Amazon is making enterprise AI more affordable by offering a tiered pricing structure that starts at just $0.07 per million tokens, with its new Nova model family providing significantly cheaper options than premium alternatives. As of May 2026, Amazon Bedrock, the company's managed AI service, offers three distinct pricing plans designed to match different workload patterns, from unpredictable usage to high-volume, steady-state deployments.

What Are Amazon's Nova Models and Why Do They Matter?

Amazon's Nova family represents a significant shift in how enterprises can access capable AI without breaking the bank. The lineup spans multiple tiers, each optimized for different use cases and budget constraints. Nova Micro, the most affordable option, costs just $0.035 per million input tokens and $0.14 per million output tokens, making it ideal for lightweight tasks like text classification or simple question-answering. Moving up the stack, Nova Lite offers more capability at $0.06 per million input tokens and $0.24 per million output tokens.

For organizations needing more sophisticated reasoning and longer context windows, Nova Pro and Nova Premier provide enhanced performance. Nova Pro runs at $0.80 per million input tokens and $3.20 per million output tokens, while the flagship Nova Premier costs $2.50 per million input tokens and $12.50 per million output tokens. This tiered approach allows enterprises to match model capability directly to their specific needs, avoiding the cost of overpowered models for simpler tasks.

How Do Amazon Bedrock's Three Pricing Plans Work?

• On-Demand (Pay-Per-Token): This consumption-based model charges only for tokens processed with no monthly seat fees, making it ideal for AWS-native deployments that need to route requests across multiple models. Pricing varies by model, with Claude Opus 4.6 at the premium end ($15 per million input tokens, $75 per million output tokens) and Nova Micro at the budget end ($0.035 per million input tokens, $0.14 per million output tokens).
• Provisioned Throughput: Organizations with predictable, high-volume workloads can reserve capacity upfront with 1-month or 6-month commitments, achieving 30 to 50 percent discounts compared to on-demand pricing. This plan guarantees dedicated capacity and predictable latency, making it suitable for production applications with consistent traffic patterns.
• Enterprise (Bedrock + AWS Deal): Large AWS customers can negotiate custom pricing through enterprise data processing agreements (EDPs), gaining access to volume commits, custom models via the Bedrock Marketplace, and dedicated AWS enterprise support.

The flexibility across these tiers reflects Amazon's recognition that different organizations have vastly different AI needs. A startup experimenting with AI might use the on-demand tier for its unpredictable usage patterns, while a mature enterprise running production recommendation systems would benefit from the cost savings of provisioned throughput.

How Does Amazon Bedrock Compare to Other AI Platforms?

Amazon Bedrock's pricing positions it competitively within the broader AI API market. The platform's lowest tier at $0.07 per million tokens undercuts many competitors, though direct pricing comparisons depend heavily on which models you're using. For instance, Claude Opus 4.6 costs $15 per million input tokens on Bedrock, which includes a small markup over direct Anthropic API pricing. However, organizations already invested in AWS infrastructure benefit from seamless integration and consolidated billing.

One notable feature is Bedrock's prompt caching capability, currently in preview, which can reduce costs on cached tokens by up to 90 percent. This is particularly valuable for applications that repeatedly process similar context, such as customer service chatbots that reference the same knowledge base across multiple conversations. For teams with steady-state workloads, the provisioned throughput discount of 30 to 50 percent can result in substantial savings compared to pay-as-you-go pricing.

What Should Enterprises Consider When Choosing Amazon Bedrock?

Organizations evaluating Amazon Bedrock should assess their usage patterns before committing to a pricing tier. Teams with unpredictable or experimental workloads benefit from on-demand pricing's flexibility, while those running mature applications with consistent traffic should calculate whether provisioned throughput's upfront commitment delivers better economics. The availability of 50+ models across providers including Anthropic, Meta, Mistral, AI21, Cohere, and Stability AI means enterprises can optimize both cost and capability by selecting the right model for each task.

The breadth of model options also matters for enterprises with specific compliance or performance requirements. Organizations concerned about vendor lock-in can leverage Bedrock's multi-model routing to distribute workloads across different providers, reducing dependency on any single AI vendor. For AWS customers already paying for compute, storage, and other services, consolidating AI workloads onto Bedrock simplifies procurement and billing while potentially unlocking volume discounts through enterprise agreements.

As AI becomes increasingly central to enterprise operations, Amazon's tiered pricing and diverse model selection reflect a market reality: one-size-fits-all AI pricing no longer works. By offering everything from ultra-cheap micro models to premium reasoning engines, Bedrock acknowledges that enterprises need flexibility to balance cost, capability, and performance across their entire AI portfolio.