Amazon Bedrock Cost Optimization: Techniques & Best Practices

Source: Dev.to

How Amazon Bedrock Pricing Works

• Model Inference – Pay per token (both input and output). Options:

  • On‑Demand (pay as you go)
  • Batch (bulk processing)
  • Provisioned Throughput (reserved capacity)

• Model Customization – Training, storing custom models, and using them all incur charges.

• Custom Model Import – Free to import, but inference and storage are billed.

Example: Nova Micro is about 23× cheaper than Nova Pro for the same input tokens. Choosing the right model is often the single biggest cost lever.

A Practical Framework for Cost Optimization

When building generative AI applications with Amazon Bedrock, follow this systematic approach:

1. Select the appropriate model for your use case.
2. Determine if customization is needed (and choose the right method).
3. Optimize prompts for efficiency.
4. Design efficient agents (multi‑agent vs. monolithic).
5. Select the correct consumption option (On‑Demand, Batch, or Provisioned Throughput).

Strategy 1: Choose the Right Model for Your Use Case

Not every task requires the most powerful model. Amazon Bedrock’s unified API makes it easy to experiment and switch between models.

Example: Customer Support Chatbot

• Scenario: A SaaS company needs a chatbot for support queries.
• Approach: Tiered model strategy based on query complexity.

Cost Impact: Up to 95 % reduction compared to using the most powerful model for all queries.

Best Practice
Use Amazon Bedrock’s automatic model evaluation to test different models on your specific use case. Start with smaller models and only upgrade when performance requirements justify the cost increase.

Strategy 2: Model Customization in the Right Order

When customization is needed, follow this hierarchy to minimize costs:

1. Prompt Engineering – No additional cost.
2. RAG (Retrieval‑Augmented Generation) – Moderate cost.
3. Fine‑tuning – Higher cost (one‑time training expense).
4. Continued Pre‑training – Highest cost.

Example: Legal Document Analysis

• Phase 1 – Prompt Engineering

  • Crafted specialized prompts with legal context.
  • Result: 70 % accuracy with minimal cost.

• Phase 2 – RAG Implementation

  • Integrated Bedrock Knowledge Bases with a legal document repository.
  • Result: 85 % accuracy with moderate cost increase.

• Phase 3 – Fine‑tuning

  • Fine‑tuned on labeled legal documents.
  • Result: 92 % accuracy with higher ongoing costs.

Cost Comparison

• Fine‑tuning from the start incurs significant upfront and ongoing expenses.
• Progressive approach yields 40‑60 % first‑year savings by avoiding premature fine‑tuning.

Best Practice
Start with prompt engineering and RAG. Only consider fine‑tuning or continued pre‑training when these approaches cannot meet accuracy requirements and the business case justifies the additional expense.

Strategy 3: Optimize Prompts for Efficiency

Well‑crafted prompts reduce token consumption, improve response quality, and lower costs.

Prompt Optimization Techniques

• Be Clear and Concise – Remove unnecessary words.
• Use Few‑Shot Examples – Provide 2‑3 examples instead of lengthy explanations.
• Specify Output Format – Request structured outputs (JSON, markdown).
• Set Token Limits – Use max_tokens to cap output length.

Example: Content Generation API

Before Optimization

Please generate a comprehensive product description for our e-commerce platform.
The description should be detailed, engaging, and highlight all the key features
and benefits of the product. Make sure to include information about pricing,
availability, and customer reviews. The description should be written in a
professional tone and be optimized for search engines.

Token count: ~120 tokens

After Optimization

Generate a product description (150 words max, JSON format):
{
  "title": "...",
  "description": "...",
  "features": ["...", "..."],
  "price": "..."
}

Token count: ~35 tokens

Savings: 71 % reduction in input tokens, which scales dramatically across many requests.

Strategy 4: Implement Prompt Caching

Amazon Bedrock’s built‑in prompt caching stores frequently used prompts and their contexts, dramatically reducing costs for repetitive queries.

Example: Product Recommendations

• Scenario: E‑commerce site generating recommendations; many users have similar preferences.
• Implementation: Enable prompt caching (default 5‑minute window).
• Estimated Cache Hit Rate: 40 %

Cost Impact (per month)

• 10 M recommendation requests with 40 % cache hits.
• Cached requests are charged only for input tokens, not output tokens.
• Savings: ~6‑7 % reduction in total costs.

Client‑Side Caching Enhancement

Combine Bedrock caching with a client‑side cache (e.g., Redis) for exact prompt matches.

• Redis TTL: 5 minutes
• Client‑Side Hit Rate: 20 %

Enhanced Savings

• Client‑side cache serves 20 % of requests (no API calls).
• Remaining requests benefit from Bedrock’s prompt cache, further lowering expenses.