Azure OpenAI vs OpenAI API
Two sides of the LLM API decision: through your cloud and model provider. When each fits, what it costs, who moves from one to the other, and what makers who chose it say.
OpenAI APIModel providerllms.txtIn Lovable, ReplitWhich fits you
- OpenAI models inside an Azure account, with regional and EU/US data-zone deployments.
Use it whenYour customers or credits already live on Azure, or you need data kept in a specific region.
Trade-offMore setup than the OpenAI API (Azure resources, deployments per model), and new models can arrive later.
- You want the strongest general models and the widest ecosystem of examples and integrations
Use it whenYou want one account that covers text, images, speech and embeddings, with the most examples to copy from.
Trade-offFrequent model and API changes to keep up with, and no option to run its hosted models elsewhere.
At a glance
| Used by | 23 makers' products · 25 open-source projects | 734 makers' products · 481 open-source projects |
|---|---|---|
| Cost at default usageinput tokens 50 million tokens, output tokens 10 million tokens | — | $10/mo GPT-6 Luna |
| Downloads | 2.2M/wk3.2× vs npm | 34.1M/wk+61% vs npm |
| Pricing | Pay per token (standard), or reserved throughput billed hourly (provisioned); batch is 50% off. · paid from Pay per token | Pay per token. · paid from Pay per token |
| Free tier | No | No |
| Open source | No | No |
| Incidents, 90 daysfrom its status page | no public status feed | 25+ (4 major) |
What makers say
Makers on using it for LLM API, from Product Hunt and Starter Story interviews, each linked to the source. Products with a page of their own and fuller notes first.
The Azure OpenAI service was better than the direct OpenAI one for inferene purposes because it was more closer to our servers and had a much better latency
OpenAI's API might have temporary failures. Azure's OpenAI service is always the fallback we rely on when the OpenAI's API is unavailable.
We're using Azure OpenAI to do the document processing, shout out to the Microsoft Startup Hub for their support too.
We use GPT-5.6 to power the Basedash AI data analyst. It's incredibly intelligent and, with the right harness, allows us to rank #1 on BI Bench for solving real-world BI scenarios.
Credit where it’s due: OpenAI’s dev tools made it incredibly easy to prototype and test multiple ideas fast. Still unmatched when you need raw speed, documentation, and flexibility.
Quven writes subtitles from the audio of a film on the machine of the user. The Whisper models, run locally through whisper.cpp, can do it and the audio never leaves that machine.
Loved and watch-outs
Themes that recur in makers' words and Hacker News comments, each linked to what it summarises.
- Its models are strong at code generation, reasoning and creative tasks, often chosen after testing alternatives. PHPH 2PH 3PH 4
- The API is reliable and well-priced as the foundation for production features. PHPH 2PH 3HN
- Good docs and a smooth developer experience make integration straightforward. PHPH 2PH 3PH 4
