Valuemaxxing: Observability That Proves Its Value

Ali Ghodsi at Databricks has a useful way of describing where enterprise AI is heading. The early game was tokenmaxxing, where more consumption was treated as more progress. The game that matters now is valuemaxxing, a move from maximizing how much you spend to maximizing the value that spending produces. The phrase has stuck because it names a journey that most organizations are somewhere in the middle of, whether or not they have noticed.

That journey tends to follow the same arc. At the start, everyone goes all in and reaches for the biggest, smartest model for everything, because capability is thrilling and the bill has not landed yet. Then the bill lands, and the instinct flips. Teams start doing less, rationing where they use AI, holding back on experiments that might have paid off, because the cost of the largest model on every task turns out to be hard to justify. From there a smarter pattern emerges. People begin matching smaller, dedicated models to the ad-hoc tasks that never needed a frontier model in the first place, keeping the heavyweight in reserve for the work that genuinely calls for it. And at the far end of the arc, many will deploy their own models inside their own infrastructure, arriving back where they started, able to do everything again, only now at a normal and predictable cost.

The interesting part is that the destination is not really about model size at all. It is about knowing which work is worth which spend, and being able to act on that knowledge. Valuemaxxing is the discipline of seeing what your effort returns and steering accordingly. That discipline is exactly what observability has always needed, and rarely had.

Because the same story has played out in observability for years, just with gigabytes instead of tokens. Teams go all in and collect everything. Then the bill lands and they start sampling, dropping context, and shortening retention, doing less to control the cost. What they have lacked is the third and fourth act, the ability to see which telemetry actually earns its keep and to run the whole thing efficiently without giving anything up. Valuemaxxing is a good name for that missing discipline, and it is what Tsuga was built to make possible.

It starts with where your data lives. Because Tsuga runs inside your own cloud, we collect the output of your AI work in place, alongside everything else your systems produce, rather than pulling it out to a separate platform. From there, connectors into your FinOps tooling let you line up activity against cost and read them together. You can see not only which teams are getting the most value from the tokens they use, but which teams paired with which models are having the biggest impact on real outcomes. That picture is what lets you guide deployment deliberately, the right model against the right task, effort matched to difficulty, and move through that arc on purpose rather than by trial and expensive error.

None of it requires sending your most sensitive material somewhere else to be measured. The prompts, the completions, the code, and the production context that reveal how your AI is really performing are exactly the things you cannot afford to hand to a third party. In the usual model, understanding your own usage means exporting it to an outside service and trusting that arrangement with your most confidential data. That trade should never have been the price of visibility. With Tsuga the analysis happens inside your infrastructure, under your keys, within your boundary.

What ties it together is people, not just architecture. Our forward deployed engineers work alongside your teams with a single focus, which is valuemaxxing across the board. They help you read the signal in your own data, retire the spend that is not returning anything, match models and telemetry to the work that actually needs them, and keep moving toward doing everything you want at a cost that makes sense. It is the same principle applied to your AI and to your observability at once, because in the end they are the same problem: know what your spending returns, and own that view completely.

The organizations that will do well in this next era are not the ones spending the most. They are the ones who can see what their spending returns, and who never have to give up their data to find out. To move toward valuemaxxing, you have to observe everything your AI and your systems do and share none of it outside your walls. Those two things have always pulled against each other, because visibility meant handing your data to someone else. Tsuga removes that tradeoff. See everything, share nothing, and let that complete and private view guide every model you deploy and every task you point it at. That is where valuemaxxing begins, and it begins inside your cloud. Talk to us about turning your spend into proven value, without any of your data leaving your infrastructure.