Amazon Bedrock vs Google AI (Gemini): Pricing Comparison 2026

Amazon Bedrock and Google AI Gemini both offer managed access to frontier AI models via API, but they are fundamentally platform plays that reflect their respective cloud ecosystems. The right choice depends almost entirely on where your infrastructure already lives. Amazon Bedrock provides access to multiple models (Claude from Anthropic, Llama from Meta, Mistral, Cohere, and others) through a unified AWS API. It uses usage-based pricing - you pay per API call based on token volume. Bedrock integrates natively with AWS services: Lambda for serverless execution, SageMaker for model customization, and AWS security controls for compliance. For organizations already running on AWS, Bedrock is the natural choice because the integration cost and mental overhead are minimal. Google AI Gemini is Google's unified multimodal model available via API with generous free tier pricing (1 million tokens free monthly, then usage-based). Gemini excels at handling mixed modalities - text, images, audio, and video - in a single request. If your use case involves processing images, videos, or audio alongside text, Gemini's native multimodal support reduces complexity compared to chaining separate models. For Google Cloud-based teams, Gemini integrates with BigQuery, Vertex AI pipelines, and other Google services similarly to how Bedrock integrates with AWS. The practical decision is ecosystem-driven: AWS teams should use Bedrock, Google Cloud teams should use Gemini. Cloud-agnostic teams or multimodal-heavy use cases might favor Gemini's unified approach. Both services are comparable in capability and pricing for pure text workloads - the ecosystem fit is the decisive factor.