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What Is AWS Bedrock Pricing
Amazon Bedrock is AWS's fully managed service for accessing foundation models from leading AI providers — including Anthropic (Claude), Meta (Llama), Amazon (Titan), Mistral, Cohere, and Stability AI. Bedrock pricing varies significantly by model, input/output token counts, and whether you use on-demand or provisioned throughput, making cost estimation essential before deploying AI workloads.
This calculator helps you estimate Bedrock costs based on your expected usage patterns, model selection, and throughput requirements — enabling informed decisions about model selection and deployment strategy.
Bedrock Pricing Models
| Pricing Model | How It Works | Best For |
|---|---|---|
| On-Demand | Pay per input/output token with no commitment | Development, testing, variable workloads |
| Batch Inference | Up to 50% discount for async processing | Large-volume offline processing |
| Provisioned Throughput | Reserved model units for guaranteed performance | Production workloads needing consistent latency |
| Model Customization | Training costs + storage + inference | Fine-tuned models for specific use cases |
Cost Factors
| Factor | Impact on Cost |
|---|---|
| Model selection | Claude Opus vs Haiku can differ by 30-60x per token |
| Input vs output tokens | Output tokens are typically 3-5x more expensive than input |
| Context window usage | Longer prompts = more input tokens = higher cost |
| Response length | Longer outputs significantly increase per-request cost |
| Throughput needs | Provisioned throughput has a monthly minimum commitment |
| Region | Pricing varies by AWS region |
Common Use Cases
- Budget planning: Estimate monthly AI costs before deploying Bedrock-powered features in production applications
- Model selection: Compare cost per query across models (Claude Sonnet vs Haiku vs Llama) to find the best price-performance ratio for your use case
- Architecture decisions: Determine whether on-demand, batch, or provisioned throughput is most cost-effective for your usage pattern
- Cost optimization: Identify opportunities to reduce costs through model selection, prompt optimization, or throughput provisioning
- ROI analysis: Calculate the cost of AI-powered features to justify investment against business value generated
Best Practices
- Start with smaller models — Use Claude Haiku or Llama for tasks that don't require the largest models. Test whether a smaller model meets quality requirements before defaulting to Opus.
- Optimize prompt length — Shorter, well-structured prompts reduce input token costs. Avoid repeating instructions across requests when using conversation history.
- Use batch inference for bulk processing — If latency is not critical (analytics, content generation, data processing), batch inference provides up to 50% savings.
- Monitor token usage — Use AWS Cost Explorer and CloudWatch to track actual token consumption. Unexpected spikes may indicate prompt injection, recursive calls, or inefficient prompts.
- Evaluate provisioned throughput at scale — Once your usage is predictable and consistent, provisioned throughput can be more cost-effective than on-demand pricing while guaranteeing performance.
ℹ️ Disclaimer
This tool is provided for informational and educational purposes only. All processing happens entirely in your browser - no data is sent to or stored on our servers. While we strive for accuracy, we make no warranties about the completeness or reliability of results. Use at your own discretion.