Elastic
Search and analytics platform behind Elasticsearch, with vector search
About Elastic
Elastic develops Elasticsearch, a distributed search and analytics engine, along with Kibana and solutions for search, observability and security. It supports vector search and retrieval for generative AI applications and is offered as Elastic Cloud or self-managed software. The company is publicly listed.
Common use cases
- RAG applications
- Enterprise search
- Log and observability data
- Security analytics
Social listening
What people are saying about Elastic
Reviewers value search speed, scale and Kibana, and buyers typically plan for pricing, documentation, tuning and licensing scope.
What stands out
- Fast search results and handling of large volumes.
- Kibana is a strong option for dashboards and monitoring.
- Connects readily with third-party tools and cloud storage.
- Self-hosted or managed options help manage cost.
Things to consider
- Worth modeling pricing, including storage, early on.
- Ask about documentation and support for customized needs.
- Plan performance testing for very large scale and frequent updates.
- Teams typically budget time to learn the query language for advanced queries.
Sources (1)
- Elasticsearch pros and cons (PeerSpot) Review site
Themes summarized from public reviews, community discussion and press coverage as of November 2026. They reflect public discussion, not a rating or an endorsement by AI Connect.
Products and services
Elasticsearch
Distributed search, analytics and vector database.
Kibana
Visualization and exploration interface.
Elastic Cloud
Managed service on major clouds.
Elastic Observability and Security
Solutions built on the same data platform.
Key features
- Full-text search
- Vector search
- Hybrid retrieval
- Log analytics
- Machine learning
- Role-based security
Integrations
- AWS
- Google Cloud
- Microsoft Azure
- OpenAI
- LangChain
- Kafka
- Logstash
Elastic alternatives
FAQs
Is it open source?
Elasticsearch is available under open source licensing options, with commercial features in paid tiers.
Can it do RAG?
Yes, it supports vector and hybrid search for retrieval.
How is it deployed?
As Elastic Cloud or self-managed.

Compare with similar providers
All Data Infrastructure providers- ActiveloopMultimodal AI data lake and vector store (Deep Lake)
AerospikeReal-time NoSQL database built for low-latency, high-throughput workloads- ApertureDataMultimodal database for AI with vector search and graph
- ArangoGraph-native multi-model data platform for AI applications and agents
- ArangoDBMulti-model database combining graph, document and search
- CouchbaseDistributed NoSQL document database with cloud service and mobile sync
- DataStaxCassandra-based database platform with vector search for generative AI
- GraphwiseKnowledge graph, semantic AI and GraphRAG software (merger of Ontotext and Semantic Web Company)
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