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Use Amazon Bedrock Managed Knowledge Bases when its supported connectors, access controls, and retrieval options fit your application and you want AWS to manage storage, indexing, and retrieval infrastructure. Choose a customer-managed knowledge base when your team needs direct control of the vector store or ingestion pipeline. Neither choice is universally better: confirm fit with your data and security requirements, then validate retrieval quality, latency, and total cost in a representative proof of concept.
What changes when AWS manages the knowledge base?
A retrieval-augmented generation (RAG) system prepares documents as chunks, turns those chunks into embeddings, stores them with links to source material, then retrieves relevant content to supply as context to a language model. Amazon Bedrock Knowledge Bases automates parts of that path. With a managed knowledge base, AWS manages the storage, indexing, and retrieval infrastructure; your application team still chooses and configures data sources, access, ingestion behavior, and how the APIs fit into the application.
AWS recommends Managed Knowledge Bases “for optimized retrieval accuracy and a managed experience.” That is AWS’s product guidance, not evidence that the managed option will produce better results or lower costs for every corpus or application. AWS Knowledge Bases overview
Managed or customer-managed: the practical trade-off
| Decision area | Managed knowledge base | Customer-managed knowledge base |
|---|---|---|
| Infrastructure ownership | AWS manages storage, indexing, and retrieval infrastructure. | Your team manages more of the RAG pipeline and its infrastructure. |
| Vector store and pipeline control | Less direct control over the underlying infrastructure and pipeline configuration. | More direct control over the vector store and ingestion, parsing, indexing, and storage configuration. AWS gives OpenSearch Serverless, Aurora, and Neptune as examples of vector-store options. |
| Setup and continuing work | Reduces infrastructure operations, but still requires connector and IAM setup, ingestion management, API integration, and application-side evaluation. | Requires your team to operate and configure more of the retrieval stack. |
| Good fit when | Supported defaults and connectors meet the workload, and reducing infrastructure ownership is a priority. | Owning or tuning the data path and its components is a requirement. |
For managed setup, AWS says the default embedding model is service-managed. The creation flow also allows a customer to provide a Bedrock embedding model and a KMS key, and to configure parser and chunking settings, optional indexing for additional modalities, and optional ingestion-log delivery. These choices do not make the service equivalent to owning the full pipeline; check the applicable configuration options for the specific service and Region. Create a managed knowledge base
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Check connector, access, and Region fit
AWS’s managed knowledge base creation guide lists Amazon S3, Confluence, Custom, Google Drive, OneDrive, SharePoint, and Web Crawler as data-source types. Availability and behavior can depend on the target Region, so confirm the connector you need is available where the application will run.
AWS describes document-level ACL filtering for managed knowledge bases, with Web Crawler as an exception. If users must only retrieve documents they are authorized to see, verify that the source’s permission behavior maps to your application’s policy model. Do not assume that the presence of a connector alone proves that its access controls satisfy your requirements. Retrieve data and generate AI responses with Amazon Bedrock Knowledge Bases
Rank #2
- Your favorite music and content – Play music, audiobooks, and podcasts from Amazon Music, Apple Music, Spotify and others or via Bluetooth throughout your home.
- Alexa is happy to help – Ask Alexa for weather updates and to set hands-free timers, get answers to your questions and even hear jokes. Need a few extra minutes in the morning? Just tap your Echo Dot to snooze your alarm.
- Keep your home comfortable – Control compatible smart home devices with your voice and routines triggered by built-in motion or indoor temperature sensors. Create routines to automatically turn on lights when you walk into a room, or start a fan if the inside temperature goes above your comfort zone.
- Do more with device pairing – Fill your home with music using compatible Echo devices in different rooms, or create a home theatre system with Fire TV.
- Say goodbye to drop-offs and buffering - With eero Built-in, Echo Dot doubles as a mesh wifi extender, adding up to 1,000 sq. ft. of wifi coverage to your existing eero network.
Choose how much retrieval orchestration the application needs
The retrieval API choice determines how much of the RAG flow Bedrock performs as one operation and how much remains explicit in application code:
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- Retrieve: returns relevant source chunks or images. Use it when the application needs retrieved material but will control the remainder of the flow.
- RetrieveAndGenerate: combines retrieval with model invocation and can return citations to source chunks.
- AgenticRetrieveStream: decomposes complex queries, retrieves iteratively, and returns trace events.
You can also use Retrieve separately and customize the rest of the RAG flow. Reranking is another retrieval option. Evaluate the API against the application’s query patterns and need for orchestration; managed infrastructure does not require you to hand over every application-level decision. Retrieving information from data sources using Amazon Bedrock Knowledge Bases
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Plan for ingestion updates and freshness
Adding, editing, or removing source documents does not by itself update the knowledge base: AWS says to sync the data source. Sync is incremental. It skips unchanged files, ingests new files, re-parses, re-chunks, re-embeds, and re-indexes changed content or metadata, and removes deleted documents from the vector store. Set a sync cadence that matches how fresh answers need to be, and monitor ingestion so failures do not leave retrieval based on stale content. Sync your data with your Amazon Bedrock knowledge base
For the documented customer-managed ingestion configuration, AWS lists parser and chunking customization. Its documentation says the chunking strategy cannot be changed after a data source is connected; the documented default is approximately 300 tokens while preserving sentence boundaries. Test representative documents before committing to ingestion settings. This documented default applies to that customer-managed configuration and should not be assumed for a distinct managed-service configuration. Customize ingestion for a data source
Rank #4
- Your favorite music and content – Play music, audiobooks, and podcasts from Amazon Music, Apple Music, Spotify and others or via Bluetooth throughout your home.
- Alexa is happy to help – Ask Alexa for weather updates and to set hands-free timers, get answers to your questions and even hear jokes. Need a few extra minutes in the morning? Just tap your Echo Dot to snooze your alarm.
- Keep your home comfortable – Control compatible smart home devices with your voice and routines triggered by built-in motion or indoor temperature sensors. Create routines to automatically turn on lights when you walk into a room, or start a fan if the inside temperature goes above your comfort zone.
- Do more with device pairing – Fill your home with music using compatible Echo devices in different rooms, or create a home theatre system with Fire TV.
- Say goodbye to drop-offs and buffering - With eero Built-in, Echo Dot doubles as a mesh wifi extender, adding up to 1,000 sq. ft. of wifi coverage to your existing eero network.
Estimate managed-service charges and check quotas
AWS’s pricing page, viewed October 7, 2026, lists these Managed Knowledge Base charges. They are service list prices, not an all-in estimate for an application; confirm current pricing and Region applicability before budgeting. Amazon Bedrock Pricing
| Charge listed by AWS | Scope |
|---|---|
| $5.00 | Per GB of raw data per month for managed knowledge base storage. |
| $1.00 | Per 1,000 standard Retrieve API calls. |
| $0 | Managed parsing, embedding generation, and reranking. |
| $4.00 plus $1.00 | Per 1,000 Agentic Retrieve calls with managed planning, plus per 1,000 underlying Retrieve calls, respectively. |
Custom embedding or reranking models add model-provider charges. For Agentic Retrieval with a customer-selected LLM, AWS lists the underlying Retrieve charge plus the model provider’s pricing for query planning. Estimate generation model calls, data transfer, observability, gateway use, and any other AWS services alongside knowledge base charges.
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AWS lists default managed knowledge base quotas of 10 TB raw data storage per knowledge base, 200 data sources per knowledge base, and 600 Retrieve requests per minute per knowledge base; some quotas are adjustable. These are planning limits, not a performance guarantee or a prediction of achievable throughput. Check the current quota values for your account and Region. Service quotas for managed knowledge bases
Verify security prerequisites before setup
A managed knowledge base requires a supported data connector and appropriate IAM permissions. The setup role needs iam:PassRole to pass the service role to Bedrock. Custom embedding or reranking models require model access, and using a customer-managed KMS key requires additional configuration and permissions. Validate least-privilege policies for your environment before provisioning. Prerequisites for Amazon Bedrock managed knowledge bases
Use a decision checklist, then validate with a proof of concept
- Does the required connector exist in the target Region?
- Does its document-access behavior enforce the application’s permission model?
- Are the managed parsing, chunking, indexing, and retrieval options sufficient, or must your team tune the pipeline directly?
- Does Retrieve, RetrieveAndGenerate, or AgenticRetrieveStream fit the required orchestration and citation behavior?
- Can the planned sync cadence keep content fresh enough, and can the team monitor ingestion?
- Do indexed data volume and expected request rates fit the current quotas?
- Does a workload-based estimate include storage, retrieval, generation, optional model charges, and supporting services?
For a proof of concept, use representative documents and real query types. Inspect retrieved chunks and citations, test additions, edits, deletions, and resync behavior, and measure end-to-end latency and answer quality against your requirements. Build a cost estimate from expected raw data, retrieval volume, generation use, and optional services. Those results—not the managed label or AWS’s general recommendation—should determine whether to proceed.
Quick Recap
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