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OpenAI vs Azure: How to benchmark

Whether you use GPT through the OpenAI or Azure APIs, the results are pretty similar. But there are some key differences:

  • Speed of inference
  • Frequency of model updates (Azure tends to move more slowly here) and therefore variation between models
  • Variation in performance between Azure regions
  • Cost
  • Ease of integration
  • Compliance with data regulations

This guide will walk you through a systematic approach to comparing these models using the promptfoo CLI tool.

The end result will be a side-by-side comparison view that looks like this, which includes timing information and outputs.

openai and azure comparison

Prerequisites​

Before we get started, you need the following:

  • An API key for OpenAI and Azure OpenAI services.
  • Install promptfoo.

Additionally, make sure you have the following environment variables set:

OPENAI_API_KEY='...'
AZURE_OPENAI_API_KEY='...'

Step 1: Set up the models​

Create a new directory for your comparison project and initialize it:

npx promptfoo@latest init openai-azure-comparison

Edit your promptfooconfig.yaml to include both OpenAI and Azure OpenAI as providers. In this case, we're going to compare GPT 3.5 on both services.

providers:
  - id: openai:chat:gpt-3.5-turbo
  - id: azureopenai:chat:my-gpt-35-turbo-deployment
    config:
      apiHost: myazurehost.openai.azure.com

Make sure to replace the above with the actual host and deployment name for your Azure OpenAI instances.

Optional: configure model usage​

For each provider, you may configure additional parameters such as temperature and max_tokens:

providers:
  - id: openai:chat:gpt-3.5-turbo
    config:
      temperature: 0
      max_tokens: 128
  - id: azureopenai:chat:my-gpt-35-turbo-deployment
    config:
      apiHost: your_azure_openai_host
      temperature: 0
      max_tokens: 128

Step 2: Create prompts and test cases​

Define the prompts and test cases you want to use for the comparison. In this case, we're just going to test a single prompt, but we'll add a few test cases:

prompts:
  - 'Answer the following concisely: {{message}}'

tests:
  - vars:
      message: "What's the weather like in Paris today?"
  - vars:
      message: 'Summarize the latest news on Mars exploration.'
  - vars:
      message: 'Write a poem about the sea.'

Step 3: Run the comparison​

Execute the comparison using the promptfoo eval command:

npx promptfoo@latest eval --no-cache

This will run the test cases against both models and output the results.

We've added the --no-cache directive because we care about timings (in order to see which provider is faster), so we don't want any

Step 4: Review results and analyze​

After running the eval command, promptfoo will generate a report with the responses from both models.

Run promptfoo view to open the viewer:

openai and azure comparison

Inference speed

In this particular test run over 25 examples, it shows that there is negligible difference in speed of inference - OpenAI and Azure take 556 ms and 552 ms on average, respectively.

Once you set up your own test cases, you can compare the results to ensure that response time and latency on your Azure deployment is consistent.

Output accuracy & consistency

Interestingly, the outputs differ despite the speed and temperature being set to 0.

The comparison view makes it easy to ensure that the accuracy and relevance of the responses are consistent.

Next steps​

Once you've set up some test cases, you can automatically test the outputs to ensure that they conform to your requirements. To learn more about automating this setup, go to Test Assertions.