The Tool Desk
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Short answer: choose the platform that matches the cloud estate, identity system, data services, governance tools and model strategy you already operate. Microsoft Foundry is usually the most practical choice for Azure and Microsoft-centered organizations; Amazon Bedrock fits AWS-native teams that need multi-provider model access under AWS controls; and Google Vertex AI, now presented on Google’s product site as the Gemini platform, is strongest when Gemini multimodality, BigQuery and integrated MLOps are central. None is universally cheapest or fastest. Run the same representative workload on the finalists and compare the complete operating bill before committing.
The decision in one view
Cloud fit normally matters more than a feature-count comparison. Reusing your existing identity, networking, storage, security, logging and billing integrations can remove months of integration work and reduce operational risk. The practical estate-fit conclusion is Foundry for Microsoft estates, Bedrock for AWS estates and Google’s platform for Google Cloud- or Gemini-first strategies.
| Choose this platform when… | Why it fits | Verify before committing |
|---|---|---|
| Microsoft Foundry | Your organization runs Azure, Microsoft Entra, Microsoft 365, Azure AI Search, Cosmos DB or Microsoft governance tooling; enterprise policy and networking integration are priorities; access to OpenAI and other models is important. | Current resource type, API version, model-region availability, quota and the exact capabilities available in your subscription. |
| Amazon Bedrock | AWS is the operating base and you want Anthropic, Amazon, Meta, Mistral and other providers behind IAM, AWS networking and AWS billing, with options such as batch inference, routing or provisioned tiers. | Per-model regional availability, account quotas, retrieval and guardrail charges, throughput commitments and the cost of surrounding AWS services. |
| Google Vertex AI (now presented as Gemini platform) | Gemini multimodality, BigQuery, notebooks, custom machine learning and an integrated training-to-monitoring workflow are core requirements. | Exact model and region availability, Cloud resource charges, storage, compute, vector search, pipeline execution and management fees. |
Make the final choice after a pilot that uses your production-like prompts, retrieval corpus, safety rules, latency target, regions and expected request volume. Model catalogs, quotas, prices and product names change frequently.
What the three platforms are in 2026
Microsoft Foundry: a unified Azure control plane
Microsoft’s current name is Microsoft Foundry; Azure AI Studio and Azure AI Foundry are previous names. Foundry places agents, models and tools under one Azure resource and management grouping. Microsoft Learn describes built-in tracing, monitoring, evaluations, Microsoft Entra identity, role-based access control, content filters, network isolation and Azure Policy integration.
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The current Microsoft page advertises access to more than 10,000 models (Microsoft, 2026). That is a catalog claim, not a promise that every model is deployable in every region, subscription, quota tier or production configuration. Treat the catalog as a starting point and check the model card and deployment requirements for the region you will actually use.
You can prototype in the Foundry portal and build with SDKs for Python, C#, JavaScript and Java. Microsoft also lists the Azure Developer CLI, Visual Studio Code support and hosted agents. The unified resource and project client are part of a transition from multiple older resources and endpoints, so an existing Azure AI implementation may require API and resource updates.
Amazon Bedrock: AWS-native access to many model providers
Amazon Bedrock is a managed AWS service for foundation models and generative-AI controls. AWS states that pricing depends on the modality, provider and model. Its pricing page lists Standard, Flex, Priority and Reserved tiers, allowing teams to trade scheduling guarantees and commitment for different economics.
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The main architectural advantage is provider choice without rebuilding the entire AWS security and operations layer. A team can test different model families while retaining IAM, VPC and logging patterns, S3 data pipelines and other AWS services. That convenience does not eliminate model-specific prompt behavior, quotas or regional restrictions.
Google Vertex AI, now presented as the Gemini platform
Google’s former Vertex AI product destination now resolves to a page branded Gemini platform. Readers and existing documentation still commonly say Vertex AI, so the practical description is “Vertex AI, now presented as Gemini platform.” This is a naming transition, not a statement that every older Vertex AI API has been replaced by an identical endpoint.
Google describes Gemini platform as an open, comprehensive platform for building, scaling, governing and optimizing enterprise agents grounded in enterprise data. The product page advertises 200+ Google and third-party models and tools (Google Cloud, 2026), including Gemini, Anthropic Claude and open models such as Gemma.
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Compare the decision axes that affect a real deployment
1. Existing cloud estate and identity
Start with the systems your security and platform teams already operate. Azure estates can reuse Microsoft Entra identities, Azure Policy, Azure networking and Microsoft data services with Foundry. AWS estates can apply IAM, VPC controls, S3 permissions and established AWS observability patterns to Bedrock. Google Cloud estates can connect Gemini platform workflows to BigQuery, notebooks and Google’s data and ML services.
A multi-cloud company should not assume that a single vendor’s native integration automatically wins. Quantify the work to mirror identities, private networking, key management, audit logs, data movement and chargeback in each cloud. The platform with the lowest model-token rate can still cost more if every request requires cross-cloud transfer or duplicate security controls.
2. Model families and portability
Foundry’s advertised catalog exceeds 10,000 models, while Google advertises more than 200 Google and third-party models and tools. Bedrock exposes multiple providers, including Anthropic, Amazon, Meta and Mistral. These counts describe available catalogs, not equal production access: model versions, regions, quotas, safety settings and commercial terms differ.
Portability is limited at the behavior layer. A prompt that works with one model may produce different structure, tool calls or refusal behavior on another. Embedding dimensions, tokenizer behavior, context limits and fine-tuning options can also vary. Keep your application’s model interface separate from provider SDK calls, store prompts and evaluation cases in version control, and make model selection a configuration value where practical.
3. Agents, retrieval and enterprise data
Foundry groups agents, models and tools in one management plane and adds tracing, evaluations, monitoring, filters, RBAC and network controls. Bedrock offers knowledge bases, guardrails, evaluation, prompt optimization, intelligent routing and web search as managed capabilities around foundation models. Google positions Gemini platform around agents grounded in enterprise data and combines that experience with BigQuery, notebooks and its MLOps services.
For a retrieval-augmented generation (RAG) project, compare the full path rather than the chat model alone:
- How documents are ingested, chunked, embedded and indexed.
- Whether the managed retrieval layer meets your tenancy, residency and encryption requirements.
- How citations, filters, reranking and access-control trimming are implemented.
- What tracing and evaluation data is available when retrieval returns the wrong passage.
- Charges for vector storage, indexing, reranking, retrieval calls, model tokens and network transfer.
Bedrock’s knowledge bases may reduce AWS integration effort; Google’s BigQuery and ML tooling may be more natural for a Google analytics estate; and Foundry can align RAG operations with Azure identity, policy and Azure data services. The right answer depends on where the governed source data already lives.
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4. Observability, evaluation and governance
Governance is more than a content filter. Require an audit trail for prompts, retrieved context, tool calls, model responses, policy decisions, human approvals and deployment changes. Foundry’s documented enterprise capabilities include tracing, monitoring, evaluations, RBAC, content filters, network isolation and Azure Policy. Bedrock supplies guardrails and model evaluation among its managed options. Google includes monitoring in the MLOps surface it advertises.
Ask each vendor to demonstrate the same control in your pilot: private connectivity, identity-based access to a document, prompt and response logging, retention controls, red-team evaluation, rollback and an approval path for a new model. “Available” in a product catalog does not prove that the feature is enabled for your region or subscription.
5. Fine-tuning and custom training
Teams that only call hosted models should weight deployment, guardrails and operations heavily. Teams that train or adapt models should examine notebooks, training jobs, pipelines, registries, feature management, evaluation and monitoring as one lifecycle. Google explicitly advertises training, prediction, pipelines, model registry, feature store and monitoring alongside BigQuery-integrated notebooks.
Foundry and Bedrock can still be appropriate for customized applications, but no apples-to-apples comparison of fine-tuning methods, supported algorithms, training prices or resulting quality is established here. Treat those as pilot questions rather than assumed advantages.
6. Regions, latency and quotas
Do not select a model because it appears in a global catalog. Confirm the exact model version, deployment type, region, throughput quota, context limit and data-processing location for the account that will run it. A model may be listed yet unavailable in your required geography or production tier. Measure p50 and p95 latency with your prompt and retrieval payload; no controlled, current benchmark establishes a universal winner among these platforms.
Pricing: compare the bill you will actually receive
There is no universal cheapest platform. Token rates are only one component. Include input and output tokens or characters, retrieval, embeddings, reranking, guardrails, evaluation, storage, compute, network egress, provisioned capacity, logging and support in the same model.
| Platform | Published pricing facts | Costs that need workload-specific modeling |
|---|---|---|
| Microsoft Foundry | Specific prices vary by underlying model, deployment and Azure region; a single cross-platform rate is not established. | Model inference, token volume, Azure AI Search or other retrieval services, storage, networking, monitoring, evaluations, capacity and any committed-use terms. |
| Amazon Bedrock | AWS says pricing depends on modality, provider and model. Standard, Flex, Priority and Reserved tiers are listed. Selected models support batch inference at 50% below on-demand pricing. Intelligent prompt routing may reduce cost by up to 30%, workload-dependent. | Model tokens, tier or throughput commitments, knowledge bases, guardrails, evaluation, data automation, routing, prompt optimization, web search, storage, S3/OpenSearch and network traffic. |
| Google Gemini platform | Google lists $300 in free credits for new customers, text/chat/code generation starting at $0.0001 per 1,000 characters and pipeline execution starting at $0.03 per run (Google Cloud, 2026). | Those are starting figures, not a complete application price. Add model usage, tools, storage, compute, vector search, pipelines, monitoring, BigQuery and other Cloud resources. |
Free credits and starting rates are not recurring production allowances. Record whether a quoted rate is per token, per character, per run, per hour or a commitment, and identify the region and model version. Then calculate at your expected daily and monthly volume, including peak capacity and failure retries.
Is Amazon Bedrock cheaper than Azure?
There is no general yes-or-no answer. Bedrock’s batch and routing options can lower specific workloads, while an Azure-native organization may avoid migration and data-transfer costs by keeping inference and retrieval in Foundry. Compare the same model family, prompt, output length, retrieval steps, safety checks, region and reliability target. Include fixed platform work and engineering time; list prices alone do not answer the question.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesA practical selection process
- Map the estate. List your cloud accounts, identity provider, private-networking pattern, storage, analytics, security policy, logging, procurement and chargeback requirements. Mark which services already hold the data the model must use.
- Define the workload contract. Specify requests per minute, peak concurrency, input and output size, latency targets, availability, retention, residency, human-review rules and acceptable error or hallucination rates.
- Create a three-platform shortlist. Start with the native-cloud candidate, then include any platform whose model or MLOps capability is strategically important. Do not eliminate a platform solely because its catalog count is smaller or larger.
- Normalize the model test. Use identical prompts, system instructions, retrieval documents, tool schemas and evaluation cases. Record structured-output success, grounded-answer accuracy, refusal behavior, token or character usage and latency.
- Test governance paths. Demonstrate least-privilege identity, private access, policy enforcement, content filtering, trace visibility, evaluation runs, incident response and model rollback with production-like data.
- Check availability. For every candidate model, verify region, quota, context window, deployment type, API version and commercial eligibility in the intended account. Capture the date because these values change.
- Build a total-cost model. Separate variable inference from fixed capacity and managed add-ons. Add retrieval, embeddings, reranking, guardrails, evaluation, storage, compute, logs, egress and support. Model ordinary, peak and growth scenarios.
- Run a limited pilot. Use a representative slice of traffic and measure quality, latency, operational effort and cost per successful task. A short pilot is more reliable than extrapolating from a vendor calculator or a public benchmark.
- Choose with a written exit plan. Document the model interface, prompt versions, evaluation set, data schemas, export process and fallback model. This keeps a future migration feasible even after selecting a native-cloud platform.
Which platform fits common business situations?
Microsoft 365 and Azure-first enterprise
Choose Foundry when Microsoft Entra, Azure Policy, Azure networking and Microsoft data services are already your control plane, especially if enterprise delivery needs centralized agents, models, tools, tracing and evaluations. Confirm the new Foundry resource and project-client pattern before freezing an architecture built on older Azure AI names.
AWS operations team seeking provider choice
Choose Bedrock when IAM, S3, OpenSearch, AWS analytics and existing AWS operations are non-negotiable and the business wants to switch among several model providers. Evaluate whether batch, intelligent routing or a reserved tier materially improves your workload, and price every managed add-on used by the application.
Gemini and BigQuery-centered data science
Choose Google’s Gemini platform when multimodal Gemini capabilities, BigQuery-connected notebooks and a full training, pipeline, registry, feature-store and monitoring lifecycle are central. Keep the Vertex AI terminology in internal search and migration documentation so teams can find older references while adopting the current product presentation.
Regulated or data-local workloads
Start with the platform that can place inference, retrieval and logs in the required geography under your existing policy controls. Validate every component separately: the model endpoint, embeddings, vector index, evaluation data, backups, telemetry and support access. A regional model gap can outweigh a favorable catalog or token price.
Multi-cloud application
Keep a provider-neutral service boundary for prompts, messages, tool calls, usage accounting and safety outcomes. Run the same corpus and evaluation set on each platform, but allow provider-specific adapters for identity, retrieval and observability. Compare operational cost at expected volume, not just published token prices.
Migration and architecture safeguards
- Separate business logic from SDKs. Your application should call an internal interface for generation, embeddings, tool use and moderation; provider adapters translate that interface to Foundry, Bedrock or Gemini APIs.
- Version everything that affects output. Store system prompts, templates, model identifiers, retrieval settings, safety policies, tool schemas and evaluation cases with deployment metadata.
- Keep data access explicit. Pass only the documents and fields a request is authorized to see. Do not treat a model’s ability to retrieve a document as proof that the user may read it.
- Budget for change. Catalog entries, quotas, regions, API versions and brand names change. Schedule a quarterly review and re-run a small quality and cost suite after material model or policy updates.
- Measure successful work. Report cost per accepted answer, completed workflow or resolved case, not only cost per million tokens. Include retries, human review and failed retrievals.
Pitfalls that produce misleading comparisons
- Comparing catalog counts as if they were capacity. The Foundry figure is more than 10,000 models; Google’s figure is 200+ models and tools. Neither number guarantees regional deployment, quota or identical support.
- Using one model’s price as the platform price. Bedrock’s modality, provider and tier rules and Google’s separate tool, storage, compute and Cloud charges show why a complete bill is necessary.
- Ignoring the data path. Cross-cloud retrieval, duplicated indexes, egress and logging can dominate inference charges.
- Assuming names imply identical APIs. Vertex AI references now lead readers to Gemini platform, and Foundry replaced several older Azure AI resource patterns. Confirm current documentation and API versions.
- Trusting an unrepresentative demo. A short prompt with no retrieval, tools, policy checks or concurrency says little about an enterprise workload.
- Locking in before checking quotas. A model that works in a development account may have different limits or availability in the production region.
Bottom line
For an Azure and Microsoft governance estate, start with Microsoft Foundry. For an AWS operating base and multi-provider model strategy, start with Amazon Bedrock. For Gemini multimodality, BigQuery and integrated MLOps, start with Google’s Vertex AI experience now presented as Gemini platform. Treat those as starting points, not permanent winners: normalize the workload, verify regional and quota details, price every component and keep the application interface portable enough to change models when quality, availability or economics shift.
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