Choosing a model provider for your agents

Start from the task

Teams often begin by asking which model is the best. The more useful question is which model is good enough for this task, at a cost and speed you can live with, in a place your data is allowed to go.

A hospital in Surat reading discharge summaries, a D2C brand answering order questions in Gujarati and a law firm reviewing NDAs need different things. One model may serve all three, but it is rarely the cheapest or fastest choice for each.

AksharAI works with leading providers, such as OpenAI, Anthropic Claude and Google Gemini, and with open models you host yourself. The choice is made per application and can be changed later.

Five questions that decide most choices

1. How hard is the reasoning?

Extracting fields from a typed invoice, classifying a ticket or answering from a short FAQ needs less reasoning than reviewing a contract against a playbook or reconciling a ledger with several exceptions. Larger models handle complex, multi step work better. Smaller, faster models are often enough for well defined steps.

A useful pattern is to split the task: a smaller model does the routine steps, a larger one handles the hard cases or the final check.

2. Which languages and scripts?

If your customers write in Hindi, Gujarati, Tamil or a mix with English, test each candidate on real messages, including spelling variations and voice note transcripts. Quality in Indian languages varies between models and between versions. Also note that Indian scripts usually take more tokens per word, which affects cost and context limits.

3. How much volume, and how fast?

A support agent answering several thousand WhatsApp messages a day has very different cost and speed needs from a legal copilot used twenty times a week. For high volume work, the price per call and the time to answer matter a great deal. For voice, the answer has to start within a second or two, which rules out slower models for the live conversation.

4. Where may the data go?

This often decides the matter. Ask:

• Is there personal data, such as names, phone numbers, health or salary details?
• Does a contract, regulator or internal policy require data to stay in India or in your own environment?
• Does the provider offer settings that stop your data being used for training, and in which regions does it process data?

If data must not leave your environment, an open model running in your private cloud or on premise may be the only option, even if a hosted model scores a little better. Running AI on premise or in a private cloud covers what that takes.

5. Whose account pays?

You can use our provider accounts or bring your own. Bringing your own gives you direct billing, your own data settings and your own limits. It also means your team manages the account.

Test candidates on your own cases

Shortlist two or three models and run each on the same test set of real past cases, as described in evaluating an AI agent before launch. Compare:

• how often each is correct, fixable or wrong,
• how often each hands over when it should,
• the cost per case at your expected volume,
• the time to answer.

A cheaper model may match a larger one on a narrow task, and a model that scores well in published comparisons may struggle with your documents.

Plan to change

Providers release new models several times a year, prices move, and older models are retired. Design for that:

• Keep instructions, tools and checks independent of any one model where you can.
• Keep the test set, so you can try a new model in an afternoon.
• Record which model and version produced each run, so you can trace a change in results.

On AksharAI the model is a setting on each step, and the run log records which one was used. Switching takes a test run and a decision; the rest of the agent stays as it is.

A starting point

For a first agent, many teams use a well known hosted model from a leading provider for the main reasoning, with settings that keep their data out of training, and keep personal data to the minimum each step needs. They revisit the choice once they have a test set and real volumes. That is a sensible default. If your data cannot leave the building, start with an open model and accept a little more engineering work.