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Prompt engineering, management, and LLM observability platform.
Pay-as-you-go API aggregating thousands of image, video, audio and LLM models with custom inference hardware for lower per-request cost.
Developer framework and LlamaParse service for parsing documents and building AI agents and RAG workflows over them.
Open-source React/Angular SDK and platform for embedding agentic, generative-UI copilots into apps, Slack and Teams.
Free all-in-one desktop AI app to chat with your documents and run RAG and AI agents fully local and private.
No public pricing
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No public pricing
No public pricing
- ✦Prompt management
- ✦Prompt evaluations
- ✦LLM observability
- ✦Team collaboration
- ✦Version control for prompts
- ✦A/B testing of prompts
- ✦Prompt Registry
- ✦Historical backtests
- ✦Regression tests
- ✦Usage monitoring
- ✦Single API for image, video, audio, 3D and LLM models
- ✦Standardized model addressing across hosted, partner and custom uploads
- ✦Support for LoRAs, ControlNets, VAEs and embeddings on open-source models
- ✦WebSocket and REST access with async webhook delivery
- ✦Pay-per-request billing with no infrastructure to manage
- ✦Raw serverless GPU/CPU compute for custom workloads
- ✦LlamaParse document parsing and extraction
- ✦Open-source framework for AI agents and workflows
- ✦Document indexing for retrieval/RAG
- ✦Prebuilt solutions by industry and use case
- ✦Free starter credits for LlamaParse
- ✦React and Angular frontend SDKs
- ✦Agent-rendered generative UI
- ✦AG-UI agent-user interaction protocol
- ✦Connectors for LangChain and other frameworks
- ✦Pre-built customizable chat/sidebar components
- ✦Slack and Teams integrations
- ✦Thread and state persistence
- ✦Chat with your documents (RAG)
- ✦Runs locally and offline for privacy
- ✦Supports any LLM (local or cloud)
- ✦Built-in AI agents
- ✦Handles PDFs, Word, CSV, codebases
- ✦No-code setup
- →Scaling customer support automation with LLMs
- →Empowering non-technical teams with prompt engineering
- →Building personalized AI interactions
- →Debugging LLM agents
- →Improving content creation processes
- →Managing and monitoring prompts with a team
- →Adding AI image or video generation to an app without managing infra
- →Batching multi-modal generation tasks in one API call
- →Running custom fine-tuned models via Model Upload
- →Cutting inference costs at high generation volume
- →Parse complex documents for AI apps
- →Build RAG and agent workflows
- →Automate invoice and claims processing
- →Search across technical documents
- →Adding an AI assistant to a SaaS product
- →Building agents that render interactive UI
- →Deploying copilots across Slack and Teams
- →Connecting existing agents to a frontend
- →Privately querying your own documents
- →Running local AI without the cloud
- →Building AI agents over your data
- →Using multiple LLM providers in one app