Langwatch is a platform that optimizes large language models (LLMs) by automating prompt selection and model evaluation using the Stanford DSPy framework, enabling teams to achieve quality assurance and performance metrics efficiently. It addresses the lengthy and uncertain process of moving AI applications from proof of concept to production by providing a structured framework for dataset management and real-time performance monitoring.
Funding
$1M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
VVFounders
Product
Problem
Developing and deploying large language model (LLM) applications is a lengthy process, often plagued by uncertainty in AI performance and bottlenecks caused by manual prompt engineering and model selection. The lack of a structured framework for dataset management and real-time performance monitoring hinders the transition from proof of concept to production.
Solution
Langwatch is a platform designed to streamline LLM optimization and quality assurance, enabling AI teams to accelerate the deployment of reliable AI applications. By leveraging the Stanford DSPy framework, Langwatch automates prompt selection and model evaluation, significantly reducing the time and effort required to achieve desired performance metrics. The platform provides a structured environment for dataset management, real-time performance monitoring, and collaborative development, ensuring consistent quality and facilitating the transition of AI applications into production.
Target Audience
Langwatch primarily targets AI teams and engineers building and deploying LLM applications who need to ensure quality, optimize performance, and accelerate time to market.
Features
- Automated prompt and model optimization using DSPy optimizers, including MIPROv2
- Dataset management tools for collaboration and quality standard setting
- Real-time monitoring of quality, latency, cost, and debugging information
- Versioned experiments to track the performance of pipelines, prompts, and models
- Support for various prompting techniques, including ChainOfThought, FewShotPrompting, and ReAct
- Compatibility with multiple LLM models, including OpenAI, Claude, Azure, Gemini, Hugging Face, and Groq
- Integration with LangChain, DSPy, Vercel AI SDK, LiteLLM, and LangFlow
- Customizable quality evaluators and off-the-shelf options
- Enterprise-grade controls, including self-hosted deployment, GDPR compliance, and role-based access controls