A coworker today, not a six-month integration.
Elasticsearch is a search engine — kNN, ELSER sparse retrieval, aggregations, and the scoring control to build almost any search product. ZenSearch is an AI coworker that ships the whole stack out of the box: connectors, identity-federated permission sync, RAG, agents that take governed action, and guardrails. With Elastic you assemble the AI layer yourself; with ZenSearch it's already assembled. (For the record, ZenSearch is not built on Elasticsearch — it uses Qdrant for vectors and PostgreSQL for metadata.)
The Short Version
Choose ZenSearch if you need:
- A complete AI coworker — not search primitives you assemble into one
- RAG, agents, chat, and citation grounding working on day one
- Production in hours, not a multi-month build of ingestion, RAG, and UI
- Built-in connectors with identity-federated permission sync
- Governed agent actions — risk tiers T0–T5, approvals, and guardrails
Choose Elasticsearch if you need:
- A general-purpose search engine to build a custom product on
- Fine-grained control over indexing, mapping, and query DSL scoring
- Observability and log analytics at scale (the ELK stack)
- Massive scale — billions of documents with hands-on cluster tuning
- An existing Elasticsearch investment and the team to extend it
Feature by Feature
ZenSearch vs Elasticsearch, in detail.
Agents & AI Capabilities
| Capability | ZenSearch | Elasticsearch |
|---|---|---|
Hybrid search (dense + sparse) | ||
Vector search (kNN) | ||
Faceted search & filtering | ||
Cross-encoder reranking Elasticsearch provides APIs; you build the reranking pipeline | DIY | |
Retrieval augmented generation Elastic provides building blocks (ESRE); you assemble the RAG pipeline | DIY | |
Cited answers with sources | DIY | |
AI agents with tool calling Elasticsearch is a search engine, not an AI platform | ||
Custom agent builder | ||
Approval-gated write actions Every consequential write pauses on a per-team policy and routes to the right human | Risk tiers T0–T5 | Not available |
Cross-surface approvals | Slack, Teams, web | Not available |
Automations (cron / email / event / meeting) | Not available | |
Multi-agent delegation Out-of-the-box agent-to-agent handoff via discover_agents + delegate_to_agent — no DIY orchestration required | ||
Self-improving procedural memory Agents learn reusable workflows from successful sessions. Elasticsearch has no agent layer to attach this to | ||
Observational memory (long-conversation compression) 80%+ token compression on long sessions via cacheable LLM-extracted summaries | ||
NL-to-SQL database queries | ||
Answer confidence scoring | ||
Conversational chat | ||
Guardrails (hallucination, PII, injection) |
Deployment & Time to Value
| Capability | ZenSearch | Elasticsearch |
|---|---|---|
Cloud (SaaS) | ||
On-premise deployment | ||
Air-gapped deployment | ||
Docker & Kubernetes | ||
Bring your own LLM Elasticsearch doesn't include LLM integration natively | N/A | |
Time to production Elasticsearch requires building ingestion, RAG, and UI layers | Hours | Weeks/Months |
No infrastructure expertise needed Elasticsearch requires cluster management, shard tuning, and index optimization |
Data Connectors
| Capability | ZenSearch | Elasticsearch |
|---|---|---|
Confluence | Connector framework | |
Slack | Connector framework | |
GitHub | Connector framework | |
Google Drive | Connector framework | |
SharePoint | Connector framework | |
Jira | Connector framework | |
Notion | Community | |
Salesforce | Connector framework | |
SAP | ||
HubSpot | Community | |
PostgreSQL / MySQL / SQL Server ZenSearch: NL-to-SQL with schema discovery. Elastic has database connectors for ingestion only. | ||
Permission-aware connectors Elastic connectors ingest content; permission sync requires custom implementation | Partial |
Security & Compliance
| Capability | ZenSearch | Elasticsearch |
|---|---|---|
SOC 2 Type II | In progress | |
Document-level RBAC Elasticsearch has document-level security but requires manual index-level ACL setup | DIY | |
Permission-aware search | DIY | |
End-to-end encryption | ||
Audit logging | ||
Input/output guardrails | ||
Prompt injection detection | ||
PII detection & filtering |
Where ZenSearch Stands Apart
A coworker out of the box vs building blocks.
Elasticsearch is genuinely powerful — the right pick when you need a custom search product with fine-grained scoring control and have the team to build the AI layer. These are the places where ZenSearch ships the whole coworker instead, so you don't.
AI-native coworker vs DIY
ZenSearchZenSearch is an AI-native platform: RAG pipeline, agents, guardrails, chat, citation grounding, and connectors are built in. Deploy and start working in hours, not months.
AI-native coworker vs DIY
ElasticsearchElasticsearch is a search engine. Building an enterprise AI experience on top means assembling ingestion pipelines, embedding generation, RAG orchestration, chat UI, permission sync, and guardrails — each its own engineering effort.
Zero infrastructure expertise
ZenSearchZenSearch runs as a single Docker Compose stack or managed SaaS. No cluster management, shard tuning, or index optimization required. Try it, then scale as you grow.
Zero infrastructure expertise
ElasticsearchElasticsearch clusters require real operational expertise: shard sizing, replica configuration, index lifecycle management, JVM tuning, and monitoring. Elastic Cloud reduces this but adds cost and vendor coupling.
Governance on every action
ZenSearchEach tool carries a risk tier (T0–T5). Consequential writes pause the run and route an approval to the right human — in Slack, Teams, or on the web — alongside input/output guardrails for prompt injection, PII, and hallucination.
Governance on every action
ElasticsearchElasticsearch has no agent layer and no AI guardrails. If you build a RAG or agent pipeline on top, every safety check and approval gate is yours to implement separately.
NL-to-SQL over your databases
ZenSearchAsk questions in plain English against PostgreSQL, MySQL, ClickHouse, and SQL Server. Schema discovery, read-only execution, and query validation are built in.
NL-to-SQL over your databases
ElasticsearchElasticsearch is not a relational database. Querying existing databases means building a separate integration layer, or ingesting that data into Elasticsearch indices first.
Get Started
See the
difference.
Try ZenSearch in the live demo, or talk to our team about a deployment shaped to your environment.