Unified data platform
From ingestion, embedding models, and hybrid retrieval to visualization and workflow automation across every data type, Elastic provides a unified data platform to ship context-driven AI applications at scale.
Explore Elastic's capabilities
One platform for every data type
Elastic provides a unified platform for every data type — documents, vectors, logs, metrics — with one schema and one query language. Purpose-built index modes reduce storage costs without rearchitecting.
Retrieve accurate context, efficiently
Get accurate context efficiently, even from data outside Elastic. Keyword and semantic search are combined natively with Jina AI. No merging results. No accuracy lost.
Ship context-driven applications at scale, faster
Built-in skills and tools for agentic search create multi-step agents without code. Accelerate agent response with the Context Engine.
Deploy anywhere, control everything
Run Elastic in any environment your organization requires — cloud, on-prem, or serverless. Meet data residency, sovereignty, or compliance requirements without fragmenting your data or infrastructure.
Retrieve accurate context. Ship faster at scale.
Elastic's unified data platform combines Elasticsearch, Jina AI models, Elastic Context Engine, Elastic Workflows, and Kibana — built to work together, out of the box.
Elasticsearch: Search database for AI and more
Elasticsearch is the search database for AI. Unify documents, vectors, logs, and metrics together in a single cluster: one schema, one query language, with purpose-built index modes for every workload.
Streams: Managed data pipelines for telemetry
Streams gets you investigating in minutes — ingesting, filtering, parsing, and enriching any data, structured or not. Store on your own terms with zero ops and fully managed infrastructure.
Jina AI: Preserve meaning from documents to answers
Frontier-grade Jina AI embedding and reranking models run in cloud, on-premises, or via Elastic Inference Service — at a fraction of the compute cost. Jina Reader and OCR extract meaning at ingestion.
Context Engine: Accurate, complete, and fresh context
Elastic Context Engine connects any enterprise source to build accurate agents. Sync every turn or on a schedule, precompute context for fast and low-cost cross source retrieval, and constantly improve accuracy with built-in evals.
Kibana: Real-time dashboard and insights
Search, analyze, visualize, and alert from a single UI. Query data in real time with built-in machine learning, geospatial analysis, and AI-powered correlation. Built for speed and scale.
Workflows: Native automation for Elasticsearch
Built natively into the platform, Elastic Workflows automates scripted tasks and uses AI-driven reasoning for complex ones, triggered from events, schedules, or on demand. No external tools or separate installs required.
AutoOps: Instant insights, intuitive ops
Real-time issue detection, automated root cause analysis, performance recommendations, and cost insights for every Elasticsearch cluster — free for all users, with zero setup required.
One platform, trusted by teams worldwide
"One of the key reasons is the sheer volume of agreements we handle — millions daily. There’s no other company in the world that can manage this level of activity."
"One of the key reasons is the sheer volume of agreements we handle — millions daily. There’s no other company in the world that can manage this level of activity."
Hiral Shah, Director of Product
<1 min
Less than a minute to find a specific vendor agreement
1B
More than 1 billion users across 180 countries, processing millions of agreements daily
Frequently asked questions
What is the Elastic platform?
Elastic's unified data platform combines Elasticsearch, Jina AI models, Context Engine, Elastic Workflows, and Kibana to power your most business critical applications from RAG and customer service to Observability and Security. Elastic covers everything from data ingestion and storage to retrieval, visualization, and agentic orchestration, built to work together out of the box, without assembling a pipeline of separate tools. Deploy in any environment your organization requires — cloud, on-premises, or serverless.
What can I build with Elastic?
Build RAG pipelines, AI agents, and search, observability, and AI applications faster. Access chunking, embedding models, hybrid retrieval, and reranking out of the box. Retrieve accurate context from your data at petabyte scale in a single query with keyword and semantic understanding combined natively. Connect any MCP or A2A-compatible client to your data with one config file, no custom orchestration code to write. Query and visualize every signal type — documents, logs, metrics, and vectors — from a unified data platform.
What makes the Elastic platform technically differentiated?
Elastic is the unified data platform for context engineering and AI, providing search database, AI models, agent orchestration, and operational visibility — all built-in and integrated. Elasticsearch combines keyword and semantic retrieval in a single query at petabyte scale. Jina AI models run natively — no embedding service to deploy. Purpose-built index modes reduce storage costs without rearchitecting. Native MCP and A2A servers connect any AI client with one config file. Deploy in any environment — cloud, on-prem, or serverless — with full capability parity across all of them.
What deployment options does Elastic offer?
Elastic offers five deployment options — Elastic Cloud Serverless, Elastic Cloud Hosted, Elastic Cloud Enterprise, Elastic Cloud on Kubernetes, and self-managed — giving you the flexibility to choose the deployment model that best fits your security, compliance, and scalability requirements. Every deployment option runs the same platform — no feature is locked behind a specific deployment model.
How does the Elastic platform support generative AI and RAG applications?
The Elastic platform provides accurate retrieval, built-in embeddings, reranking, production-scale vector performance, and agent orchestration — all in one place. Elasticsearch combines keyword and semantic search in a single query, grounding LLM responses in accurate, relevant context from your private data. Jina AI models handle embeddings and reranking natively — no separate embedding service required. Elastic Workflows connects any MCP or A2A-compatible AI client with one config file. Prebuilt RAG pipeline templates get you from prototype to production without assembling tools from scratch.
How does the Elastic platform integrate with my existing stack?
The Elastic platform meets you where you are, without rearchitecting your existing architecture. You can integrate your existing stack through:
- AI clients and agents: Any MCP or A2A-compatible client connects to your Elasticsearch data through a native server with one config file and no custom code. Claude, ChatGPT, and Cursor connect natively.
- Agent frameworks: LangChain, LangGraph, AutoGen, and Mastra are supported natively.
- Data sources: 200+ built-in connectors bring external data onto the platform. Native OTLP ingest and Prometheus API support mean your existing observability pipelines connect without modification.
- APIs: Nearly 500 REST API endpoints give you full programmatic control over every platform capability.
How is the Elastic platform priced?
Pricing depends on your deployment model and workload. Elastic Cloud Hosted uses resource-based pricing — you pay for compute and storage, not per user or per GB ingested. Elastic Cloud Serverless is consumption-based, so you pay only for what you use. The open source Elasticsearch and Kibana software is free to self-host.