Dive into the industry's first Search AI Lake

With the new Search AI Lake cloud-native architecture, you get vast storage and low-latency querying, with built-in vector database functionality. All on Elastic Cloud Serverless.

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What makes Search AI Lake different?

Old data lakes were only optimized for storing large volumes of data. But with Search AI Lake, you get all the storage plus the powerful search capabilities of Elasticsearch.

Store, share, and query more data — without compromising performance.

  • Separate compute and storage

    Say goodbye to data tiering and hello to simpler operation. Update and quickly query both more and less frequently searched data at any time. Scale workloads independently. Select and optimize hardware for each use case.

  • Durable and inexpensive object storage

    Take advantage of persistent object storage without needing to replicate indexing operations to one or more replicas. Reduce indexing cost and data duplication to cut down storage expenses.

  • Low-latency querying at scale

    Experience incredibly fast and reliable performance. You get more efficient data caching plus segment-level query parallelization to enable more requests to be pushed to object stores, faster.

Why choose Search AI Lake?

  • Effortless scalability

    Fully decoupling storage and compute enables boundless scale and reliability. Plus high throughput, frequent updates, and interactive querying of large data volumes.

  • Real time, low-latency

    Excellent query performance even when the data is safely persisted on object stores. Segment-level query parallelization and more efficient caching reduce latency.

  • Independent autoscale

    By separating indexing and search at a low level, you can independently and automatically scale to meet the needs of a wide range of workloads.

  • Durable object storage

    Cloud-native object storage provides high data durability, while cutting down on indexing costs and reducing data duplication — for any scale.

  • Optimized for GenAI

    Use RAG to tailor generative AI applications using your proprietary data. Fine-tune AI relevance and retrieval. Rerank with open inference APIs, semantic search, and transformer models.

  • Powerful query and analytics

    Experience faster time to value. Get flexibility with improved performance and scale. All this with a powerful query language, full-text search, and time series analytics to identify patterns.

  • Native machine learning

    Build, deploy, and optimize ML models directly on all data — even historical. Run unsupervised models for more accurate forecasts and near real-time anomaly detections.

  • Truly distributed

    Query data in the region or datacenter where it was generated — from one interface. No need to centralize or synchronize. You can search across clusters and go from data ingestion to analytics in seconds.

Multiple solutions, one powerful platform

Get relevant results at unprecedented speed with open and flexible enterprise solutions. Plus a streamlined developer experience to optimize workflows.

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    Search AI Lake balances search performance and storage cost-efficiently. By separating compute and storage, as well as index and querying, you can seamlessly harness large data sets for retrieval-augmented generation.

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    Security

    Search AI Lake elevates security posture by allowing a seamless analysis of relevant data, even from years past. Enhance anomaly detection, threat hunting, and AI security analytics.

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    Observability

    Search AI Lake enables faster than ever analytics with near-instant queries. Deliver insights in minutes even on petabytes of data by analyzing all your data, at unprecedented speed and scale.

Go serverless with Search AI Lake

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