoring of token usage, costs, and latency to ensure optimal performance and cost efficiency. Additionally, LLM applications are vulnerable to harmful inputs and outputs, necessitating robust safety and compliance measures.
Solution
Murnitur is an observability platform designed to help teams monitor and optimize their LLM applications. The platform provides real-time tracking of token usage, costs, and latency, enabling users to identify performance bottlenecks and unexpected expenses. Murnitur also offers a suite of tools for evaluating LLM performance, including anomaly detection and comparison of different models and prompts. Murnitur Shield protects LLM applications from harmful inputs and outputs, acting as a firewall for data and ensuring compliance with safety standards.
Target Audience
Murnitur is designed for LLM application developers, data scientists, and AI engineers who need to monitor, evaluate, and secure their LLM-powered applications.
Features
- Real-time monitoring of token usage, costs, and latency with two-line SDK integration
- LLM evaluation tools for assessing input and output quality using customizable metrics
- Version control for models and prompt templates, enabling tracking and comparison over time
- Murnitur Shield for protection against harmful inputs and outputs through continuous testing and real-time evaluation
- Prompt engineering tools for creating reusable prompts with version control
- Dataset generation from traces to improve LLM accuracy and relevance
- Customizable alerts and notifications for unexpected events, such as cost overruns, via Slack and email
- Support for leading LLM providers, frameworks, and vector databases