AI Model Selection: Balancing Cost, Latency & Quality for PMs

The biggest AI model is rarely the right one. Vineet Kumar Sehrawat opens this video with that point: every AI product trades quality against speed and cost, and product managers are the people who make that call. The video offers a way to choose between models without guessing, built around one rule: pick the cheapest model that is good enough for the job.

Why bigger is not automatically better

Bigger models are usually smarter, but they are also usually slower and costlier. The video stresses that the right choice depends on the task and is found by testing, not by reputation. Improving one property tends to move another, so model selection is a balancing act.

The five levers

The video names five levers: quality, response speed, token usage, infrastructure capacity, and cost. The remaining sections cover the last four.

Cost follows tokens

Providers usually charge both for the tokens you send in and for the tokens the model writes back, and output often costs more. Long prompts, long documents and long answers all add up. The suggested check is to multiply the cost of one request by your expected daily traffic and compare the result with what a user is worth.

Speed has two parts

Speed is split into how long until the first words appear and how long the full answer takes. Streaming makes a wait feel shorter. Larger models are usually slower, and long outputs take longer. If the user is waiting, for example in a chat, speed is part of the product rather than a technical detail.

Infrastructure capacity, the hidden constraint

Providers set rate limits, and traffic spikes can cause slowdowns or errors. If you host models yourself, you need enough hardware for the peak, not just the average. The advice is to plan for peaks, add fallbacks, and know what happens when a model is unavailable.

Tactics for balancing the levers

The video lists several ways to reduce cost without giving up quality:

• Use a smaller model for easy tasks and a larger one for hard tasks, routing each request to the right model.
• Shorten prompts.
• Cache repeated answers.
• Limit output length.

Often a mix of models beats any single model.

A selection framework

First, define the quality bar and the latency and cost budgets. Next, test two or three candidate models on your own real cases, using the same rubric for each. Compare quality, speed and cost side by side, then pick the cheapest one that clears the bar. Recheck as new models arrive.

Key takeaways

• The biggest model is rarely the right model; choose the cheapest one that is good enough.
• The five levers are quality, response speed, token usage, infrastructure capacity, and cost.
• Cost follows input and output tokens, and output often costs more.
• Speed means time to first words and time to the full answer; streaming helps perception.
• Plan capacity for peak traffic, and have fallbacks for when a model is unavailable.
• Routing, shorter prompts, caching and output limits can cut cost.
• Test candidates on your own cases with one shared rubric.

Try it yourself

Describe your feature to Claude and ask it to help you set a quality bar, a latency budget and a cost budget per request. Have it suggest how to compare three candidate models on your own test cases, and which tactics such as routing, caching or shorter prompts could cut cost without hurting quality. Then ask for a monthly cost estimate at your real traffic and check it against current provider pricing, since prices change often. The video is part of the Project Medhavy series.