We run production LLM workloads on all three of these platforms across different clients, and we get asked the same question every couple of weeks: which one should we standardise on? The honest answer is that the right choice almost always falls out of two questions about your business — your compliance posture and where the rest of your infrastructure already lives — long before it has anything to do with raw model quality.
What follows is what we actually look at when we're recommending a path. Treat the model rankings as a snapshot; they shift every quarter. Treat the operational characteristics as more durable.
The decision in one paragraph
If you are already an enterprise Azure shop with a Microsoft EA, use Azure OpenAI; the integration story and the compliance paperwork will save you weeks. If you are deep in the AWS ecosystem and you want to mix open-source and frontier models behind one API, use Bedrock. If you are an early-stage team that values being on the latest model the day it ships and you do not care which cloud you live in, use OpenAI's API directly. The rest of this post is the nuance behind that summary.
Model availability and freshness
OpenAI's direct API is always first to a new model. Azure OpenAI typically follows within days to a few weeks for the same OpenAI models, with the same names, at very similar pricing. Bedrock is a different proposition — instead of racing for OpenAI's latest, it gives you a curated menu of frontier and open-source families (Anthropic Claude, Meta Llama, Mistral, Cohere, Amazon's own Nova) behind one consistent API.
What this means in practice: if your product roadmap depends on being on the bleeding edge of OpenAI capabilities, the direct API is where you live. If you would rather have choice between providers without rebuilding your integration every time, Bedrock pays for itself the first time you swap model families.
Latency and throughput
On streaming chat completions with comparable models, the three platforms are within noise of each other for typical single-request latency. Where they diverge is under sustained load.
- OpenAI direct API: generous default rate limits at scale, but requests share a global pool. We have seen visible latency increases during major launch events and US-business-hours peaks. Provisioned throughput is available but expensive.
- Azure OpenAI: rate limits are per-deployment and predictable. Provisioned Throughput Units (PTUs) give you genuinely deterministic capacity in exchange for a commitment, which is usually the right answer once you cross a few million daily tokens.
- Bedrock: on-demand limits are workable for small workloads and visibly tight for large ones. Provisioned Throughput is the intended path for production; budget for it if you are running anything serious on Anthropic models.
Compliance and data handling
This is usually where the decision is made for our enterprise clients. All three providers will sign a BAA. All three offer regional data residency. The differences:
- Azure OpenAI inherits Azure's very mature enterprise compliance posture. If your security team has already approved Azure for other workloads, the review is usually a rubber stamp. Data is not used for training and stays inside the tenant region you select.
- Bedrock works the same way: data never leaves your AWS account boundary, and it's not used to train the underlying model providers. For AWS-native organisations this is the path of least friction.
- OpenAI direct API data is also not used for training by default and supports zero-data-retention on request — but the surrounding paperwork is shorter and less mature than the two hyperscaler offerings, which can slow enterprise review.
Diagram
Where your data lives on each platform
Bedrock and Azure OpenAI keep your data inside your cloud account; the direct OpenAI API does not.
Pricing
On the same OpenAI model, Azure OpenAI and OpenAI direct quote the same per-token pricing within a few percent. Bedrock prices are model-family-dependent — Anthropic Claude is in the same ballpark as comparable OpenAI models; open-source options are dramatically cheaper.
The harder cost question is provisioned capacity. PTUs on Azure and Provisioned Throughput on Bedrock both demand monthly commitments measured in thousands of dollars per unit. They are absolutely the right call for high-volume production traffic, and absolutely the wrong call for experimentation. We almost always start clients on on-demand and migrate to provisioned only after we have a real traffic curve.
Developer experience
OpenAI's SDK is the cleanest of the three and the reference implementation that everyone else mimics. Azure OpenAI uses the same SDK with a different base URL and deployment-based model routing — a 30-minute adjustment, no more. Bedrock's API is more verbose because it spans multiple model families, but the AWS SDKs are well-maintained and the recently shipped Converse API papers over most of the per-model differences.
If you are building a multi-provider abstraction internally, tools like LiteLLM and the AI SDK can flatten all three behind a common interface. We use these on most engagements and they are good enough that the underlying provider choice stops being a code-level concern within a week.
So which one
For most of the production engagements we run, the decision shakes out like this:
- Pick Azure OpenAI if your team already runs production on Azure, you need predictable enterprise paperwork, and you want OpenAI models specifically.
- Pick Bedrock if you are AWS-first and you want optionality across model families — including open-source — without managing the inference infrastructure yourself.
- Pick the OpenAI direct API if you are an early-stage team that values shipping on day-one of a new model and does not need a hyperscaler-grade compliance story yet.
And whichever one you pick: build the abstraction layer early. The platforms move quickly, the relative pricing shifts, and the team that can swap providers in an afternoon will quietly outperform the team that locked in two years ago and can't justify the migration.