
31 Oct. 2024 - Michael Simonetti, BSc BE MTE - Total Reads 781

Speed, quality, and cost — why you can’t have all three (yet)
Powerful AI comes at a price — and not just financial. Models like GPT-4 and Claude 3 Opus are excellent at reasoning and complex outputs, but they’re slower and more expensive to run than smaller, faster models like GPT-3.5 or Claude Instant. In high-volume enterprise environments, this latency-to-cost ratio can make or break your project.
The Triangle of Pain: Speed, Quality, Cost
In most enterprise use cases, you want:
Unfortunately, current LLM technology only lets you reliably pick two:

If your customer support bot handles 1,000 chats per hour:
Which do you choose? That depends on your use case.
Use a fast, cheap model as your default (e.g. GPT-3.5), and escalate only to a slower, more expensive model when:

When planning AI at scale, don’t just ask “What’s the best model?” — ask “What’s fast enough and smart enough at a cost that scales?” Balancing latency, quality and budget is the difference between a flashy demo and a commercially viable product.
Want help designing AI systems that perform under pressure? AndMine can help you scale smart — not just big.
Go on, see if you can challenge us on "Latency vs. Cost Trade-offs in Enterprise AI" - Part of our 183 services at AndMine. We are quick to respond but if you want to go direct, test us during office hours.
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