Couchbase's new features target cost, complexity of deploying agents
New capabilities in the vendor's AI Data Plane include native integration with Amazon Bedrock and converting unstructured data to vector embeddings in private networks.
Couchbase is taking aim at lowering the cost and complexity of deploying agents on an enterprise scale with the latest additions to its recently launched AI Data Plane.
The new features, made generally available on Tuesday, are highlighted by capabilities that enable users to convert unstructured data to vector embeddings within a private network infrastructure and a native integration with Amazon Bedrock that provides customers with access, on a pay-per-token basis, to hundreds of foundation models and.
In addition, Couchbase is adding both a Model Context Protocol server and managed Agent Memory, a feature that provides a memory layer so agents can pick up where they left off rather than begin each interaction from scratch that was, as managed cloud services on AWS and making vector search available in its Operational Insights environment.
Collectively, the new AI Data Plane capabilities are significant additions for Couchbase customers because they attempt to unify previously fragmented data services to speed, simplify and lower the cost of managing agents, according to Michael Ni, an analyst at Constellation Research.
"What makes Couchbase's release important is that it addresses the work required to get agents running against real operational data," he told TechTarget. "Getting AI agents into production is only half the battle. The bigger challenge is running them economically at scale. ... Bringing [AI infrastructure capabilities] together can reduce that overhead and make it easier to give agents current context."
Database vendors including MongoDB and Redis, along with data platform providers such as AWS, Databricks, Google Cloud, Microsoft and Snowflake, offer many of the same building blocks, Ni continued. However, Couchbase does stand apart from some by integrating them onto an operational database.
"The question is whether Couchbase can [truly] help customers get agents working with current business information faster, more reliably and at lower costs," Ni said. "That's particularly interesting for companies running AI applications across cloud, data centers,and the edge, where managing separate services can become expensive."
Reducing complexity and cost
Compared to analytics applications and traditional AI tools such as predictive models, AI agents are more complex and costly to build and manage. Vendors have responded by rolling out unified environments for deploying agents.
What makes Couchbase's release important is that it addresses the work required to get agents running against real operational data.
Michael NiAnalyst, Constellation Research
Couchbase joined the fray in June by launching the AI Data Plane, a governed infrastructure for agents featuring capabilities such as real-time data retrieval and agent memory. Now, the vendor is expanding the suite to further address the complexity and cost of managing agents.
"The new announcements are significant milestones in the continued reinvention of Couchbase as an AI Data Plane provider following its acquisition a little over a year ago," Matt Aslett, an analyst at ISG, told TechTarget. "Building on its core components of a distributed NoSQL database and AI services, the company has … expanded its functionality to better address the requirements of agentic applications."
The integration with Amazon Bedrock is potentially the most valuable of the new AI Data Plane capabilities because it reduces the development and deployment delays created by connecting disparate tools and lowers token usage, Aslett continued. In addition, he suggested that native vector search within Couchbase Operational Insights is an important addition because it enables users to combine structured queries with semantic vector search without piecing together tools from various vendors and creating integration pipelines.
However, like Ni, Aslett noted that Couchbase evolving in step with its competitors rather than providing substantial differentiation.
"All these providers are assembling the core capabilities required to deliver a data layer capable of delivering the speed and scale required for emerging agent-driven applications," he said. "While Couchbase is ahead of some others in terms of agent memory and AI functions, a core capability being … that Couchbase is yet to address is support for automated database cloning and branching."
While Couchbase's new AI Data Plane capabilities show that Couchbase is evolving to keep pace with ongoing trends, customer feedback played a part in the vendor's decision to add the new capabilities, according to Barry Morris, Couchbase's chief product and strategy officer.
"It was half customer pull, half architectural conviction," he told TechTarget. "We watched enterprise teams try to get AI agents into production and hit the same wall every time. The model was fine, but the data layer underneath was never designed for agentic workloads."
Customers were stitching together separate systems for memory, vectors, operational data, and analytics, each with its own logic and security, Morris continued. Meanwhile, according to Cloudflare's Radar report, machine traffic on enterprise data infrastructures surpassed human traffic in June 2026.
"If agents are the primary consumers of enterprise data infrastructure, and most of that infrastructure was not built for them, you have to ask what a purpose-built data layer should actually provide," Morris said. "The capabilities we shipped on [Tuesday] are our first answer to that."
Looking ahead, as Couchbase continues to build up its AI Data Plane, agent memory governance is one area of focus, according to Morris. In addition, the vendor plans to add capabilities that make it easier for new customers to move their data and AI workloads from competing vendors' platforms to Couchbase.
Streamlining beyond database specialization
By bringing native integration with Amazon Bedrock, managed Agent Memory and a managed MCP server on AWS, and vector search together in the AI Data Plane, Couchbase is taking a logical approach of lowering the cost and complexity of deploying agents, according to Ni.
"Memory, caching and operational data are closely related, so bringing them together makes sense," he said.
However, adding governance capabilities would strengthen the AI Data Plane and further Couchbase's attempt to streamline managing agents.
"Giving an agent trusted information isn't the same as deciding what the agent can do with it," he said. "Couchbase has an opportunity to make its platform more valuable by connecting data access with runtime policies, permissions and a record of the decisions agents make," Ni said.
Aslett similarly noted that Couchbase is effectively addressing customer concerns over the complexity and cost of managing agents. However, like other data management providers evolving to meet the needs of organizations building and deploying agents, Couchbase needs to invest in changing the perception that it remains a database specialist.
"All these providers face the challenge of changing established perceptions based on their existing or prior products," Aslett said. "While Couchbase is providing its existing customers with new AI data plane functionality to address agentic workloads, it also needs that message to reach potential new customers who are more likely to think of the company in relation to its distributed document database."
Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.
