Amazon Bedrock
AWS-native LLM access — Nova, Claude, Llama, and more via one API
Amazon Bedrock is AWS's managed LLM service, providing access to Amazon's own Nova models alongside third-party models from Anthropic, Meta, Mistral, and others. It integrates natively with the AWS ecosystem including IAM, VPC, and CloudWatch, making it the default choice for teams already on AWS.
The only way to run Claude, Llama, and Amazon Nova within your own AWS VPC — data never leaves your account.
- Native AWS integration — IAM, VPC, CloudWatch, S3
- Access to Claude, Llama, Mistral, and Amazon Nova via one API
- Enterprise compliance: SOC 2, HIPAA, GDPR
- Provisioned throughput for guaranteed capacity
- No data leaves your AWS account
- More complex setup than standalone inference APIs
- Pricing can be higher than direct provider APIs
- Latency overhead from AWS abstraction layer
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Full comparison tableAmazon Bedrock LLM pricing overview
Amazon Bedrock is a proprietary AI lab that develops and hosts its own large language models. Amazon Bedrock is AWS's managed LLM service, providing access to Amazon's own Nova models alongside third-party models from Anthropic, Meta, Mistral, and others. It integrates natively with the AWS ecosystem including IAM, VPC, and CloudWatch, making it the default choice for teams already on AWS. Common use cases include AWS-native, Enterprise compliance, Multi-model, HIPAA workloads. Headquartered in Seattle, WA, founded 2023. All models are accessible via a REST API compatible with standard OpenAI-style request formats, enabling drop-in integration with most LLM frameworks and orchestration tools.
Amazon Bedrock vs other LLM providers
Amazon Bedrock competes with OpenAI, Anthropic, Google, Groq, Together AI, Mistral, Cohere, and other inference API providers across dimensions of price, throughput, latency, context window, and model intelligence. The only way to run Claude, Llama, and Amazon Nova within your own AWS VPC — data never leaves your account. Use the LLM provider comparison tool to see Amazon Bedrock token pricing, latency, and throughput side-by-side with any other provider. The full LLM pricing table shows all providers ranked by input token cost, output token cost, and throughput in a single sortable view.
Why choose Amazon Bedrock?
Amazon Bedrock's key strengths are: Native AWS integration — IAM, VPC, CloudWatch, S3; Access to Claude, Llama, Mistral, and Amazon Nova via one API; Enterprise compliance: SOC 2, HIPAA, GDPR. Limitations to consider: More complex setup than standalone inference APIs; Pricing can be higher than direct provider APIs. For teams running high-volume inference workloads, prompt caching and batch API endpoints can reduce effective input token costs by 50–90% — check the context window cost guide for a full breakdown of caching economics.
Understanding Amazon Bedrock token pricing
Amazon Bedrock charges separately for input (prompt) and output (completion) tokens, priced per 1M tokens in USD. Output tokens are typically 3–5× more expensive than input tokens due to the compute cost of autoregressive generation. Prices shown are sourced from Amazon Bedrock's public pricing page and updated daily. Need help estimating your spend? Read the LLM API cost calculator guide — it covers tokens, context windows, prompt caching, and batch discounts with worked examples for RAG, chat history, and document processing workloads.
Amazon Bedrock context window and model capabilities
Context window size directly affects both capability and cost — every token in the context window is charged as an input token. For RAG and document processing workloads, longer context windows enable richer retrieval but increase per-call costs proportionally. Prompt caching — where supported — stores the KV state of repeated prefixes and charges 75–90% less for cache hits, making it the most impactful cost optimization for applications with consistent system prompts or retrieved documents. See the LLM context window cost guide for a full analysis of how context length affects your API bill.
Self-hosted vs managed inference: when Amazon Bedrock makes sense
Managed inference APIs like Amazon Bedrock eliminate infrastructure overhead — no GPU provisioning, driver management, or model serving stack to maintain. The trade-off is cost at scale: a single H100 at ~$2.50/hr can serve ~500K tokens/min of Llama 3.3 70B, which at Amazon Bedrock API rates would cost significantly more per token. The break-even point depends on your request volume, latency requirements, and engineering capacity. For teams processing fewer than ~10M tokens/day, managed APIs are almost always cheaper when total cost of ownership is considered. Above that threshold, self-hosted inference on rented GPU compute typically wins on unit economics. Read the cheapest GPU cloud guide for a full break-even analysis. Historical Amazon Bedrock token price data is available in the LLM price history charts.