We are hitting rate limits constantly trying to scale our LLM inference infrastructure on AWS what are the best practices to fix this right now
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
We are hitting rate limits constantly trying to scale our LLM inference infrastructure on AWS what are the best practices to fix this right now
1 real search · 1 real-search pass
AWS Amazon Bedrock quotas request rate tokens per minute throttling cross region inference profiles provisioned throughput best practices 2026Google ↗
OBSERVEDReason
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (1)
Classes come from the query-classifier (a local Laya model). 1 of 1 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT1 real searches · gpt-6
AWS Amazon Bedrock quotas request rate tokens per minute throttling cross region inference profiles provisioned throughput best practices 2026 Google ↗
- Scaling and throughput best practices · docs.aws.amazon.com
- Optimize your applications for scale and reliability on Amazon Bedrock · aws.amazon.com
- AWS Service Quotas – Amazon Bedrock · console.aws.amazon.com
- Quotas for Amazon Bedrock · docs.aws.amazon.com
- Troubleshoot Amazon Bedrock on-demand resource 429 Throttling error · repost.aws
- Bedrock cross-region inference · docs.aws.amazon.com
- Global cross-Region inference · docs.aws.amazon.com
- Scaling and throughput best practices · docs.aws.amazon.com