Azure OpenAI vs AWS Bedrock vs Vertex AI (2026)

Choosing between Azure OpenAI, AWS Bedrock and Google Vertex AI usually comes down to three things: which cloud you already run, how strict your data-residency rules are, and which models you actually need. All three are mature, enterprise-grade platforms in 2026 — there is no single winner, only a best fit for your situation. Here is an honest, vendor-neutral breakdown to help you decide.

We are an AI consultancy, not a reseller for any of these clouds, so the goal here is clarity rather than a recommendation you cannot question.

The short answer

• Already on AWS, or want the widest model choice? Amazon Bedrock gives you Claude, Llama, Mistral, Cohere, Amazon Nova and more behind one API.
• Microsoft-first shop? Azure OpenAI (within Azure AI Foundry) has the cleanest enterprise data-processing terms and tight Microsoft 365 integration.
• On Google Cloud, or need very long context? Vertex AI and the Gemini family are the natural pick.

If you take nothing else away: pick the platform that matches your existing cloud and your compliance needs first, and treat the model menu as the tie-breaker.

Amazon Bedrock: model breadth and EU options

Bedrock's biggest strength is choice. One API gives you access to several model families — Anthropic's Claude, Meta's Llama, Mistral, Cohere, Stability and Amazon's own Nova — so you can swap models without re-architecting your application. For teams that want to avoid lock-in to a single model provider, that flexibility is genuinely valuable.

For European organisations, Bedrock has invested in EU data-residency options, including EU inference for some Claude models, which matters when personal data is involved. As always, default behaviour is not the same as a contractual guarantee — confirm the specifics for your region and model in writing.

Azure OpenAI: the Microsoft-first choice

If your business already lives in Microsoft 365 and Azure, Azure OpenAI is the path of least resistance. It offers the GPT family with well-documented, contractually enforceable enterprise commitments: by default your data is not used to train the underlying models, and Microsoft's data-processing terms are among the clearest in the market. Add strong SLAs and integration with tools your team already uses, and it is a comfortable fit for regulated, Microsoft-centric enterprises.

The trade-off is model breadth: you are primarily in the OpenAI ecosystem, so if you want to A/B different model families, you have less room than on Bedrock.

Google Vertex AI: Gemini and long context

Vertex AI is the strongest pick if you are already on Google Cloud or if your workloads lean on the Gemini family's long-context strengths — think document-heavy analysis, large codebases, or knowledge systems that need to reason over a lot of text at once. Vertex also has a solid MLOps story for teams that want to train, tune and serve their own models, not just call an API.

As with the others, the deciding factor is usually gravity: if your data and tooling are already in Google Cloud, Vertex removes a lot of integration friction.

How to actually choose

In practice the decision is rarely about benchmark scores, which change every few months anyway. It is about fit. We weigh four factors with clients:

• Existing cloud. The platform that matches where your data and team already are will almost always win on total cost and speed.
• Data residency and compliance. Under the GDPR and the EU AI Act, where data is processed and what the contract guarantees matters more than a marketing claim. Get residency commitments in writing.
• Model needs. Do you need one excellent model, or the freedom to switch between several? That single question often separates Bedrock from the other two.
• Team skills and MLOps. If you plan to fine-tune and operate your own models, weigh each platform's tooling, not just its model menu. Clean, well-governed data engineering underpins all of it.

Benchmarks move monthly; your cloud, your compliance posture and your team do not. Anchor the decision on those.

The Dutch and EU angle

For organisations in the Netherlands and the wider EU, data residency and lawful processing are usually the deciding constraints, not raw capability. The honest position in 2026 is that all three providers offer routes to compliant deployment, but the details — which model, which region, which contractual terms — vary and change. This is general guidance, not legal advice; verify the current position for your specific use case before you commit.

How the three platforms bill you

Pricing on all three is mostly usage-based — you pay per token (or per character on some Google models), split between input and output, with higher rates for the most capable models. That makes costs predictable per call but easy to underestimate at scale, especially with long prompts, retrieval-augmented context, or chatty agents that loop. The practical advice is the same everywhere: prototype on a mid-tier model, measure real token usage on your own traffic, and only move to a frontier model where the quality genuinely pays for itself.

Two cost factors are easy to miss. First, data movement and surrounding services — storage, vector databases, networking — often cost more than the model calls themselves once a system is live. Second, committed-use or provisioned-throughput discounts can change the maths significantly for steady, high-volume workloads, so model the discounted price, not the on-demand sticker.

Common mistakes when choosing a platform

• Picking on a benchmark, not a fit. A model that tops a leaderboard this month may be mid-table next quarter; your cloud and compliance needs are far more durable criteria.
• Assuming residency instead of contracting it. "Available in an EU region" is not the same as a written guarantee that your personal data stays there. Get it in the contract.

• Ignoring exit costs. Building tightly against one provider's proprietary features makes switching painful later. A thin abstraction layer keeps your options open — one reason Bedrock's multi-model API appeals to teams wary of lock-in.
• Forgetting who operates it. The best platform for a team with deep AWS skills is rarely the best for a Microsoft-only shop. Match the tool to the people who will run it.

Multi-cloud, portability and avoiding lock-in

A question we are asked constantly: should you commit to one platform or stay portable across several? For most organisations, a pragmatic single-platform choice beats a complex multi-cloud setup you cannot staff. Running the same workload across Azure, AWS and Google at once multiplies your operational surface — three billing models, three security postures, three sets of quirks — and the savings rarely justify it. Pick the platform that fits, and revisit only if a concrete need appears.

That said, you can reduce lock-in cheaply without going fully multi-cloud. Keep your prompts, retrieval logic and business rules in your own codebase rather than buried in a provider's proprietary feature. Put a thin abstraction between your application and the model API so that swapping a model — or even a provider — is a configuration change, not a rewrite. Bedrock's single-API access to many model families is attractive precisely because it bakes this portability in, but you can achieve much of the same discipline on any platform with good engineering. The goal is not to avoid commitment; it is to make your eventual exit affordable.

Security, privacy and the EU AI Act

For Dutch and European organisations, the security and compliance story usually outweighs raw capability. All three platforms offer enterprise-grade security — encryption in transit and at rest, private networking, identity controls, and a stack of certifications such as ISO 27001 and SOC 2. The meaningful differences sit in the contractual detail: whether your data can be used to improve the provider's models (by default it should not be on enterprise tiers), where inference physically happens, and what the data-processing agreement actually guarantees.

The EU AI Act adds obligations that scale with the risk of your use case, and the GDPR continues to govern any personal data your system touches. Neither is a reason to avoid these platforms — all three can be deployed compliantly — but both are reasons to read the contract rather than the marketing page. Confirm the region, the model, and the residency commitment in writing before you process real personal data. This is general guidance, not legal advice; for a specific obligation, check the official EU sources or qualified counsel. Strong, well-governed data engineering underneath makes all of this far easier to evidence when an auditor asks.

Run a two-week bake-off before you commit

Benchmarks from a blog will not tell you which platform is right for your data and your team. A short, structured bake-off will. Take one real use case, implement a thin version on your two most likely platforms, and measure what actually matters: answer quality on your own data, latency under realistic load, cost per request at your expected volume, and how quickly your team can build and debug on each. Two weeks is usually enough to surface the differences that a feature comparison hides.

• Use your own data and prompts, not the vendor's demo set — that is where platforms diverge.
• Measure total cost, including storage, retrieval and networking, not just model tokens.
• Score the developer experience, because the platform your team can operate confidently will win over three years regardless of who tops this month's leaderboard.

This evidence-first habit is the same one we bring to client work: prove it small, then scale what works.

A quick recommendation by scenario

To make this concrete, here is how the decision usually lands for the situations we see most often:

• Microsoft 365 shop, regulated sector: Azure OpenAI. The clean enterprise terms, SLAs and native integration outweigh model breadth, and your team already knows the surrounding tooling.
• AWS-native team that wants to A/B models: Amazon Bedrock. One API across Claude, Llama, Mistral and Nova lets you test and switch without re-architecting, and the EU inference options help with residency.
• Google Cloud shop, or document-heavy analysis: Vertex AI. Gemini's long-context strengths and native BigQuery/Google Cloud integration remove a lot of friction.
• Not committed to any cloud yet: start where your data already lives, or where your team is strongest. The model menu is a tie-breaker, not the headline.

These are starting points, not verdicts. The right answer for your organisation depends on details a generic guide cannot see — which is exactly why a short, structured evaluation beats a long argument about benchmarks. Whatever you choose, design for portability, contract for compliance, and measure on your own data before you scale.

Key takeaways

• There is no universal winner. Azure OpenAI, AWS Bedrock and Vertex AI are all enterprise-grade; the best one is the one that fits your cloud, compliance and model needs.
• Cloud gravity usually decides it. The platform that matches where your data and team already live wins on cost and speed far more often than any benchmark.
• Bedrock for breadth, Azure for Microsoft fit, Vertex for Google-native and long context. Use that as your starting hypothesis, then validate.
• Contract your compliance. Under the GDPR and EU AI Act, written residency and data-processing terms matter more than any marketing claim.
• Prove it small. A two-week bake-off on your own data beats months of debate, and an abstraction layer keeps your future options affordable.

The bottom line

There is no universally best platform among Azure OpenAI, AWS Bedrock and Vertex AI. Bedrock wins on model choice and EU options, Azure on Microsoft integration and clean enterprise terms, Vertex on Google-native workloads and long context. Decide on cloud fit and compliance first, model menu second, and you will rarely regret the choice. If you would like a neutral second opinion mapped to your actual use case, book a free consultation or review our transparent pricing — we will help you pick the platform that fits, not the one we sell.

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