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Prompt engineering, management, and LLM observability platform.
High-performance open-source vector database for production AI retrieval and RAG, for teams needing scale, hybrid search, or self-hosting.
Developer framework and LlamaParse service for parsing documents and building AI agents and RAG workflows over them.
Infrastructure company building large-scale GPU data centers and compute for AI, including Anthropic's compute buildout.
Managed AI ranking engine powering personalized search, recommendations, and feeds via a SQL-like query language.
No public pricing
No public pricing
No public pricing
No public pricing
Free trial available
- ✦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
- ✦Hybrid dense and sparse vector search (BM25, SPLADE, miniCOIL)
- ✦Advanced metadata filtering applied during search traversal
- ✦Multivector support for multimodal retrieval
- ✦Reranking with score boosting and late-interaction models (ColBERT, MMR)
- ✦Flexible deployment: cloud, hybrid, private, or edge
- ✦Rust-based engine optimized for low-latency, high-scale search
- ✦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
- ✦Large-scale GPU and data-center infrastructure for AI
- ✦Power acquisition and data-center design/build
- ✦Fast deployment (gigawatts in ~6 months)
- ✦Operates both hardware and software stack
- ✦ShapedQL SQL-style query interface for retrieval and ranking
- ✦Hybrid semantic and keyword search
- ✦Continuous learning from user feedback signals
- ✦30+ native data connectors for warehouses and streams
- ✦Sub-50ms query latency
- ✦Python and TypeScript SDKs plus MCP support
- →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
- →Building retrieval-augmented generation (RAG) pipelines
- →Powering AI recommendation and semantic search systems
- →Enterprises needing on-prem or hybrid deployment for compliance
- →AI agent platforms needing fast contextual retrieval at scale
- →Parse complex documents for AI apps
- →Build RAG and agent workflows
- →Automate invoice and claims processing
- →Search across technical documents
- →Training and running large AI models at scale
- →Provisioning GPU compute for AI labs
- →Building dedicated AI data-center capacity
- →Personalizing 'for you' content feeds
- →Building product recommendation systems
- →Powering RAG retrieval with behavioral ranking
- →Adding hybrid search to an e-commerce site