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You can build a Claude-powered coding assistant by putting an AWS Lambda handler in front of Amazon Bedrock: the handler accepts a coding request, calls Claude through Bedrock, and returns the answer. To reuse long, stable instructions and reference material, enable Bedrock prompt caching where the selected Claude model supports it. This guide covers the Bedrock route; direct Anthropic API requests use different interfaces and cache controls.
How the Lambda and Bedrock architecture works
A typical request travels from a client to an HTTPS endpoint, then to Lambda, and finally to a Claude model through the Bedrock Runtime API. Lambda handles application logic; Bedrock provides model inference. AWS documents both InvokeModel and Converse examples with Boto3.
- Accept a request. Expose the handler through a Lambda function URL or API Gateway. Decide how clients authenticate before making the endpoint available.
- Validate and assemble input. Check the request shape and size, then combine the stable assistant instructions with the current user task and any conversation context your application needs.
- Call Bedrock. Use the selected model with Converse or InvokeModel, and grant the Lambda execution role permission for that API and model resource.
- Return a bounded response. Shape the model result for the client and align client, Lambda, and model timeouts with the expected interaction.
The UI, authentication scheme, conversation store, streaming design, and coding tools are application choices; the AWS API examples do not prescribe them.
Choose Converse or InvokeModel
| API | When it fits | What to account for |
|---|---|---|
| Converse | Multi-turn chat where a unified message interface is useful. | Use it only when the selected model supports it. AWS recommends Converse for supported models because it simplifies multi-turn interactions. |
| InvokeModel | You need to work directly with a model-specific request and response format. | Construct the body expected by the chosen model and follow the InvokeModel API reference. |
These are Bedrock interfaces, not interchangeable spellings for Anthropic’s direct API. Confirm the selected Claude model’s supported API and whether it requires an inference profile in your target Region.
Give Lambda the Bedrock permission it needs
The Lambda execution role must authorize the invocation method used by the handler. AWS identifies bedrock:InvokeModel as required for InvokeModel and Converse calls. Scope the policy to the selected resource where possible, and check the Bedrock inference permissions guidance for the model and account setup.
Streaming has a separate permission action. If the handler uses a streaming API, verify that action and the model’s support rather than assuming non-streaming access covers it.
Enable prompt caching for reusable coding context
Prompt caching can reuse eligible prompt prefixes across requests. Bedrock describes it as a way to reduce inference response latency and input-token costs, but the result depends on model support, workload, request composition, and whether the cache is hit. It does not guarantee a hit or a fixed saving. See the current Amazon Bedrock prompt-caching guide.
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Put stable context first
Arrange the prompt so reusable material is in a stable prefix, before content that changes from request to request. For a coding assistant, that may include system instructions, coding conventions, tool descriptions, and reference material that is genuinely reused. Put the current task, changing code excerpts, and recent conversation later. If an explicit checkpoint’s prefix changes, the cache may miss.
Choose implicit or explicit caching
| Mode | How it works | Best fit |
|---|---|---|
| Implicit | Bedrock and the model attempt to reuse an eligible prefix without explicit cache controls. It is best effort. | Start here when you want eligible repeated context to be considered without managing checkpoint markers. |
| Explicit | The request marks reusable prefix checkpoints using model-specific controls. | Use when you need control over where eligible prompt prefixes are checkpointed and can meet that model’s constraints. |
With either mode, caching only helps when requests share reusable context and the chosen model supports the behavior.
Check the model’s minimum and TTL rules
Cache rules vary by model and API. AWS’s guide, for example, lists Claude Haiku 4.5 with a 4,096-token minimum and up to four explicit checkpoints; those values should not be generalized to other Claude models. A checkpoint below the model’s minimum may leave inference successful but not cache the prefix.
The guide says the documented default TTL is five minutes. A supported one-hour TTL must be set explicitly. Check the current model entry and regional availability before configuring a deployment, because model support, minimum token counts, checkpoint limits, and TTL options differ.
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A Lambda function URL provides direct HTTP(S) access; API Gateway is another way to invoke the handler. AWS documents function URL authentication modes and Region availability in its Lambda function URLs guide.
- AWS_IAM: Requests must be signed with SigV4. Choose this when callers can make authenticated AWS requests.
- NONE: Requests are unsigned. Do not treat an endpoint with this setting as a production-safe default; decide how access will be controlled before exposing it.
Use API Gateway when its role as an API front door suits your routing and request-handling needs. The cited AWS material confirms both endpoint options but does not establish a universal feature-by-feature winner.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Match invocation style, payloads, and timeouts
A chat request that waits for an answer commonly uses a synchronous request/response flow. Work that may run longer can require asynchronous job handling or a streaming design. These approaches change how the client receives results and handles retries.
The AWS Lambda Invoke API documents a 6 MB payload ceiling for synchronous invocation and 1 MB for asynchronous invocation. These are limits for that Lambda API, not a promise that every endpoint or model request accepts those sizes. See the Lambda Invoke API documentation and account for the complete request path. Keep client timeouts, Lambda timeout, model latency, payload limits, and retry behavior aligned.
Quick Recap
Build a deployment checklist
- Confirm the chosen Claude model is available in the target Region and supports the Bedrock API and caching mode you plan to use.
- Check whether the model requires an inference profile in that Region.
- Grant the Lambda role the required Bedrock invocation action, scoped to the relevant resource where possible; check the separate action for streaming.
- Place stable prompt material before changing task details and verify explicit checkpoint minimums, limits, and TTL support in the current model entry.
- Choose endpoint authentication deliberately; AWS_IAM requires SigV4, while NONE permits unsigned requests.
- Set payload and timeout expectations for the whole request path, including retry and long-running-work behavior.
- Decide how your application handles private source code, conversation state, and any tools that can act on code. The cited AWS materials do not define a complete privacy, retention, or code-execution policy for your assistant.
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