Question 1

How much does Amazon Bedrock cost?

Accepted Answer

Amazon Bedrock is billed primarily on token usage, with separate rates for input and output tokens that vary by foundation model. Premium models like Claude Opus cost significantly more per token than lighter models like Claude Haiku, and output tokens are typically priced higher than input tokens. On top of per-token inference you can incur charges for provisioned throughput, Knowledge Bases retrieval, the underlying Amazon OpenSearch Serverless vector store, and Guardrails. Most teams find a large share of their Bedrock bill comes from using premium models and verbose prompts where cheaper options would work.

Question 2

How do I reduce Amazon Bedrock costs?

Accepted Answer

The highest-impact levers are: (1) route requests to the cheapest model that meets quality requirements instead of defaulting to a premium model, (2) enable prompt caching so repeated context is billed at a large discount, (3) use batch inference for non-latency-sensitive workloads at roughly half the on-demand price, (4) trim verbose prompts and oversized context windows, and (5) right-size or drop unused provisioned throughput. MLCostIntel surfaces each of these opportunities automatically against your actual Bedrock usage.

Question 3

What drives Amazon Bedrock costs?

Accepted Answer

Bedrock cost is driven mainly by token volume and model choice: total input and output tokens multiplied by the per-model rate. Verbose prompts, large retrieved context in RAG applications, and using premium models for simple tasks all inflate spend. Additional drivers include the Amazon OpenSearch Serverless vector store behind Knowledge Bases, retrieval calls, provisioned throughput commitments, and Guardrails evaluations.

Question 4

Does prompt caching reduce Bedrock costs?

Accepted Answer

Yes. With prompt caching, repeated portions of a prompt (such as a long system prompt or shared context) are stored and billed as cached input tokens at a large discount versus standard input tokens — often up to around 90% cheaper on the cached portion. Batch inference is another discount path, running non-urgent requests asynchronously at roughly half the on-demand price. Both are underused because they require deliberate configuration, which is exactly what MLCostIntel flags.

Question 5

How does MLCostIntel help with Bedrock cost optimization?

Accepted Answer

MLCostIntel connects to your AWS account via a read-only IAM role and ingests your AWS Cost and Usage Report (authoritative cost) alongside Bedrock invocation logs for token, model, and request attribution. It breaks Bedrock spend down by model and token type — input, output, cached, and batch — and surfaces savings from prompt caching, batch inference, model right-sizing, and RAG / Knowledge Base optimization. You get per-team and per-application attribution, provisioned-throughput vs on-demand analysis, and real-time anomaly detection, typically within 5 minutes of connecting.

Amazon Bedrock Cost Optimization

Common Amazon Bedrock Cost Challenges

No Per-Model Visibility

Token Waste

Missed Caching & Batch Discounts

Hidden RAG / Knowledge Base Cost

How MLCostIntel Optimizes Amazon Bedrock Costs

How It Works

Connect Your AWS Account

We Analyze Your Bedrock Spend

Get Your Savings Roadmap

Where Teams Save on Amazon Bedrock

Total Bedrock Spend

Prompt Caching

Batch Inference

Model Selection

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