Elastic AI Search
Elastic solution that combines vector and keyword search with LLM retrieval to power in app search and support bots on Elastic Cloud with usage based pricing.
Overview
Elastic AI Search packages Elasticsearch vector and sparse retrieval tools with hybrid ranking pipelines so developers deliver fast relevant in app search. Teams index documents with text fields metadata and embeddings then compose queries that mix BM25 sparse vectors HNSW dense vectors and reranking models for better top results. Retrieval output can ground LLM responses so support bots and assistants cite sources from the same index.
Elastic Cloud handles scaling snapshots and security while plugins address ingest pipelines synonyms and grammar rules. Observability ties search quality to business metrics through click analytics and relevance tuning dashboards and serverless options simplify ops for spiky workloads. Pricing is usage based in Elastic Cloud with a free trial and marketplace billing paths which helps align spend to traffic rather than fixed seats.
Enterprises adopt the stack to consolidate logging and search while product teams ship a single index that powers help docs web and app search.
Key features
- Hybrid retrieval pipeline design: mix BM25 sparse vectors dense vectors and reranking so top results balance lexical match and semantic intent at query time
- Embeddings ingestion at scale: index vectors with HNSW graphs and filters so searches remain fast while honoring document level permissions and facets
- Grounding for LLM answers: retrieve cites and snippets from the same index so assistants answer with evidence and limit hallucinations in production
- Observability and analytics: track clicks zero results and query classes then tune synonyms boosts and rules to improve conversion and case deflection
- Elastic Cloud resilience: autoscaling snapshots and security templates reduce ops toil while serverless options smooth costs for bursty workloads
- Enterprise controls and SSO: namespace data by tenant apply document level security and integrate identity providers for regulated environments
- Ingest pipelines and transformations: clean fields extract entities and normalize text at ingest so queries stay precise across messy sources
- Marketplace billing options: purchase via cloud providers to use commit funds and simplify invoicing across teams who already standardize on a vendor
Best for
- In app search for SaaS where users need instant results with synonyms filters and typos handled without leaving the product experience for support
- Help center and agent assist where hybrid retrieval powers self help and grounds suggested replies to reduce case volume and increase first contact resolution
- Ecommerce and catalog search where vectors improve discovery for vague queries while filters and facets preserve precision for power shoppers and ops
- Data portals and documentation search where devs index code examples guides and API refs then measure click quality and tune queries over time
- Internal knowledge bases where permissions and tenants matter and teams need audit trails while keeping latency low under bursty traffic
- Site wide search consolidation where one index powers web mobile and docs with shared analytics and query rules for consistency across channels
- Chatbot retrieval augmentation where Elastic feeds snippets and citations to LLMs so answers cite sources and follow compliance guidelines
- Event search for logs metrics and traces where operational teams reuse Elastic skills and tooling across observability and product search stacks
Capabilities
Vectors and Text
Store text metadata and embeddings together with HNSW graphs and document security so results stay fast and permission aware at scale.
Hybrid Ranking
Combine lexical sparse and dense vectors with rerankers to capture intent and exactness which improves top results and downstream LLM grounding.
Analytics and Rules
Track search sessions add synonyms boosts and rules then watch conversion or deflection metrics move as relevance improves.
Cloud and Serverless
Rely on Elastic Cloud autoscaling snapshots and marketplace billing so ops teams right size clusters and smooth costs during spikes.
Frequently Asked Questions
What does pricing look like on Elastic Cloud?
Elastic AI Search is billed on Elastic Cloud as usage based resources with a free trial and marketplace options that align spend to workload traffic.
Can we keep permissions in place for results?
Document level security and tenant namespacing ensure users see only authorized content which is vital for internal and multi tenant apps.
How do we ground LLM answers safely?
Retrieve top passages from the index then include cites in responses to reduce hallucinations and keep compliance teams comfortable.
Do we need a separate vector database?
No you can store text and vectors in Elasticsearch which simplifies ops and lets hybrid pipelines run in one place with shared analytics.
How is relevance tuned over time?
Click analytics zero result reports and query classes guide synonym and rule updates and experiments with rerankers to lift KPIs.
Is serverless an option for spiky loads?
Yes serverless options reduce ops toil and maintain predictable latency for bursty or seasonal search traffic.
What scale can this handle?
Elasticsearch powers large public sites and internal portals with billions of docs and sustained query volume when sized correctly.
How do we buy through our cloud provider?
You can purchase via cloud marketplaces to use existing commit funds and centralize invoicing with other platform services.
