How to use AI APIs: OpenAI, Claude and Gemini compared
OpenAI, Anthropic (Claude) and Google (Gemini) all offer APIs that let your software send text, images and documents to their models and receive answers, call tools and return structured data. Each has tiers from small, cheap models to frontier ones, pay-per-token pricing, prompt caching and batch discounts. Claude is also available on AWS, Google Cloud and Microsoft; Gemini runs on Google Cloud; OpenAI models are also on Microsoft Azure. Choose by testing your own tasks on two or three models.
What is an AI API?
An AI API lets your software use a language model directly instead of through a chat app. You send a request with instructions, the user’s input and optional documents, images or tool definitions, and receive the model’s response. Businesses use AI APIs to add features to products, automate document work, power support assistants and build agents. If APIs are new to you, read what an API is first.
| OpenAI | Anthropic (Claude) | Google (Gemini) | |
|---|---|---|---|
| Model range | Small to frontier GPT models | Haiku, Sonnet, Opus and Fable | Flash to Pro and top-tier Gemini models |
| Pricing | Per token; caching and batch discounts | Per token; caching and batch discounts | Per token; caching and batch discounts |
| Cloud availability | OpenAI and Microsoft Azure | Anthropic, AWS Bedrock, Google Vertex AI, Microsoft Foundry | Google AI Studio and Vertex AI |
| Strengths often cited | Broad ecosystem and tooling | Long documents, writing, coding, agents | Google integration, long context, multimodal |
| Tool connections | Function calling, MCP support | Tool use, MCP | Function calling, MCP support |
Model names and prices change frequently; check each provider’s pricing page. Our Claude API beginner’s guide shows Anthropic’s current prices as an example.
Which features matter most?
- Tool use: The model can call your functions or APIs to fetch data or act.
- Structured output: Responses in JSON that match a schema you define.
- Long context: Large documents or many files in one request.
- Vision: Reading images, charts and scanned documents.
- Prompt caching: Lower cost for repeated context.
- Batch API: About half price for jobs that can wait.
Why does cloud availability matter?
Large organisations often prefer to use AI through the cloud platform they already trust, for billing, security reviews and data residency. Claude models are available through Amazon Bedrock, Google Cloud Vertex AI and Microsoft’s platforms as well as Anthropic directly; OpenAI models are available on Microsoft Azure; Gemini runs on Google Cloud. For a company standardised on one cloud, that can narrow the choice before any testing begins.
How do you choose between them?
- Define the task and quality bar: For example, “extract eight invoice fields with 95% accuracy”.
- Collect 20–50 real examples: With the correct output for each.
- Pick one model per provider at the right tier: Compare like with like: small with small, frontier with frontier.
- Run identical prompts: Adjust only where a provider’s format requires it.
- Score accuracy, speed and cost: Calculate cost per completed task.
- Check practical fit: Cloud, data terms, rate limits and support.
This follows our broader AI model decision framework.
How do you avoid lock-in?
What does a real evaluation look like?
A legal-tech startup needs to summarise contracts into a fixed template of parties, dates, obligations and termination terms. It collects 40 contracts with lawyer-written summaries, then tests a mid-tier and a frontier model from each provider with the same prompt and JSON schema. Mid-tier models from two providers reach the accuracy bar at a fraction of the frontier cost; one handles very long contracts more reliably. Because the company runs on AWS, it picks the option available through Bedrock that meets the bar, keeps the test set in its repository and re-runs it each quarter.
How do you make a first call?
- Create an account and key: Sign up with the provider and store the key securely on your server.
- Install the SDK: Use the official library for your language.
- Send a simple request: Choose a model, add a system instruction and a user message.
- Ask for structured output: Define a JSON schema for predictable results.
- Log tokens and cost: Track usage from the first request.
How do you control costs?
- Use the smallest tier that passes your tests
- Route simple requests to small models
- Cache repeated instructions and documents
- Use batch APIs for background jobs
- Track cost per feature and per task
Our guide to cutting LLM API costs covers these techniques in detail.
Whichever provider you start with, design so you can change your mind later.
Keep a short decision record explaining why you chose each model.
How UIUX Studio helps
We help teams evaluate AI APIs on their own tasks and build features that can switch providers as models improve. Talk to our product engineering team.
Frequently asked questions
Which AI API is best?
It depends on your tasks; test two or three on real examples.
Are AI APIs expensive?
Small models are very cheap; frontier models cost more. Routing and caching help.
Can I use several providers?
Yes. Many products route different tasks to different providers.
Is my data used for training?
Business API terms from major providers generally exclude training on customer data by default; check each provider’s terms.
Do I need a cloud account?
Not necessarily; each provider offers direct API access.