AWS Bedrock vs Google Vertex AI — Cloud AI Platforms Compared (2026)

Most GenAI engineering decisions come down to which cloud AI platform your organization already lives in. If you are AWS-first, AWS Bedrock is the default path. If you are GCP-first, Google Vertex AI is. But when you have architectural flexibility — or when a stakeholder asks you to justify the choice — you need a clear technical comparison, not just “use whatever your cloud provider offers.”

This guide compares AWS Bedrock and Vertex AI across the dimensions that matter: model catalog, RAG features, fine-tuning, agent tooling, pricing, security, and when each platform genuinely wins.

The most common question from GenAI engineers entering a new project: “Should we use Bedrock or Vertex AI?”

The honest answer: in most organizations, this is already decided. Your cloud contract, your security team’s approved vendors, your data residency requirements, and your existing infrastructure all point in one direction before you write a line of code.

But three scenarios make this a genuine architectural decision:

Greenfield projects where the cloud provider has not been chosen. Here, the AI platform capabilities, model availability, and pricing legitimately influence the cloud choice — not the other way around.

Multi-cloud organizations where workloads span AWS and GCP. Which platform you invoke from a given service can have latency, cost, and compliance implications.

Model-first requirements where a specific model — notably Claude — is non-negotiable. Claude is only available as a managed service on AWS Bedrock. Vertex AI does not offer Claude. If your organization has committed to Claude for quality, safety, or contractual reasons, Bedrock is the only managed cloud path.

Understanding the genuine trade-offs lets you make — and defend — the right choice regardless of which scenario you are in.

The most consequential difference between the two platforms is model availability: Bedrock has Claude, Vertex AI has Gemini, and neither has both.

AWS Bedrock is the more diverse marketplace. Its model catalog in 2026 includes:

• Anthropic Claude: Claude Sonnet 4.5, Claude Opus 4, Claude Haiku 3.5 — the most capable general-purpose models on the platform
• Meta Llama: Llama 3.3 70B, Llama 3.1 405B — best open-weight models for self-directed fine-tuning
• Mistral: Mistral Large, Mistral Small — strong for European data residency needs
• Cohere: Command R+ — optimized for enterprise RAG workloads
• Amazon Titan: Titan Text, Titan Embeddings — AWS-native models, lowest latency for Bedrock-native RAG
• AI21 Labs: Jamba — mixture-of-experts architecture

All models share a single authentication mechanism (IAM) and API surface (Converse API), regardless of underlying provider. This is the defining architectural characteristic of Bedrock: one API, many models.

Google Vertex AI is Gemini-first with a narrower catalog:

• Gemini 2.0 Flash: The production workhorse — fast, cheap, multimodal, 1M context window
• Gemini 2.0 Pro: Highest capability Gemini model for complex reasoning
• Gemini 1.5 Flash / Pro: Previous generation, still widely deployed
• Model Garden: Access to Llama 3, Mistral, Falcon — open-weight models for fine-tuning and deployment
• Imagen 3: Image generation and editing

Claude is not available on Vertex AI. If you need the Anthropic model family on Google Cloud, you must use the direct Anthropic API — forfeiting GCP-native security and compliance tooling.

Prices change frequently — always verify against current provider documentation before committing.

The cost differential between Gemini Flash and Claude Sonnet is substantial — roughly 30x on input tokens and 37x on output tokens. For high-volume, cost-sensitive workloads where Gemini’s quality meets the bar, Vertex AI is dramatically cheaper. For workloads where Claude’s reasoning quality justifies the cost, Bedrock’s pricing matches the direct Anthropic API.

Three concrete scenarios show when the choice is obvious and when it requires genuine architectural reasoning.

A digital health company processes PHI (Protected Health Information) in AWS. Their GenAI engineer is tasked with building a clinical documentation assistant that summarizes patient notes and generates structured SOAP notes for physicians.

Constraints: PHI cannot leave the AWS boundary, all AI invocations must appear in CloudTrail for HIPAA audit trails, the model must be covered by a BAA (Business Associate Agreement).

The choice is straightforward: Bedrock. AWS has a BAA covering Bedrock, and Claude’s performance on clinical language tasks is a strong match for the use case. All API calls go through VPC endpoints, authentication is IAM (no separate API key), and CloudTrail captures every InvokeModel call automatically.

Using the Gemini API directly would require routing PHI to a Google endpoint over the public internet — violating the architecture constraint. Vertex AI is not in scope because the organization’s entire infrastructure is on AWS.

A fintech data team runs all analytics in BigQuery. Their data science team uses Vertex AI for traditional ML models (fraud detection, churn prediction). Now they want to build an AI-powered report generation tool that reads from BigQuery tables and produces analyst-ready summaries.

The natural choice: Vertex AI. Gemini can be invoked directly from BigQuery SQL using ML.GENERATE_TEXT, enabling in-place text generation without extracting data to a separate service. Vertex AI Search can index their internal document library (stored in Cloud Storage) for RAG, with no additional ETL. The entire pipeline uses the same IAM policies governing their existing GCP resources.

Standing up Bedrock for this would require cross-cloud data transfer, separate credentials, and a new observability system — all for a use case that Vertex AI handles natively.

An AI-native startup has no existing cloud commitment. They evaluated models extensively and found Claude Sonnet’s instruction-following quality was measurably better for their legal document analysis use case than Gemini. They want managed infrastructure with compliance tooling, not a direct API call.

The choice: Bedrock on AWS. The model requirement (Claude) drives the platform choice, which drives the cloud choice. They set up the minimum viable AWS footprint (VPC, IAM, Bedrock) and begin on-demand.

For teams choosing Bedrock, three capabilities differentiate it from a simple proxy to model providers: Knowledge Bases, Bedrock Agents, and Guardrails.

Knowledge Bases is Bedrock’s managed RAG layer. It abstracts the entire pipeline: ingest documents from a source, chunk them, embed them using a configured embedding model, store vectors in a managed vector store, and expose a retrieval API.

Supported data sources: S3 buckets, Confluence, SharePoint, Salesforce, and web crawler — covering the most common enterprise document repositories. Sync is incremental: Bedrock tracks which documents have changed and re-indexes only those.

Supported vector stores: Amazon OpenSearch Serverless (default), Aurora Serverless with pgvector, Pinecone, Redis Enterprise Cloud, and MongoDB Atlas. The flexibility to bring your own vector store is valuable for teams with existing vector infrastructure.

Retrieval API: Two patterns — Retrieve (returns ranked chunks without generation) and RetrieveAndGenerate (retrieval + generation in one call). Hybrid search (semantic + keyword BM25) is configurable and outperforms pure semantic search on most production query distributions.

The managed trade-off: Knowledge Bases handles operational complexity at the cost of control. Chunking strategies are configurable (fixed-size, hierarchical, semantic) but limited compared to a fully custom pipeline. Teams with specialized chunking needs for structured documents (tables, code, multi-part legal clauses) often build custom retrieval pipelines using the Retrieve API as the retrieval backend.

Bedrock Agents provides managed ReAct-loop orchestration. You define action groups — sets of tools expressed as Lambda function ARNs or OpenAPI schemas — and Bedrock manages the think/act/observe cycle.

The operational advantage: no agent loop code to write or maintain. The limitation: observability into intermediate reasoning is shallow — you see action traces but not the full thought process. For complex agents where debugging intermediate reasoning is important, implementing the loop directly with the Converse API (or LangGraph backed by Bedrock model invocations) gives significantly better visibility.

Guardrails provides a configurable safety layer that sits between the application and the model: content filtering (violence, hate, sexual content), topic denial (block the model from discussing specific subjects), PII detection and redaction, and grounding checks (verify that the model’s answer is supported by retrieved context).

Guardrails apply consistently across all Bedrock model invocations — including Knowledge Bases and Agents — making it the enforcement point for safety policy across an entire application stack. For regulated industries where output safety is an audit requirement, having a managed, policy-driven safety layer that is separate from the model is architecturally significant.

Vertex AI’s differentiation runs in three directions: Gemini’s native multimodality and long context, BigQuery integration depth, and the Agent Builder platform.

Model Garden is Vertex AI’s model catalog. Unlike Bedrock’s multi-vendor marketplace of independent model providers, Model Garden is Google-first with selected open-weight additions.

Gemini 2.0 Flash is the production anchor: 1M token context window, native multimodality (text, images, audio, video), tool use, and code execution. At $0.10 per million input tokens, it is the most cost-effective frontier model available on a managed cloud platform as of early 2026.

Gemini 2.0 Pro is the top-of-range model for complex reasoning tasks where Flash quality is insufficient. It adds significantly better performance on multi-step reasoning, code generation, and ambiguous instruction following — at higher cost.

Grounding with Google Search: Vertex AI’s Gemini can be configured to ground responses against live Google Search results, providing real-time web knowledge without a custom RAG pipeline. For general knowledge Q&A where currency matters and a proprietary corpus is not needed, this is a unique capability with no Bedrock equivalent.

The ML.GENERATE_TEXT SQL function enables Gemini invocations directly within BigQuery queries. This means AI-powered text generation, classification, and extraction can run inside existing data pipelines as SQL operations, with no extract-load-transform step.

SELECT
  document_id,
  ML.GENERATE_TEXT(
    MODEL `project.dataset.gemini_model`,
    STRUCT(
      CONCAT('Summarize this earnings call transcript in three bullet points: ', transcript_text) AS prompt,
      0.2 AS temperature,
      500 AS max_output_tokens
    )
  ).ml_generate_text_llm_result AS summary
FROM
  `project.dataset.earnings_calls`
WHERE
  call_date >= '2026-01-01';

For data engineering teams already operating in BigQuery, this pattern eliminates the need to build a separate AI inference service for batch processing workloads.

Vertex AI Search is the managed RAG equivalent of Bedrock Knowledge Bases, with direct integrations into Google Workspace data sources: Google Drive, Cloud Storage, BigQuery, web crawl, and Cloud SQL.

For organizations using Google Workspace, the Drive integration is particularly valuable: Vertex AI Search indexes Drive documents with no data export pipeline, keeping the index current as documents are updated or added to Drive.

Agent Builder is Google’s managed agent orchestration platform, integrating Gemini with Vertex AI Search for grounding and supporting the same define-tools/manage-loop pattern as Bedrock Agents. It has deeper Dialogflow integration, making it the natural choice for teams migrating from Dialogflow CX conversational AI applications.

The comparison below distills the platform trade-offs into a single view — positive and negative signals per dimension, with a verdict and use-case breakdown.

Headline per-token rates are only part of the cost picture. A complete cost model covers: model inference, managed feature costs (RAG, agents, guardrails), and operational infrastructure.

For high-volume workloads where Gemini Flash quality is sufficient, Vertex AI is dramatically cheaper. A workload processing 1 billion input tokens per month costs approximately:

• Gemini 2.0 Flash: $100/month in input token costs
• Claude Haiku 3.5: $800/month in input token costs
• Claude Sonnet 4.5: $3,000/month in input token costs

This is a 30x cost difference between the cheapest Gemini and cheapest Claude options. For commodity use cases — document classification, extraction, summarization at high volume — Gemini Flash on Vertex AI offers a strong quality-to-cost ratio.

Bedrock Knowledge Bases: OpenSearch Serverless OCU charges ($0.24/OCU-hour), S3 storage for documents, and per-query retrieval charges. For moderate RAG workloads (thousands of queries per day), managed costs typically run $50–$500/month depending on corpus size and query volume.

Vertex AI Search: Charged per query ($2–$4 per 1,000 queries depending on tier) plus storage costs. For high-query-volume RAG applications, per-query pricing can accumulate significantly.

Bedrock Guardrails: Charged per unit processed — $0.75 per 1,000 text units for content filtering. For applications processing every user message through Guardrails, this adds material cost at scale.

Vertex AI grounding with Google Search: $35 per 1,000 grounded queries — premium pricing for real-time web knowledge access.

Both platforms offer reserved/provisioned capacity options. Bedrock Provisioned Throughput requires committing to model units for 1 or 6 months — meaningful for applications with stable, high-volume traffic, but a significant over-commitment for variable workloads.

Vertex AI offers committed use discounts for sustained API usage, with pricing typically 10–20% below on-demand for committed annual spend.

For most teams: start on-demand on either platform. Evaluate committed pricing only after 30+ days of production traffic data.

The right platform choice follows a structured decision tree, not a gut feeling about AWS vs Google.

Claude is a requirement. This is the clearest signal. If your organization has evaluated models and Claude’s quality, safety behavior, or contractual terms are requirements — not preferences — Bedrock is the only managed cloud path. Using the Anthropic API directly forfeits IAM authentication, CloudTrail logging, VPC isolation, and Guardrails.

Your organization is AWS-first with compliance requirements. If the rest of your infrastructure is on AWS and you operate under HIPAA, PCI DSS, FedRAMP, or similar frameworks where your AWS compliance certifications need to cover AI model invocations, Bedrock keeps everything within the certified boundary.

You need a multi-model API. If your application needs the ability to route different tasks to different models — Claude for complex reasoning, Titan for embeddings, Llama for fine-tuned domain tasks — Bedrock’s unified Converse API handles this without managing separate authentication flows for each provider.

Your data stack is Google-native. BigQuery, Google Drive, Cloud Storage, Google Workspace — if this is your infrastructure, Vertex AI’s native integrations reduce RAG pipeline complexity from weeks of engineering to hours of configuration.

Cost at scale is a hard constraint. If you need a frontier model for high-volume workloads and Gemini Flash’s quality meets your bar, the cost advantage over Claude is too large to ignore at production scale. A workload that costs $3,000/month on Bedrock (Claude Sonnet) might cost $100/month on Vertex AI (Gemini Flash) for equivalent output quality on appropriate tasks.

You need real-time web grounding. Vertex AI’s Google Search grounding provides current web knowledge without building or maintaining a web crawl pipeline. For general knowledge Q&A requiring recency, this is a capability Bedrock does not offer.

Your team uses Vertex AI for existing ML models. If your pipeline, experiment tracking, and model monitoring infrastructure is already in Vertex AI for traditional ML, extending it to LLMs avoids introducing a second platform.

Cloud AI platform questions appear in GenAI engineering interviews primarily at companies with significant AWS or GCP footprints, and in solutions architect and platform engineer roles. For a broader set of GenAI interview questions, see the GenAI interview questions guide.

“How would you choose between Bedrock and Vertex AI for a new project?”

Strong answer structure: start with cloud constraint (is the cloud provider already chosen?), then model requirement (is Claude needed?), then data integration (where does source data live?), then cost sensitivity. Demonstrate that you understand the decision is usually driven by organizational context, not platform-level features in isolation. Interviewers flag candidates who give generic answers about “evaluating both platforms” without a structured decision framework.

“How does RAG work differently on Bedrock vs Vertex AI?”

Bedrock Knowledge Bases vs Vertex AI Search are the equivalent managed RAG layers. Key differences: Bedrock supports S3, Confluence, SharePoint, and Salesforce as native data sources. Vertex AI Search supports Google Drive, Cloud Storage, BigQuery, and web crawl. Bedrock’s retrieval API uses Titan or Cohere embeddings by default. Vertex AI Search uses Google’s proprietary embedding models. Both support hybrid search (semantic + keyword). Neither gives full control over chunking granularity — that requires a custom pipeline using the retrieval API.

“What is Guardrails for Amazon Bedrock and why does it matter for compliance?”

Guardrails is a managed safety layer that sits between the application and the model, applying content filtering, PII redaction, topic denial, and grounding verification. Its compliance value: safety policy is enforced at the infrastructure layer, not the application layer, which means policy changes are centralized and every model invocation — including those in Bedrock Agents and Knowledge Bases — applies the same policy automatically. Auditors can verify the Guardrails configuration as a compliance artifact without reviewing application-level prompt engineering.

“A team needs to process 10 million documents per month through an LLM for classification. Should they use Bedrock or Vertex AI?”

This is a cost-sensitivity question. At 10 million documents, even small per-token differences multiply significantly. The right answer is to evaluate Gemini 2.0 Flash on Vertex AI first — at $0.10/million input tokens, it is the lowest per-token cost for a frontier model on any managed cloud platform. If the classification task can be handled within Flash’s quality ceiling, Vertex AI is likely 5–30x cheaper than Claude on Bedrock, depending on the comparison point. If Claude’s reasoning quality is essential for classification accuracy, the cost difference becomes an input to ROI analysis, not an automatic disqualifier.

AWS Bedrock and Google Vertex AI solve the same problem — managed AI model access inside a cloud security boundary — for different cloud ecosystems. Neither is universally superior.

Choose Bedrock when:

• Claude is a hard requirement (it is not available on Vertex AI)
• Your organization is AWS-first and IAM/CloudTrail/VPC compliance applies to AI invocations
• You need a single API for multiple model providers (Anthropic, Meta, Mistral, Cohere)
• Your RAG data sources are S3, Confluence, or SharePoint

Choose Vertex AI when:

• Your data lives in BigQuery or Google Workspace (Drive, GCS)
• High-volume cost sensitivity makes Gemini Flash’s pricing a primary constraint
• Real-time web grounding (Google Search) is a use case requirement
• Your existing ML infrastructure (pipelines, experiments, monitoring) is in Vertex AI

Key operational rules regardless of platform:

• Start on-demand — do not commit to Provisioned Throughput (Bedrock) or committed discounts (Vertex AI) until you have 30+ days of production usage data
• Both platforms use cloud-native IAM — never use API keys for production workloads
• Evaluate managed RAG (Knowledge Bases or Vertex AI Search) before building a custom pipeline — the managed options cover the majority of enterprise use cases with less operational overhead
• Test model quality on your specific task before committing to either platform — benchmark Gemini vs Claude on representative examples from your actual workload

For the broader picture of how Bedrock and Vertex AI fit into the cloud AI platform landscape alongside Azure AI Foundry, see the Cloud AI Platforms comparison guide.

• Cloud AI Platforms Overview — Three-way comparison: Bedrock, Vertex AI, and Azure AI Foundry
• AWS Bedrock Deep-Dive — Complete guide to Bedrock: Knowledge Bases, Agents, Guardrails, and compliance
• Google Vertex AI Deep-Dive — Gemini API, BigQuery integration, Vertex AI Search, and Agent Builder
• RAG Architecture Guide — Building production RAG systems, applicable to both platforms
• AI Agents and Agentic Systems — The agent patterns that Bedrock Agents and Vertex AI Agent Builder implement
• GenAI Interview Questions — Practice questions on cloud AI platforms and architecture trade-offs

Last updated: March 2026. Model pricing and feature availability change frequently — verify current rates against AWS Bedrock pricing and Vertex AI pricing before architectural decisions.