If you need an observability data lake for your Amazon Bedrock workloads, you’re not alone. When you’re running thousands of model invocations a day across multiple accounts, after a request is sent you might be wondering, what happened? You can see the aggregate bill, but you can’t see what happened inside each call.
You might start to ask questions: what prompt was sent, what the model returned, how many tokens it used, which model handled it, and whether the response was any good! If you’re a platform or machine learning (ML) engineer looking after a fleet of Amazon Bedrock workloads across teams, that’s a real problem. You can’t debug a spike, you can’t spot wasteful prompts, and you can’t tell one team’s usage from another’s.
We built something to help: a centralized observability data lake for your Amazon Bedrock invocations. In this post, I cover what native data you have and how we designed the lake. I also show how to use it to see…
https://aws.amazon.com/blogs/machine-learning/amazon-bedrock-tokenomics-data-lake/
