Amazon Bedrock vs Google Vertex AI: the 2026 comparison.

IWhat you are actually choosing between

Both are hyperscaler GenAI platforms, shaped differently. Bedrock is a multi-provider model platform with managed building blocks. Vertex AI is a full end-to-end AI platform anchored by Google's own Gemini models.

Amazon Bedrock gives you many foundation models through one API (the Converse API). On 10 October 2026 the Bedrock pricing page listed 19 providers, including Anthropic, Meta, Mistral AI, Amazon, Cohere, DeepSeek, OpenAI and Google itself. Around the models sit Knowledge Bases, Agents, Guardrails, evaluation and fine-tuning, inside your AWS account under IAM, VPC and AWS compliance. Amazon Nova is AWS's in-house family, but the platform is deliberately model-neutral.

Google Vertex AI centers on Gemini (flagship and cheaper Flash-class models), plus a Model Garden of third-party and open models, including Claude and Llama. Around them sits a deep ML stack: custom training, pipelines, a feature store, model registry, endpoints, evaluation, and grounding with Google Search and your data.

So the choice is a model-neutral platform inside AWS versus a Gemini-anchored AI and MLOps platform inside GCP. Both can run Claude. Their defaults, native integrations and MLOps depth differ, and which cloud you already use drives most decisions. Prices and features change fast, so confirm on each vendor's live pages.

IIModel selection: model-neutral platform vs Gemini-first garden

Bedrock is provider-neutral by design. Vertex leads with Gemini and surrounds it with a garden of other models.

• Pick Bedrock for breadth: Claude for nuanced reasoning and writing, Llama or Mistral for open-weight cost efficiency, Nova for low-cost volume, Cohere for retrieval, and easy swapping through one API. No house model you are nudged toward.
• Pick Vertex for Gemini depth: The most integrated path to Gemini: long context, native multimodality (text, image, audio, video) and grounding against Google Search and your data. Some platform features are Gemini-centric.
• Both can run Claude: Anthropic's Claude is on both platforms, so neither choice forces you off frontier-grade quality. The frontier lead changes release to release anyway.

IIIPricing shape and cost at scale (worked math)

Both bill mainly per million input and output tokens, per model, so the structure is comparable. The real cost driver is which model you pick and how many tokens you push. The example is illustrative, not a quote.

A small model (a Nova or a Gemini Flash-class model) can be one to two orders of magnitude cheaper per token than a flagship, which dwarfs the platform difference. Bedrock offers batch at 50% off on-demand for supported models, prompt caching and Provisioned Throughput (Bedrock pricing, checked 10 October 2026). Vertex offers batch prediction, context caching and provisioned throughput.

Take a support assistant handling 100,000 conversations a month at 2,000 input and 500 output tokens each: 200M input and 50M output tokens a month. With illustrative rates, a mid-tier model at $1 and $4 per 1M tokens costs about $400 a month; a frontier model at $5 and $15 costs about $1,750; a small model at $0.20 and $0.80 costs about $80. Same traffic, a 22 times spread, entirely from model choice.

IVRegions, residency, governance and compliance

Both are global and mature. Governance rarely decides on the merits of one IAM over the other; it decides on which cloud you already operate.

• Regions and residency: Bedrock runs in many AWS Regions with per-model availability and cross-region inference; Vertex runs in many Google Cloud regions with regional endpoints and residency controls. Both meet common EU and country residency needs inside their own cloud. Cross-cloud setups add egress, latency and a second residency story.
• Identity and access: Bedrock uses AWS IAM; Vertex uses Google Cloud IAM. Adopting the platform native to your cloud means no second access-control plane.
• Private networking: Bedrock uses AWS PrivateLink; Vertex uses VPC Service Controls and Private Service Connect.
• Audit and compliance: Bedrock logs to CloudTrail and CloudWatch under AWS's compliance program; Vertex logs to Cloud Audit Logs and Cloud Monitoring under Google's. On enterprise terms, both say they do not train base models on your prompts. Verify the specific certification and region you need with each vendor.

VAWS-native vs GCP-native integration, and MLOps depth

Beyond the model API, the platforms differ in what they connect to and how far they go into the ML lifecycle.

Native integration. Bedrock fits the AWS portfolio: Lambda, Step Functions, OpenSearch, S3-based Knowledge Bases and SageMaker. Vertex fits GCP: very tight links to BigQuery (run Gemini over your warehouse), Cloud Storage, Dataflow, and Google Search grounding. Follow your data gravity.

MLOps depth. Vertex AI is one end-to-end platform: custom training, pipelines, feature store, registry, experiments and endpoints sit next to generative AI. On AWS the same breadth comes from two products: Bedrock for managed foundation models, and Amazon SageMaker AI (formerly Amazon SageMaker) for the full custom-ML lifecycle. So the fair comparison to Vertex is "Bedrock plus SageMaker AI," and "SageMaker vs Vertex AI" is really the classical-ML half of this page.

Lock-in. Either choice ties inference to that cloud's IAM, networking and managed building blocks. Bedrock minimizes model lock-in by design; Vertex gently centers Gemini. Keep a thin model-abstraction layer either way, so switching providers is a config change at the generation call.

VIVertex wins when, Bedrock wins when

Match your situation to the list that fits.

The short version: the dominant factor is which cloud you already live in. Co-locating GenAI with your data, governance and billing beats almost every marginal model or feature difference.

VIIMigrating from Vertex AI (GCP) to Bedrock (AWS)

Teams consolidating on AWS often move inference from Vertex AI to Bedrock. When the GenAI layer is well abstracted, that part is modest; the larger effort is the GCP data and MLOps you move with it.

• 1. Pick and enable the target models: Choose the Bedrock models that replace your Gemini calls (often a Claude tier, Nova or Llama). Access to many models is on by default with the right Marketplace permissions; Anthropic models need a one-time use-case form.
• 2. Swap the client and re-tune: Replace Vertex or Gemini SDK calls with the Converse API, then re-tune prompts and re-run your evaluation set.
• 3. Re-platform RAG, agents and grounding: Map Vertex grounding, RAG Engine and Agent Builder to Bedrock Knowledge Bases and Agents, or keep your own RAG and change only the generation call.
• 4. Move data and MLOps as needed: Decide what moves from BigQuery and Vertex Pipelines to AWS (for example Redshift, Athena and SageMaker AI) and what stays cross-cloud. This is usually the biggest part of a real GCP to AWS migration.
• 5. Wire in AWS governance, then cut over: Put access under IAM, use PrivateLink if required, turn on CloudTrail, A/B on real traffic, then shift.

VIIIWho pays: MAP for the migration, credits for the run

A move from GCP to AWS is the kind of work AWS funds through partners. Which route fits depends on what you are moving.

MAP for the migration. If you are moving workloads from GCP to AWS, a vetted AWS partner can check whether the move qualifies for MAP (Migration Acceleration Program) funding and file it. MAP is for teams that are not on AWS yet and want to migrate. See the MAP guide.

POC or IW for new AI work. If you are testing Bedrock first, the partner can file POC (proof of concept) funding. If you already run on AWS and are launching a new AI feature, incremental workload (IW) funding can fit. For startups, partner-filed MAP, POC or IW funding is $50K to $150K. Depends on the workload; AWS approves.

Activate for the run. You apply for AWS Activate yourself: Activate Founders is $1,000 to start, up to $5,000, and Activate Portfolio is up to $200,000 with the Org ID from your VC, accelerator or angel group if it is an Activate Provider. A consulting partner cannot apply for Activate on your behalf.