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.
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.
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