Couchbase AI Data Plane
by Couchbase
Agent memory, LLM cache, vector search and an MCP server on one operational database, from cloud to edge.
Couchbase AI Data Plane bundles agent memory, an LLM cache, vector and full-text search, an Agent Catalog and a self-managed MCP server on top of the Couchbase database. It is for enterprises whose agent pilots stall on data plumbing, replacing separate vector stores, caches and document databases with one governed layer.
Couchbase AI Data Plane is a unified data layer for enterprise AI agents that Couchbase made generally available in early July 2026, with coverage dated July 1 to July 3. It combines operational JSON documents, vector search, full-text search, SQL++ queries and caching with four agent-specific pieces: Agent Memory, a persistence layer for session state and short- and long-term context with access controls; an LLM cache that reduces redundant inference calls; an Agent Catalog that makes agent tools discoverable; and an open, enterprise-supported Model Context Protocol server. The pitch, in the words of Couchbase CPSO Barry Morris, is that stitching together separate vector, caching and document stores for every agent was the biggest drag on customers' production timelines. The components run the same way on Capella, Couchbase's managed cloud on AWS, Google Cloud and Azure, and on self-managed clusters, and they extend to the edge through Couchbase Lite 4.1, Edge Server 1.1 and React Native 1.1, which added peer-to-peer sync over Bluetooth. The release shipped alongside Enterprise Analytics 2.2, which federates queries over Apache Iceberg tables without ETL and adds Oracle and SQL Server CDC, with a Trino adapter planned for Q3 2026. Couchbase says it validated the stack with LangGraph, CrewAI and LlamaIndex, and named Agora as a customer. Constellation Research frames the launch as Couchbase repositioning as a broader AI data platform against Oracle and MongoDB in a crowded market. Couchbase itself was taken private by Haveli Investments in September 2025 in a deal valued at about $1.5 billion.
Data platform and architecture leads at Couchbase-using enterprises whose agent projects are blocked on wiring together a vector store, a cache and a document database.
One governed operational layer for agent memory, retrieval, caching and tool access that runs the same in Capella, self-managed clusters and edge devices.
At a Glance
- Category
- Data & Analytics
- Pricing
- Contact for pricing
- Target Market
- CIOs, CTOs, Data Architects, Enterprise Developers
- Deployment
- Hybrid, Multi-cloud, Edge-first
- Founded
- 2011
- Headquarters
- San Jose, USA
Key Features
- ✓Agent Memory
Persists agent session state and short- and long-term context with access controls, working the same across cloud and self-managed deployments.
- ✓LLM cache
Caches model responses inside the operational data layer so repeated prompts skip redundant and billable inference calls.
- ✓MCP server
An open, enterprise-supported Model Context Protocol server gives agents a standard way to read and act on Couchbase data.
- ✓Agent Catalog
Makes agent tools discoverable from one place so teams can reuse and govern tooling across agent workflows.
- ✓Unified vector, full-text and SQL++ querying
Vector search, full-text search and SQL++ run over the same JSON documents, removing the need to sync a separate vector store.
- ✓Enterprise Analytics 2.2 Iceberg federation
Queries Apache Iceberg lakehouse tables without moving data and adds Oracle and SQL Server change data capture.
- ✓Edge and mobile sync
Couchbase Lite 4.1 and Edge Server 1.1 extend the data layer to devices, including peer-to-peer sync over Bluetooth with Wi-Fi fallback.
Capabilities
Use Cases
- •Conversational AI agents
A customer-facing agent stores conversation context in Agent Memory and retrieves it quickly, the scenario Agora cited as its reason for using Couchbase.
- •Consolidating the agent data stack
An enterprise retires a standalone vector database and cache, running retrieval, caching and document storage on one governed Couchbase cluster.
- •Agents grounded in lakehouse data
Agents query Iceberg tables through Enterprise Analytics 2.2 alongside live operational data, without building an ETL pipeline first.
- •Field and offline agents
Mobile or edge applications keep working offline with Couchbase Lite and sync back over Bluetooth or Wi-Fi when connectivity returns.
- •Standardised tool access via MCP
Agents built in LangGraph, CrewAI or LlamaIndex reach Couchbase data through the MCP server instead of custom connectors per framework.
Ideal For
Best For
- ✓Existing Couchbase customers adding agents without introducing a separate vector database and cache
- ✓Agents that need persistent short- and long-term memory with access controls
- ✓Offline or edge agent scenarios that need sync to mobile and edge devices
- ✓Teams that want lakehouse data from Apache Iceberg queryable next to operational data without ETL
- ✓Organisations that must run the same AI data stack in both managed cloud and self-managed environments
Not Ideal For
- ✗Buyers who need published list pricing to compare options, since pricing for the AI Data Plane was not disclosed at launch
- ✗Teams standardised on Postgres or MongoDB who would be adopting a new primary database just to get agent memory
- ✗Projects that depend on the Trino adapter today, which was still planned for Q3 2026 at GA
Integrations
Deployment
Market Analysis
Pros
- ✓Removes the need to stitch separate vector, cache and document stores together for each agent
- ✓Runs identically on Capella and self-managed clusters and extends to edge and mobile
- ✓Framework-agnostic, validated with LangGraph, CrewAI and LlamaIndex, with a standard MCP server
- ✓Iceberg federation lets agents use lakehouse data without ETL
Cons
- ✗Pricing and licensing for the AI Data Plane were not disclosed at GA
- ✗Constellation Research notes Couchbase is entering an increasingly crowded space and that some announced features had limited availability at launch
- ✗The Trino adapter for Enterprise Analytics was still a Q3 2026 roadmap item at GA
- ✗StorageReview cautions the consolidation only pays off if it materially reduces integration overhead while keeping performance and governance
- ✗Older HN threads describe large Couchbase clusters as operationally finicky and needing a dedicated database team
Pricing
AI Data Plane (Capella or self-managed)
Contact for pricing
- ✓Agent Memory, LLM cache, Agent Catalog, MCP server
- ✓Vector, full-text and SQL++ search
- ✓Capella managed cloud or self-managed licence
StorageReview's GA coverage (July 2026) states that pricing and licensing for the AI Data Plane were not disclosed. The components ride on Capella or a self-managed Couchbase licence, so cost follows that existing contract; there is no separate published list price for the agent features. Couchbase's own pricing pages returned 403 when checked on 2026-10-07.
Security & Compliance
Sources
This page was written from 6 sources, 6 on domains other than couchbase.com.
- 1.storagereview.com — couchbase ai data plane hits ga with enterprise analytics 2
- 2.constellationr.com — couchbase plots course be broader ai data platform
- 3.itbrief.news — couchbase launches ai data plane for enterprise agents
- 4.comparethecloud.net — couchbase launches unified ai data plane to unblock stalled
- 5.en.wikipedia.org — Couchbase, Inc.
- 6.hn.algolia.com — hn.algolia.com
Stay Ahead of the Curve
Weekly enterprise AI insights for technology leaders. No spam, no vendor pitches—unsubscribe anytime.
SubscribeRelated Products
Precisely AI Studio
Ready-made data apps, agents and MCP skills for building AI on trusted, enriched data
GoodData.AI
Governed semantic layer and serving plane for AI agents and embedded analytics
Hex
Agentic analytics platform: AI notebooks for data teams and conversational Threads for everyone, grounded in governed semantic context
Unstructured
ETL for unstructured data: turn PDFs, tables, emails and scans into AI-ready JSON for RAG and agents