AWS and Google Cloud have the two most comprehensive AI service portfolios in the industry. Google’s advantage is deep AI research (the transformer paper, BERT, AlphaFold originated from Google), while AWS leads on enterprise integration and service breadth. This article maps services between the two platforms.

A naming note for 2026: at Cloud Next 2026, Google rebranded Vertex AI as the Gemini Enterprise Agent Platform (Vertex AI is now the former name, and existing Vertex AI APIs, SDKs, and workloads continue to run unchanged). This page keeps the Vertex AI names because they remain the established and widely used identifiers, but expect to see the new Gemini Enterprise branding in Google’s console and newer documentation.

Foundation Models and LLM Access

AWSGCPNotes
Amazon BedrockVertex AI Model GardenBoth provide access to multiple model families. Vertex offers Gemini (Google’s flagship), Llama, and Mistral. Bedrock offers Anthropic Claude, Meta Llama, Mistral, Cohere, and Amazon’s own Nova models (Nova replaced the earlier Amazon Titan family).
Bedrock Agents / Bedrock AgentCoreVertex AI Agent BuilderManaged agent frameworks. AWS added Bedrock AgentCore (generally available October 2025) to deploy and operate agents built with any framework (LangGraph, CrewAI, LlamaIndex, Strands Agents). Vertex AI Agent Builder includes grounding with Google Search as a built-in capability.
Bedrock Knowledge BasesVertex AI Search / RAG EngineManaged RAG pipelines. Vertex AI Search integrates Google’s search quality into enterprise applications.
Bedrock GuardrailsVertex AI Safety FiltersSafety and content controls for model outputs.

Google’s current frontier Gemini models (the Gemini 3 family, including Gemini 3 Pro) are strong competitors to the Anthropic Claude models offered on Bedrock. Gemini 3 Pro ships with a 1 million token context window, which is large but in line with the long-context options now available across providers, so context length alone is no longer the differentiator it once was. The bigger practical split is which model families and tooling each platform gives you first: Bedrock for Anthropic Claude and Amazon Nova, Vertex AI for Gemini and Google Search grounding. For teams not locked into AWS, Vertex AI is a credible alternative.

Speech and Language

AWSGCPNotes
Amazon TranscribeGoogle Speech-to-TextGCP’s Chirp model offers strong multilingual transcription. AWS Transcribe Medical is specialized for healthcare.
Amazon PollyGoogle Text-to-SpeechGCP’s Neural2 and Chirp HD voices are high quality. GCP supports more languages.
Amazon TranslateCloud Translation APIBoth support 70+ languages with neural MT. GCP’s AutoML Translation allows domain customization.
Amazon ComprehendGoogle Natural Language APIEntity extraction, sentiment, syntax. GCP’s Healthcare NL API specializes in clinical text.

Beyond the classic split of separate speech-to-text and text-to-speech services, AWS now offers Amazon Nova Sonic on Bedrock, a single speech-to-speech model for real-time voice conversations (Nova 2 Sonic followed in December 2025). It collapses the traditional transcribe, reason, then synthesize pipeline (Amazon Transcribe to a text model to Amazon Polly) into one model, which is useful for voice agents and call automation.

Vision

AWSGCPNotes
Amazon RekognitionCloud Vision AIBoth handle label detection, face analysis, OCR, explicit content detection. GCP Vision API has a strong history as Google’s own image analysis infrastructure.
Amazon TextractDocument AIGCP Document AI has strong pre-built processors (invoice, receipt, form, ID). Both handle complex table extraction.
Rekognition VideoVideo Intelligence APIVideo label detection, shot change, object tracking. GCP adds explicit content detection in video.
Rekognition Custom LabelsAutoML VisionTrain custom image classifiers and object detectors.

ML Platform

AWSGCPNotes
Amazon SageMaker AIVertex AIFull ML lifecycle platforms. The core ML platform is now branded Amazon SageMaker AI, and AWS introduced the next generation of SageMaker (announced at re:Invent 2024) with SageMaker Unified Studio, a single environment for data, analytics, and AI. Vertex AI Workbench (Jupyter notebooks) is polished. SageMaker has tighter AWS ecosystem integration.
SageMaker PipelinesVertex AI PipelinesML workflow orchestration using Kubeflow Pipelines SDK.
SageMaker Ground TruthVertex AI Data LabelingHuman-in-the-loop labeling at scale.
Amazon ForecastVertex AI ForecastTime-series forecasting service.
Amazon PersonalizeRecommendations AIPersonalization and recommendation APIs.

Infrastructure for AI

AWSGCPNotes
AWS LambdaCloud Functions / Cloud RunServerless compute for AI event handlers. Cloud Run supports containers directly.
Amazon S3Cloud StorageObject storage for AI data. Similar capabilities.
Amazon OpenSearchVertex AI SearchVector search. GCP also integrates AlloyDB and Spanner for pgvector.

Decision Factors

Choose AWS when:

  • Existing infrastructure is AWS-native
  • You need Anthropic Claude models specifically (via Bedrock)
  • Deep integration with AWS data services (Glue, Redshift, Kinesis) matters
  • Enterprise procurement through AWS Marketplace is preferred

Choose GCP when:

  • You need Gemini models or Google Search grounding
  • The team uses Google Workspace (Docs, Sheets, Drive integration)
  • BigQuery is your primary data warehouse
  • You want access to Google’s TPU infrastructure for custom model training

Sources and Further Reading

AWS Official Documentation

Google Cloud Official Documentation