Is This the Cheapest Way to Use AI in 2026? (65+ Models Tested) | Iskander Malikov, Trioma

Eye on AI · 2026-10-16

Is This the Cheapest Way to Use AI in 2026? (65+ Models Tested) | Iskander Malikov, TriomaEye on AI ▶ Play the video

Why it earned a slot

Iskander Malikov’s Trioma platform promises an “auto-select” layer that routes each request to the cheapest model among 65-plus candidates, effectively turning model selection into a runtime decision. The claim that a vertically integrated stack can out-cost major cloud providers hinges on tight hardware-software co-design and aggressive latency budgeting. This episode highlights a pragmatic path to cost control that many enterprises overlook when they chase the biggest models.

The short version

Your AI bill could shrink by half with smarter model routing.

Why it matters

AI spend is now a top-line expense for most tech companies, and the marginal cost of a single query can dominate profit margins at scale. A dynamic selection engine that matches workload characteristics to the most efficient model can deliver big savings without sacrificing quality. The challenge is building a routing layer that respects latency SLAs while staying transparent to downstream services.

My take

I work in AI and have watched cost explode when teams lock into a single, over-engineered model. Trioma’s approach of exposing a catalog of 65-plus models and letting the platform decide is a sensible, engineering-first answer. It also forces you to think about model provenance and versioning, because the routing logic must be deterministic. The upside is a self-optimizing stack that can adapt as new, cheaper models appear.

How it connects

• Dynamic model routing → lower per-query cost
• Vertical integration → tighter latency control
• Model catalog hygiene → reproducible results

Bottom line

Implement a prototype router that benchmarks at least three model families against your key workloads and measures cost per token.