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MTS · 3 min · Oct 23, 2026
AI cost controls can tie token budgets to business workflows
Kong maps AI costs across agents, APIs and repositories, then can enforce budgets by blocking traffic. That granularity helps companies connect spending to outcomes and report costs to boards.
Aghi Marietti is CEO and co-founder of Kong.

Theo Jaffee, Host · Co-founder & host, MTS
One of the first members of a16z's new media team; earlier hosted the Emergent Ventures-backed Theo Jaffee Podcast.

Sophia Dew, Host · Host, MTS
About this segment
Kong maps AI costs across agents, APIs and repositories, then can enforce budgets by blocking traffic. That granularity helps companies connect spending to outcomes and report costs to boards.
From “AI Gateway Token Traffic Growing 140X Year Over Year | Aghi Marietti”, Oct 23, 2026. Watch the full episode
Thanks. We'll look at and fix what's off.
“you can then block when the budget gets hit all the way down to individual”
Takeaways
- A cost dashboard alone does not show how spending is used.
- Costs can be traced to workflows, repositories and pull requests.
- Budgets can trigger blocks when spending limits are hit.
In this episode
Full episode- 0:55AI gateways give enterprises a checkpoint to stop agent attacks6:58
- 8:06Enterprise AI is spreading across models as token use surges7:30
15:18AI cost controls can tie token budgets to business workflows2:44
- One more moment in the full episode
Transcript
Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.
- 15:29
for a lot of enterprises right now. I mean, as you mentioned, even though the costs of tokens are going down, which can be a good thing, the overall costs that enterprises are still spending and their token spend is just going up. So despite, it's like a Jevons paradox that you're starting to see with token spend. And so what are you hearing about some of the concerns around cost management? So we preview our AI costs or token cost management products at Summit are going GA in a few weeks.
- 16:00
That was the most, I think a top two ask or excitement was around AI cost management was that one. And the reason is, you remember like in April, May, we went through this token maxing and everybody ran out of budget by the time it was May for the fiscal year. That was a big thing about Uber running out of budgets. And we are never, you know, we weren't that different on our own. So the beauty of how we approach the problem is not just a dashboard because everybody can show you AI cost dashboards.
- 16:30
But because we intercept traffic around all these entities between MCP, agents, APIs, we are able to have metadata that nobody has on how actually costs get mapped, what are the margins, but how they get used all the way down to a repo. And then you can apply ROI cards. You can see the return on investment of this type of workflow, what outcome it created. So there is this level of dashboard, which is one piece, which is all the AI cost management.
- 16:57
but also the granularity of connecting and mapping the different costs across the organization between the token part and the execution part. And then you can block. And that's unique to call. Because if you add a budget to, I don't know, R&D in France to use this amount of tokens, you can then block when the budget gets hit all the way down to individual that maybe was consuming the most token and they weren't very productive, but all the way down to the repo or to the pull request.
- 17:29
It can go super granular. And so that level of stack was very excited. And the beauty of this, it was not just a platform. Even CEOs of public companies were in need of showing this to their board because they were like, okay, how is all this cost coming together? And so that's something our enterprise customers are very excited about. We'll continue monitoring right after this message from our sponsors.