Bring the power of Large Language Models (LLMs) to your applications

Composable Prompts is the only API-first platform for building AI/LLM applications. We enable enterprise teams to automate and augment their line of business processes and applications with LLM-powered tasks.

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Composable Prompts is more than an LLM application development framework

The comprehensive platform enables enterprise teams to design, test, deploy and operate LLM-powered tasks to drive efficiency, improve performance, and lower costs.

Compose powerful prompts, and reuse them across your applications

Reuse tested prompts and compose them to create more complex versions. In addition, prompts come with schemas in and out to strengthen quality — thanks to type safety.

Leverage multiple AI models and environments

Easily test prompts in different environments including OpenAI, AWS Bedrock, Replicate, Groq, Google Vertex AI, Hugging Face, and Together AI. Use different models for different use cases. Prompts are automatically converted to the target's model format without any change. Want to assign a task to Llama2 instead of GPT4 in 30% of cases? No problem.

Optimize performance and cost while keeping the data fresh

You can design an intelligent cache strategy for each interaction, so the result can be reused, varied on a set of keys, or used only in a certain percentage of calls.

Discover the Platform

Easily Design LLM Prompts & Tasks

Governance

End-to-end governance of LLM agents and LLM-powered tasks. Know which task is deployed, which application uses it, what it does, and what data has been sent & received.

Security

Dramatically reduce the attack surface. Fine-grained security, keys restricted to tasks, short-lived restricted public keys, audit history with advanced search in runs, automated key rotation, etc.

Orchestration

Execute tasks on any inference provider and model through easy to use API endpoints. Use and reuse results, thanks to persistence, indexing, and search.

Test Multiple Models & Safely Deploy in Production

Virtualization

Create Synthetic LLM by mixing several LLMs, and choosing the appropriate distribution strategy: weighted load balancing, multi-head execution, mediator, or intelligent routing.