How to Choose an LLM Provider for Your Business

There are now many capable large language models from major AI labs, cloud providers and open-weight model families. Choosing among them can feel overwhelming. A structured approach makes it manageable.

Start with your use cases

Different tasks favour different models. Complex reasoning and agentic work may need a frontier model; classification and extraction may run well on small, cheap models. List your tasks first.

Evaluation criteria

Quality on your tasks

Public benchmarks are a rough guide. What matters is performance on your data. Build a test set and compare models side by side.

Cost

Compare cost per task, not just price per token. Longer outputs, retries and context size all matter.

Latency

Real-time chat and voice need fast responses; overnight batch jobs do not.

Privacy and data use

Check whether your data is used for training (enterprise terms usually exclude it), how long it is retained and who can access it.

Data residency

For customers in India, the UAE, Saudi Arabia or regulated sectors, check whether models are available in local or acceptable regions.

Reliability and support

Uptime, rate limits, service levels and enterprise support.

Ecosystem

Tool use, structured outputs, long context, multimodal support and integration with your cloud.

Hosted vs open-weight models

• Hosted APIs: best quality and simplicity, less control
• Open-weight models: more control, private hosting and customisation, but more operational work

Avoid lock-in

Put a thin abstraction layer between your application and model providers, keep prompts and evaluation sets portable, and design to switch models per task.

Review regularly

The model landscape changes quickly. Re-run your evaluations every few months — a cheaper or better option may have appeared.

Our generative AI development team runs model selection as part of every project's discovery.