You’ve probably felt the cold sweat of checking your AI API bill at the end of the month. You’re not alone—giants like Uber and Salesforce are publicly bleeding cash on AI spend faster than their finance departments predicted. The panic is real, and it’s driving a new wave of startups promising to fix it.
Enter Tokenless, a Y Combinator-backed API gateway that dynamically routes your AI agent traffic turn-by-turn between different models. The pitch is intoxicating: get frontier-level intelligence for half the cost by sending hard turns to Claude or GPT-4, and easy turns to cheaper open-source models.
It sounds brilliant. But it’s fundamentally solving the wrong problem.
Optimizing for model intelligence without optimizing for cache is like upgrading your car’s engine while leaving the parking brake on.
Here’s the paradox the AI industry is quietly ignoring. The biggest lever in AI cost reduction isn’t routing to a cheaper model. It’s caching. When you run long, successive agentic workflows, your LLM builds a “hot cache.” This context window is stored and reused, reducing your input token costs by up to 90%.
If you use a tool like Tokenless to switch models mid-session to save a few pennies on output tokens, you instantly destroy that hot cache. The next turn your agent takes, it has to send the entire massive context window back to the new model at full price. You didn’t save money; you just paid a 10x premium on input costs to save a fraction on output.
Tokenless acknowledges this, building a router that only switches models when the cache is “cold.” But this creates a brutal catch-22. Long, continuous agentic tasks—the exact workloads that burn the most money—rely entirely on a hot cache. By only switching when the cache is cold, Tokenless locks itself out of the very sessions where cost savings matter most.
The economics of AI aren’t about finding the smartest model for the cheapest price; they’re about keeping the memory warm.
We are so obsessed with the price-per-token of different models that we forget the architecture of the session itself. Querying multiple models at once to make a routing decision, as Tokenless does, adds even more overhead. Unless the routing accuracy is flawless, you’re paying a premium just to decide which model to use.
If you run AI agents, you need to rethink your strategy. You don’t need dynamic routing that breaks your context. You need persistent context that compounds its value over time.
Don’t optimize for the turn. Optimize for the session. In the AI arms race, the tortoise with a hot cache beats the hare with a cheap model.
FAQ
Q: Doesn't querying multiple models at once to make routing decisions cost more?
A: Yes. Tokenless queries multiple models simultaneously to decide where to route the task. Unless their routing accuracy is flawless, you're paying extra overhead just to figure out which model to use, negating the savings of switching.
Q: How does caching actually affect my AI bill?
A: Caching stores your context window so you don't have to resend it, reducing input token costs by up to 90%. In long agentic tasks where context grows, keeping the cache hot saves vastly more money than switching to a marginally cheaper model.
Q: Is dynamic model switching a dead end?
A: For long agentic workflows, yes. Switching is only useful for cold-start queries. The real money is in maintaining persistent context, not dynamically breaking it to save pennies on output tokens.