Couchbase AI Data Plane
by Couchbase
The operational data layer for production AI agents — persistent agent memory, context retrieval, tools and traces in one platform
Couchbase AI Data Plane is a unified operational data layer for production AI agents that combines persistent agent memory, vector search, semantic caching, an agent tool catalog and a supported MCP server in a single platform. It is built for CTOs and platform teams tired of stitching together separate vector, cache and document stores for every agent they ship.
Couchbase announced general availability of the AI Data Plane on June 30, 2026, repositioning its database as the operational foundation for agentic enterprises. The product — formerly marketed as Couchbase AI Services — unifies four named components: Agent Memory, which provides persistent context that survives sessions and restarts and supports short-term, long-term, semantic and conversational memory types; an enterprise-supported, self-managed MCP server for standardized Model Context Protocol data access; Agent Catalog, which manages prompts, tools and end-to-end traces so agent behavior is discoverable and governable via SQL++; and AI Functions, SQL++ and LLM utilities for tasks such as sentiment analysis. It runs on Couchbase's distributed multimodel architecture — JSON documents, key-value and SQL-for-JSON queries — and adds billion-scale vector search, semantic caching to cut redundant LLM calls, and automated data ingestion and vectorization. Couchbase's pitch is cost and latency: Agent Memory reduces token spend by reusing persistent cumulative context instead of resending whole conversations each turn, while co-located data and caching cut redundant inference calls at sub-millisecond latency. The release consolidates previous Couchbase deployment models into one architecture spanning Capella DBaaS, self-managed on-premises and multicloud, hybrid, edge and air-gapped environments, and is framework-agnostic — validated with LangGraph, CrewAI and LlamaIndex. It ships alongside Enterprise Analytics 2.2, which adds Apache Iceberg lakehouse federation, JWT authentication, change data capture for Oracle and SQL Server, an index advisor and SQL++ UPDATE support; a Trino adapter enabling SQL access via AWS Athena, Amazon EMR, Google Dataproc and Starburst is expected in Q3 2026. Chief Product and Strategy Officer Barry Morris framed the launch bluntly: 'The database layer is where agentic AI either scales or stalls, and most of the industry is still treating agent memory as an afterthought.'
At a Glance
- Category
- Infrastructure & Cloud
- Pricing
- Freemium, Usage-based, Contact for pricing
- Target Market
- CTOs, Enterprise Developers, Data Engineers, Platform Engineering Teams, AI Engineers
Key Features
- ✓Agent Memory
Persistent agent memory across sessions and restarts supporting short-term, long-term, semantic and conversational memory types.
- ✓Enterprise MCP server
An enterprise-supported, self-managed Model Context Protocol server providing standardized data access for external AI agents.
- ✓Agent Catalog
Manages prompts, tools and end-to-end traces, giving governance and visibility into agent behavior queryable through SQL++.
- ✓Billion-scale vector search with semantic caching
Vector search over structured and unstructured data at billion scale, with semantic caching to reduce redundant LLM calls.
- ✓AI Functions
SQL++ and LLM utilities that run tasks such as sentiment analysis directly against operational data.
- ✓Cloud-to-edge deployment
One architecture across Capella DBaaS, self-managed on-premises and multicloud, hybrid, edge and air-gapped environments.
Capabilities
Use Cases
- •Consolidating fragmented agent data infrastructure
Replaces the separate vector, caching and document stores teams previously stitched together for every agent, which Couchbase cites as the biggest drag on production timelines.
- •Reducing agentic AI token and inference cost
Agent Memory reuses persistent cumulative context instead of resending conversations each turn, while co-located data and caching cut redundant inference calls.
- •Operational analytics on agent and enterprise data
Enterprise Analytics 2.2 federates Apache Iceberg lakehouses and adds change data capture from Oracle and SQL Server for real-time operational querying.
Ideal For
Best For
- ✓Giving production AI agents persistent memory that survives sessions and restarts
- ✓Replacing separate vector, cache and document stores with one operational data layer
- ✓Cutting agent token and inference costs through cumulative context and semantic caching
- ✓Running agent data infrastructure from cloud to edge and air-gapped environments
Integrations
Deployment
Market Analysis
Pros
- ✓Removes real architectural sprawl across vector, cache and document stores
- ✓Framework-agnostic, validated with LangGraph, CrewAI and LlamaIndex
- ✓Genuine edge and air-gapped support that cloud-only vector databases lack
- ✓Direct token and inference cost reduction story for agents in production
Cons
- ✗Enterprise pricing is not public
- ✗Adopting it as the agent data layer concentrates dependency on one vendor
- ✗The Trino adapter is not yet shipped — expected Q3 2026
- ✗Couchbase is now privately held, which reduces financial disclosure
Pricing
Capella free tier
$0
- ✓Couchbase Capella DBaaS free tier
- ✓Agent Memory trial
Capella / Self-Managed
Contact for pricing
- ✓Agent Memory, Agent Catalog, MCP server and AI Functions
- ✓Billion-scale vector search and semantic caching
- ✓Cloud, self-managed, hybrid, edge and air-gapped deployment
- ✓Enterprise Analytics 2.2 with Iceberg federation
A Couchbase Capella free tier and an Agent Memory trial are available; enterprise pricing for the AI Data Plane was not disclosed at launch.
Sources
This page was written from 3 sources, 2 on domains other than couchbase.com.
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