AI vendors are switching to usage-based pricing and bills are spiking. Here’s the full set of features Kilo gives you to see, manage, and shrink your AI spend.
When most AI tools charged a flat monthly fee, users never had to think about what a single request cost - but that era is officially ending. GitHub Copilot flipped to usage-based billing on June 1st, and heavy users on team plans have watched their bills jump three to ten times overnight. Anthropic moved enterprise customers onto per-token pricing shortly before that. The number that used to sit hidden inside a subscription is now itemized on your invoice, and most teams have no idea how to read it, let alone bring it down.
Kilo never hid that number. We bet on per-token, pay-as-you-go pricing on day one, and 21 trillion tokens later, the tooling to make that spend legible and controllable is baked into how the platform works. So while other tools scramble to retrofit usage-based billing onto products that were never designed for it, cost control has been part of Kilo’s core from the start. It comes down to a handful of levers.
Lever 1: Model Choice
Start here, because this is the lever that likely moves your bill the most. The gap between a frontier model and a less expensive, faster one runs close to an order of magnitude. Anthropic’s Opus 4.8 costs $5 per million input tokens and $25 per million output. MiniMax M3, which holds its own on coding and agentic work, runs roughly $0.60 and $2.40. For the same task you could spend eight to ten times as much, depending on which model you reach for.
However, most of what an agent does all day - reading files, scaffolding, generating tests, grinding through boilerplate - doesn’t need frontier-level reasoning at all. Run one meaty feature task on Opus and you might spend $15. Run the same task on M3 and it’s closer to $1.70. Multiply that across every task in a sprint and every dev on the team, and the number that decides your bill isn’t your prompt length or your seat count, it’s whether you matched the model to the job.
Kilo gives you over 500 models and lets you switch between them at any point, even mid-conversation, so you can default to the cheaper model and escalate to a frontier one only when a task deserves it. Switching mid-session keeps your context intact, so moving up a tier costs nothing in setup. You stop paying Opus prices for work that M3 finishes just as well.
Lever 2: Observability
You can’t cut a cost you can’t see, and a single total at the end of the month tells you nothing about where to start. The Kilo usage analytics dashboard breaks spend down by model, by project, and by user. If one project is quietly eating the majority of the budget, or a teammate is running Opus on everything when half their work would be fine on something cheaper, it shows up here. That’s the difference between guessing at your AI bill and managing it: you find the runaway cost, trace it to a cause, and fix it, instead of staring at one big scary line.
Lever 3: Governing a Team
You can eyeball your spend solo, but on a team it sprawls. Most orgs run a handful of AI tools at once: a coding extension, a code-review bot, a personal agent, each with its own seat, its own invoice, and its own pile of allowance nobody’s using. Kilo replaces that with pooled credits and a single invoice. The credit balance is shared, so your spend compiles to one bill instead of five.
Admins can set model access limits too, enabling only the models that fit the team’s compliance and budget rules using a range of attributes and filters, so nobody runs up a tab on a model they shouldn’t be touching in the first place.
Kilo’s AI ROI dashboard sits on top of all of it, tracking adoption and output so “we spend this much on AI” becomes a conversation about what you got back. It shows where AI is woven deep into the work and where it’s barely touched, and helps your team make the most of the tokens it spends.
Lever 4: Optimize Time & Context
Tokens aren’t your only cost. For many teams, the priciest line on the sheet is developer hours - so compressing wall-clock time counts as cost optimization too. Agent Manager in Kilo’s VS Code extension lets you run several agents on one project at once without them colliding, because each works in its own dedicated git worktree. One agent implements a feature, another writes its tests, and a third clears a bug from last sprint - and none of them overwrites another’s work.
Every default agent mode (Code, Debug, Plan, Review) is also natively orchestrative, breaking a large task into subtasks and handing them to subagents on its own, so you describe the outcome instead of the choreography.
Wasted tokens belong in this column too. Without a map of your repo, an agent finds context the expensive way: opening files, reading them, and discarding the ones that don’t matter, with every read billed. Codebase indexing gives your agents semantic search across the repository, so they pull the handful of files that count instead of spelunking through the rest. They reach the right context faster, and you stop paying to re-explain and parse through your codebase every conversation.
Built for what comes next
Per-token pricing isn’t going anywhere. The teams who come out ahead match the model to the task, see where the spend goes, govern it across the org, and optimize time and context. Kilo gives you all four levers in one place, built into the platform instead of bolted on as an afterthought.
Try it at kilo.ai.