AWS has rolled out prompt caching for Amazon Bedrock, its managed service for building generative AI applications on top of foundation models like Anthropic's Claude and Amazon's own Titan/Nova models.
Prompt caching allows an application to mark portions of a prompt — such as a system instruction, a long document used as context, or a set of tool definitions — as reusable. On subsequent calls that repeat the same cached segment, Bedrock skips reprocessing that content and serves it from cache, rather than running it through the full model inference pipeline again.
AWS states this can cut input token costs by up to 90% and reduce latency by up to 85% for supported models, depending on how much of the prompt is cacheable and how frequently it repeats across calls. The feature is aimed squarely at applications with a large, static context that gets reused across many requests — a common pattern in retrieval-augmented generation (RAG) systems, customer support bots that reference a fixed knowledge base, and coding assistants that reuse large system prompts.
The mechanics matter for anyone running production AI workloads: caching applies per-model and typically requires the cached prefix to remain byte-identical across calls, meaning teams need to structure prompts so static content (instructions, reference docs) comes first and variable content (the user's actual query) comes last. AWS's blog post walks through implementation patterns and cost-latency tradeoffs for different traffic profiles.
This is not a new model or new capability in the sense of what the AI can do — it is a cost and performance optimization for existing Bedrock-based applications. Teams already running support automation, sales enablement tools, or internal copilots on Bedrock can apply this without re-architecting their model choice, though prompt structure may need adjustment to maximize the cached portion.
For companies evaluating whether to build on Bedrock versus alternatives like OpenAI's API or Azure AI, this closes some of the cost gap that caching features from competitors already offered, and is a relevant data point in vendor comparisons for infrastructure decisions made this year.
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