Migrate to Amazon Bedrock Managed Knowledge Base with your coding agent

With a customer-managed knowledge base on Amazon Bedrock , you provision and scale the vector store, tune the embedding model and index settings, and plan capacity for each new data source. The migration guide in the Agent Toolkit for AWS helps you migrate to Amazon Bedrock Managed Knowledge Base . Managed Knowledge Base takes over that work and adds new retrieval capabilities, and your team keeps control of the content and the application that uses it. Moving a production knowledge base means your team maps its configuration, creates the replacement, ingests content, updates the application’s retrieval call, and validates retrieval before switching traffic. To help, we added a knowledge base migration guide to the amazon-bedrock skill in the Agent Toolkit for AWS . The guide gives your coding agent the context to plan the migration with you, starting with read-only discovery and a plan you review line by line. On your approval, the agent creates the new knowledge base and its data sources alongside the original, or writes a migrated infrastructure as code (IaC) template for your pipeline. Ingestion and updating your application’s Retrieve calls stay with you, and your agent can walk you through both. In this post, we explain why we recommend the move and how the migration guide works, review the prerequisites, install the skill, plan a migration, update the application’s retrieval call, and validate retrieval before cutover. Why move to Managed Knowledge Base We recommend Managed Knowledge Base because it balances ease of use, retrieval accuracy, and cost. AWS runs the store With a Managed Knowledge Base, Amazon Bedrock handles ingestion, storage, indexing, and retrieval, and the store scales automatically as your content grows. Hybrid search, which combines keyword and semantic matching, is always on, with no index to configure. The knowledge base includes a managed embedding model, a managed reranker, and a built-in parser for multimodal file types. You can still bring your own Amazon Bedrock embedding model when you create the knowledge base. On each retrieval request, you choose the managed reranker, an Amazon Bedrock reranking model, or no reranking. The managed reranker requires the managed embedding model. Built-in capabilities Managed Knowledge Base includes capabilities you’d otherwise build yourself: Native connectors ingest content directly from sources such as Amazon Simple Storage Service (Amazon S3), Microsoft SharePoint, and Atlassian Confluence. For the current list, see supported connectors . After the first full sync, later syncs pick up only changes, on demand or on a schedule. The Managed Knowledge Base includes a deletion safeguard to help protect against accidental bulk removal of indexed content. Access control list (ACL) awareness ingests document permissions with the content, so retrieval returns only what the signed-in user can access. For connectors that support it, such as Microsoft SharePoint and Atlassian Confluence, a real-time check against the source also catches permission changes since the last sync. ACL awareness doesn’t authenticate users, so your application must verify and pass the user’s identity. Agentic retrieval answers questions that a single search can’t by handling multi-part questions and retrieving iteratively. It can search more than one knowledge base in a single request. Integration with AgentCore Gateway , a capability of Amazon Bedrock AgentCore, exposes your knowledge base as a target. Any client that supports the Model Context Protocol (MCP) can then query the knowledge base, and the gateway handles authentication. Resource-based policies let applications in other AWS accounts use one team’s knowledge base. Scope each policy to the specific roles that call the knowledge base, and grant only the actions they use, such as bedrock:Retrieve . Agentic retrieval, AgentCore Gateway integration, and resource-based policies are not available on customer-managed knowledge bases. For the AWS Regions where managed knowledge bases are available, and the features that AWS GovCloud (US-West) doesn’t support, see Supported AWS Regions . Higher quotas As of this writing, higher default quotas change how you can organize content. With 200 data sources per knowledge base, you can group related content in one knowledge base. With 10,000 knowledge bases per account, per Region, each team or product line can use a separate knowledge base. Default quota Customer-managed Managed Knowledge bases per account, per Region 100 10,000 Data sources per knowledge base 5 200 Concurrent ingestion jobs per knowledge base 1 50 Query input characters 1,000 10,000 For current values, see Amazon Bedrock endpoints and quotas and Service quotas for managed knowledge bases . Pay for what you store and retrieve Cost is one of three things teams weigh when they consider this move, alongside customizability and direct control of the vector store. On a customer-managed knowledge base , you choose, provision, scale, and update the vector and text stores, and pay separately for the infrastructure you select. On a Managed Knowledge Base, those stages are part of the service, and storage and retrieval are metered directly. The pricing models can differ too. With a customer-managed knowledge base, storage and retrieval charges depend on the vector store you choose. Some stores charge for provisioned or minimum capacity, and others, such as Amazon S3 Vectors, a capability of Amazon S3, charge only for what you use. A Managed Knowledge Base is priced on what you store and retrieve, so cost follows real usage. Usage-based pricing can fit enterprise question-answering, policy, and support knowledge bases that run at low utilization. For current rates, see Amazon Bedrock pricing . For the parsers available on a customer-managed knowledge base and how each is billed, see Parsing options for your data source . Cost driver Customer-managed knowledge base Managed Knowledge Base Vector storage Priced separately by your chosen store. A provisioned retrieval service can have a minimum idle cost even at low volume Included in the base storage rate of $5 per GB per month Multimodal document parsing Requires Amazon Bedrock Data Automation, billed by page or image, or a foundation model, billed by token. Text-only parsing is free. Included Embedding generation Billed separately at your embedding model’s rate Included with the default model. Charges apply only if you choose a custom model Ingestion, sync, and re-sync compute Re-embedding, orchestration code, and indexing compute spikes on each sync add recurring cost Included, with incremental sync that processes only new and changed content Connectors Building and running custom connectors can add compute cost Native connectors are included Standard retrieval No Amazon Bedrock charge per query. Your vector store can charge per query, as Amazon S3 Vectors does. $1 per 1,000 retrieval requests Reranking Amazon Rerank is charged at $1 per 1,000 queries. A custom reranking model is billed at its own rate Included in the $1 per 1,000 retrieval requests Agentic retrieval You build the agentic workflow on a Bedrock model, which can cost roughly $18 to $24 per 1,000 queries before development and maintenance. Because model usage is billed by token, the price per query varies. Agentic retrieval costs an additional $4 per 1,000 queries on top of the underlying retrieval calls, including multi-shot retrieval, reranking, and answer generation. Depending on the number of underlying retrieval calls, the total can range from roughly $6 to $9 per 1,000 queries. Rates are only part of the total cost of ownership. On a customer-managed knowledge base, your team provisions, scales, and operates each stage as a separate resource. A Managed Knowledge Base consolidates them into one resource, with a default embedding model, foundation model, and reranker, and with per-knowledge-base rate limits by default. You manage the content a