AWS Bedrock Pricing Calculator
Instantly estimate your Amazon Bedrock costs and discover proven strategies to reduce AI spending by up to 40%
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AWS Bedrock Pricing Calculator
Get instant cost estimates across different model providers and usage scenrios. See exactly how your token usage translates to monthly spend
What is Amazon Bedrock?
Amazon Bedrock is AWS's fully managed service that democratizes access to powerful foundation models from leading AI providers like Anthropic, AI21, Cohere, Meta, and Amazon's Titan models—all through a simple, unified API.
Whether you're building intelligent chatbots, document summarization tools, or custom AI assistants, Bedrock eliminates the complexity of infrastructure management, model deployment, and scaling challenges that traditionally slow down AI adoption.
Designed for enterprise-grade applications from day one, Bedrock provides the security, compliance, and reliability that Fortune 500 companies demand for their AI initiatives.
Enterprise Security
SOC compliance & VPC endpoint
Zero Infrastructure
No GPU provisioning needed
Multi-Model Access
Switch provides seamlessly
Custom Fine-Tuning
No GPU provisioning needed
Why AWS Bedrock is Adopted Broadly Across Industries
Faster Time-to-Market
Deploy AI features in weeks, not months, without ML infrastructure expertise
Cost Predictability
Pay-per-use pricing model eliminates upfront infrastructure investments
Enterprise Compliance
Built-in data governance and security controls for regulated industries
AWS Bedrock Pricing Model Explained
01 Key Cost Factors
- Model Provider Selection:
- Each provider (Anthropic Claude, Titan, Mistral, Cohere) has distinct per-token pricing
Token Type Pricing:
- Input tokens (prompts) vs. output tokens (completions) are priced differently
- Custom Model Costs:
- Fine-tuning and model storage add significant monthly expenses
- Throughput Options:
- On-demand vs. provisioned throughput pricing models
02 Pricing Pro Tip
- Most organizations underestimate Bedrock costs by 60-80% because they don't account for:
- Token usage patterns across different applications
- Regional pricing variations (up to 25% difference)
- Peak usage periods requiring provisioned throughput
- Custom model training and storage recurring costs
- Accurate cost forecasting requires understanding your usage split across providers and token types
Expert Tips & Tricks for Managing Bedrock Spend
Avoid runaway AI costs with these proven strategies from organizations that have scaled Bedrock to millions of requests per month.
01 Centralized Cost Visibility
Use a FinOps platform like Finout to unify Bedrock spend with your entire cloud infrastructure—including accurate per-team breakdowns.
02 Token-Aware Prompting
Design concise prompts that minimize unnecessary output tokens. Verbose models like Claude can generate 10x more tokens than needed with poorly crafted prompts.
03 Application-Level Monitoring
Break down costs by product feature and team, not just by model. This enables accurate budget allocation and identifies optimization opportunities.
04 Multi-Model A/B Testing
Test across providers to identify the most cost-effective model that meets your quality requirements. Price differences can be 5-10x between providers.
05 Automated Budget Alerts
Set up real-time spending alerts before costs spiral out of control. AI workloads can scale from hundreds to thousands of dollars overnight.
06 Smart Throughput Planning
Use provisioned throughput only for predictable, high-volume workloads. It's 40% more expensive unless you have consistent traffic patterns.
AWS Bedrock Pricing FAQ - Complete Cost Management Guide
Get answers to the most important questions about AWS Bedrock pricing, cost optimization, and FinOps best practices. Learn how to calculate costs, choose the right pricing model, and avoid common pitfalls.
01 How does AWS Bedrock pricing work?
AWS Bedrock pricing is based on model inference charges calculated per token processed. You pay for input tokens (prompt) and output tokens (response) separately, with different rates for each. Pricing varies by model provider (Anthropic, Cohere, Meta, etc.) and model size, with larger models typically costing more per token.
02 What are input and output tokens, and why do they have different prices?
Input tokens represent your prompt or question sent to the model, while output tokens are the model's response. Output tokens typically cost 3-4x more than input tokens because generating responses requires significantly more computational resources than processing prompts. This pricing structure encourages efficient prompt engineering.
03 What's the difference between on-demand and provisioned throughput pricing?
On-demand pricing charges per token with no upfront commitment, ideal for variable or unpredictable workloads. Provisioned throughput requires purchasing dedicated capacity (measured in model units) with hourly charges, offering cost savings of 20-50% for consistent, high-volume usage patterns.
04 How do I calculate the cost for a specific request?
Multiply your input tokens by the input token rate, output tokens by the output token rate, then sum both. For example: 1,000 input tokens × $0.0003 + 500 output tokens × $0.0015 = $1.05 total. Use AWS Bedrock's token counting API or model-specific tokenizers for accurate estimates.
