As of October 2026, pick Amazon Bedrock when your applications and data live on AWS and you want the widest choice of model providers (Anthropic, OpenAI, Meta, Mistral AI, Amazon, DeepSeek, Qwen and more) behind one API, IAM model and logging setup. Pick Google's platform, where Vertex AI's services now sit inside the Gemini Enterprise Agent Platform, when Gemini is your primary model family, your data sits in BigQuery, or you want Google's agent tooling and TPU-backed capacity. Both now offer Anthropic's Claude, so the decision follows your cloud estate and data more than any single model.
In April 2026, Google folded Vertex AI into the Gemini Enterprise Agent Platform. Google's own Vertex AI documentation now states that Vertex AI's services are part of Gemini Enterprise Agent Platform and points to the new docs. Most people still search for "Vertex AI", and many APIs, SDKs and console paths still carry the old name, so this guide uses both. If you built on Vertex AI, your work continues on the Agent Platform; check Google's release notes for any SDK deprecations that affect your code.
Bedrock is a marketplace-style service: the Bedrock pricing page lists models from AI21 Labs, Amazon, Anthropic, Cohere, DeepSeek, Google, Luma AI, Meta, MiniMax, Mistral AI, Moonshot AI, NVIDIA, OpenAI, Qwen and others. Its pitch is choice under one API and one security model. If your evaluation picks a different model next quarter, switching is a configuration change.
Google's platform is built around Gemini first. On the Agent Platform pricing page, the Gemini 3.6 to 3.8 Flash models carry introductory pricing of $0.75 per million input tokens and $3.75 per million output tokens through December 31, 2026, rising to $1.50 and $7.50 from January 1, 2027, and Gemini 4 models are also listed. Model Garden adds partner and open models. Claude on Google Cloud is available as a managed API on the Agent Platform, and Google also documents an option to use Anthropic's Claude developer platform on Anthropic-managed infrastructure.
For coding and agent work, see OpenAI vs Anthropic for enterprise. Whatever the platform, run your own evaluations; model rankings change several times a year.
For the same model, latency and throughput depend on region, capacity tier and request shape more than on the platform. What differs is how you buy capacity:
Cross-region and global routing improve capacity but may move processing outside a single region. Decide your data residency policy explicitly, then choose endpoints that match it.
Both platforms now sell an agent stack, not just model endpoints.
Managed agent services speed up the first version. For complex production agents, many teams still own the orchestration code in their repos and use the platform for models, retrieval, identity and logging. See RAG vs fine-tuning and Pinecone vs pgvector for the retrieval design choices.
Both platforms state that customer prompts and outputs are not used to train the providers' foundation models, and both keep traffic inside your cloud controls when configured correctly. Review the details in procurement: logging and retention settings, abuse-monitoring terms, which partner models run on the cloud's infrastructure versus the model provider's, and who acts as data processor. On Google, VPC Service Controls can draw a perimeter around AI and data services; on AWS, service control policies and VPC endpoints play the same role. Audit logging flows to CloudTrail on AWS and Cloud Audit Logs on Google.
Both bill per token on demand, with cheaper batch options and reserved capacity for steady high volume. Claude list prices on both clouds generally track Anthropic's own pricing, but check regional and global endpoint differences. The biggest cost levers are architectural: prompt caching, routing routine work to cheaper models, trimming retrieved context and batching offline jobs. Existing cloud commitments matter too, because model usage typically counts toward AWS or Google Cloud spend agreements, which often decides the platform on its own.
Bedrock skills overlap heavily with general AWS skills (IAM, VPC, Lambda, ECS, S3), and AWS is the larger talent pool. Google Agent Platform skills overlap with BigQuery and GKE experience, and engineers who know the Vertex AI SDK are rarer. The scarce skill on both is production LLM engineering: evaluation, retrieval quality, agent reliability and cost control. You can hire AWS Bedrock engineers or Google Vertex AI engineers, and see pay bands on our LLM engineer rates page.
Ryz Labs AI pod teams build AI systems in your AWS or Google Cloud account and your repos, alongside your engineers, and ship them to production on US business hours. A typical pod pairs a tech lead with ML and backend engineers. Our pods built an AI fraud-detection system for a global fleet company that confirmed $5.94M in fraud across 244K+ scored invoices, validated by the client's fraud team; see our case studies. Engineers work in your accounts, and you control and can revoke access at any time.
Typical cost is $7,000 to $15,000 per engineer per month; an example pod of five senior engineers runs about $50,000 to $75,000 a month, with leads quoted per team. If you want a strategy engagement on AI platforms without engineers who build, a management consultancy is the better fit.
Yes, under a new name. In April 2026 Google folded Vertex AI's services into the Gemini Enterprise Agent Platform. Existing capabilities continue, and Google's Vertex AI documentation now points to the Agent Platform documentation.
Yes. Claude is a core model family on Amazon Bedrock and is available on Google Cloud through the Agent Platform, with an Anthropic-managed option documented by Google. Check model versions and regions for your requirements.
Neither is cheaper across the board. Per-token prices depend on the model, tier and region; both offer batch pricing and reserved capacity. Prompt caching, model routing and existing cloud commitments usually matter more than platform list prices.
Bedrock lists Google among its model providers, but Gemini's frontier models are primarily offered on Google Cloud. If Gemini is central to your roadmap, Google's platform is the natural home; confirm specific model availability on Bedrock's model list.
Only with a reason, such as data in both clouds or a required model available on only one in your region. A gateway layer, shared evaluations and consistent guardrail policies keep a dual-platform setup manageable.
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