Elasticsearch Vector Search vs Graphiti

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	Elasticsearch Vector Search RAG	Graphiti RAG
Tagline	Hybrid vector + keyword search in the enterprise-grade Elasticsearch engine	Open-source temporal knowledge graph framework for building agent memory that updates in real time.
Category	RAG	RAG
Pricing	Freemium· Free self-managed open-source core; Elastic Cloud Serverless usage-based (VCU-priced); Elastic Cloud Hosted from ~$95/mo (Standard) with Gold/Platinum/Enterprise tiers; custom Enterprise pricing.	Freemium· Open-source (Apache 2.0); managed Zep Cloud sold separately
Model	BYO embeddings (OpenAI, Cohere, Hugging Face, Mistral, Bedrock, Vertex, Azure) plus Elastic's built-in ELSER sparse model and E5 dense model	Multi-model
Editorial score	8.7 / 10	7.2 / 10
Use cases	RAG chatbot over enterprise docsHybrid semantic + keyword product searchSupport-ticket similarity retrievalLegal and compliance document searchLog and observability semantic explorationRecommendation and related-content rankingMultimodal search with image embeddingsKnowledge-base grounding for internal LLM assistants	agent-memorytemporal-knowledge-graphshybrid-retrievallong-running-agentscontext-engineering
Pros	True hybrid retrieval — BM25 + dense + sparse (ELSER) in one query with reranking Filters, aggregations, geo, and time-series in the same index, so one cluster serves search + analytics + RAG `semantic_text` field handles chunking and embedding calls automatically at ingest Better Binary Quantization slashes vector RAM footprint dramatically for billion-scale corpora Broad embedding-provider and framework support (OpenAI, Cohere, Bedrock, Vertex, LangChain, LlamaIndex) Enterprise-grade RBAC, field/document-level security, and audit — rare among vector DBs Open-source core with self-managed, cloud, and serverless deployment paths	Real-time incremental graph updates without batch recomputation Temporal model tracks when facts were valid, not just current state Hybrid semantic + keyword + graph search out of the box Open source with an active commercial backer in Zep MCP server lets Claude Desktop and Cursor read agent memory directly
Cons	Steeper learning curve and operational overhead than purpose-built vector DBs like Pinecone or Qdrant JVM cluster tuning (heap, shards, HNSW parameters) is non-trivial at scale Cloud Hosted pricing is opaque compared to per-vector pricing of newer competitors License change (Elastic License v2 / SSPL) blocks some managed-service resellers Latency-sensitive pure-vector workloads can be beaten by specialised ANN-only engines	Requires running a graph database like Neo4j LLM calls during ingestion add cost vs plain vector RAG Steeper learning curve than drop-in RAG libraries
Website	www.elastic.co	help.getzep.com

Pick Elasticsearch Vector Search if

✅ True hybrid retrieval — BM25 + dense + sparse (ELSER) in one query with reranking
✅ Filters, aggregations, geo, and time-series in the same index, so one cluster serves search + analytics + RAG
✅ `semantic_text` field handles chunking and embedding calls automatically at ingest
✅ Better Binary Quantization slashes vector RAM footprint dramatically for billion-scale corpora

Pick Graphiti if

✅ Real-time incremental graph updates without batch recomputation
✅ Temporal model tracks when facts were valid, not just current state
✅ Hybrid semantic + keyword + graph search out of the box
✅ Open source with an active commercial backer in Zep

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