KEY ADVANTAGES
Why teams choose SurrealDB over Elasticsearch
Elasticsearch is a dedicated search layer that data must be synced into. SurrealDB makes search an index on the transactional data itself - so relevance, freshness, and consistency come from one system.
HOW IT COMPARES
SurrealDB vs. Elasticsearch
Modern applications need search that is consistent with the data underneath it. Running a search cluster beside the primary database means an ingest pipeline to build, monitor, and reconcile - and search results that lag the source of truth.
Feature
Elasticsearch
SurrealDB
Role in the stack
A secondary search layer. Data is copied in from a primary database through an ingest pipeline you build and monitor.
The primary database. Full-text and vector search are index types on the transactional data itself - nothing to sync.
Architecture
Lucene-based distributed search cluster on the JVM. Data lives in index shards that operators size, allocate, and rebalance.
Multi-model database in a single Rust binary, with compute-storage separation for distributed deployments.
Full-text search
Mature BM25 full-text with analysers and highlighting - its core strength.
Native BM25 full-text with custom analysers and highlighting, queried in SurrealQL alongside every other data model.
Vector search
dense_vector fields with HNSW-based kNN, queried through the search API.
Native HNSW vector indexes, combined with keyword scores, graph traversal, and structured predicates in one statement.
Consistency & transactions
No multi-document ACID transactions. Single-document reads are realtime, but search only reflects writes after an index refresh (near-real-time).
ACID transactions with read-your-writes - search results always reflect committed data.
Relationships
No general-purpose joins or graph traversal. Nested documents, parent-child mappings, and limited ES|QL lookup joins emulate relationships, with well-known performance costs.
Record links and native graph traversal with typed, queryable edges - relationships are first-class.
Live updates
No push-based query subscriptions - clients poll for changes.
Native live queries stream result changes to clients over WebSocket.
Security & permissions
TLS and role-based access in the free tier; document-level and field-level security sit in paid subscription tiers.
Record-level permissions defined in the schema and enforced by the engine - included in the open source core.
Query language
JSON Query DSL plus ES|QL for search and aggregations, with administration through separate REST APIs.
One SQL-like language - SurrealQL - across every model, plus SDKs, REST, WebSocket, and live queries.
Operations
JVM heap tuning, shard sizing, index lifecycle management, and cluster rebalancing as routine operator work.
Single binary, no JVM. Runs embedded, single-node, or clustered without manual shard planning.
On-prem pricing & licensing
Free Basic tier with advanced features gated behind paid self-managed subscription tiers, priced per node and negotiated with sales.
Open source engine, free to self-host with every feature included. Paid support and managed cloud are optional.
MIGRATION
What moving off Elasticsearch looks like
Migration is mechanical rather than architectural: export documents with the scroll or point-in-time API and bulk-insert them into SurrealDB; translate index mappings into table and field definitions; recreate analysers and search settings as analyzer and index definitions; and port Query DSL queries to SurrealQL. Most teams run both systems in parallel behind their search interface until results match, then cut over - and retire the ingest pipeline that kept the two stores in sync. Talk to us about a structured migration workshop for your workload.
TRUSTED BY
Enterprise teams building on SurrealDB
From knowledge graphs to AI assistants - how enterprise teams are building on the context layer.
Samsung
Unlocking insights with knowledge graphs
Samsung Ads uses SurrealDB to build dynamic, real-time knowledge graphs for smarter campaign execution - collapsing three legacy data stores into one.
Read case study
Verizon
AI assistant empowering 10,000 technicians
Verizon uses SurrealDB to power a generative AI assistant for 10,000 field technicians, delivering instant access to documentation, outage updates, and workflows.
Read case study
Tencent
Unified infrastructure monitoring
Tencent consolidated nine backend tools into one real-time monitoring platform powered by SurrealDB's multi-model context graph.
Read case study
PolyAI
High-performance customer service AI powered by RAG
PolyAI connects SurrealDB to Agent Studio for low-latency, customer-controlled RAG across voice AI experiences.
Read case study
FREQUENTLY ASKED QUESTIONS
SurrealDB vs. Elasticsearch
GET STARTED
Evaluate SurrealDB against Elasticsearch
Full-text, vector, graph, and structured data in one engine - with the consistency your search layer never had.
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