u/Artistic_Lock_6483

I was formally credentialed as an architect for IBM storage flash arrays (along with several other platforms). This weekend, I realized I could take those skills and (along with some of the recent research I’ve been doing) build an agent that could fulfill all of my normal tasks (provisioning, additional storage, resizing, snaps, etc.) so I did. I made it so that our internal team can now make requests via Slack and (depending on the defined guardrails) either receive those requests provision to them or get a reply with what they need in order to receive that. It even applies the business standards (new workload volumes on existing host added to replication groups, etc.). Obviously anything disruptive requires human approval from elevated permissions. Is anybody else doing this right now?

I was formally credentialed as an architect for pure storage flash arrays (along with several other platforms). This weekend, I realized I could take those skills and (along with some of the recent research I’ve been doing) build an agent that could fulfill all of my normal tasks (provisioning, additional storage, resizing, snaps, etc.) so I did. I made it so that our internal team can now make requests via Slack and (depending on the defined guardrails) either receive those requests provision to them or get a reply with what they need in order to receive that. Obviously anything disruptive requires human approval from elevated permissions. Is anybody else doing this right now?

At the FinOps X keynote this week, SAP's Frederik Pohl and Maida Nazifi showed how they run FinOps for AI at global scale: an AI cost control plane managed by cost per OUTCOME — "because GPUs and LLMs don't behave quite like VMs."

It was the best moment of the keynote, and honestly, the most needed one. The FinOps Foundation recently declared that FinOps now covers ALL technology spend — yet before defining data center unit economics or naming authoritative sources for those metrics, it has pivoted again, to token economics. An arena J.R. Storment's own keynote called a "Wild West." Scope is expanding faster than definitions. SAP's segment was the part you could actually build on.

I was curious what an A.I. benchmark, driven by SAP's cost-per-outcome idea would look like (rather than just quantifying problem solving, long running context, or reading comprehension)… so I ran a series of tests towards a working benchmark:

14 models: closed frontier and open weights, 420 graded document-extraction runs, deterministic grading, no LLM judges, run overnight unattended. One metric: Cost Per Successful Outcome = total dollars spent ÷ answers that actually passed. Failures stay in the bill, because that's how your invoice works.

SAP is right. They don't behave like VMs. At all:

1. Cost per success ranged $0.0002 to $0.59 on IDENTICAL work — 3.5 orders of magnitude. The token price sheet shows only ~70x. Rate cards understate the real economics by 35x.

2. An open-weight model won outright: best pass rate (70%) and lowest cost per success, confidence intervals clear of every frontier model.

3. No model at any price beat 70% on this task set. Every dollar above the cheapest model at the ceiling bought nothing.

4. The priciest model scored 7 points BELOW the winner. Price and quality were uncorrelated across all 14.

Practical payoff: routing this workload to the value leader instead of a frontier model cuts cost per successful document ~99.9% with zero quality loss — a governable decision, IF someone in the room can read cost-per-outcome data.

That someone is FinOps. You can't make a defensible AI value statement to the business from a price sheet and a leaderboard — the real economics live in the gap between them, and reading that gap is the new core skill. One keynote slide became a working benchmark in a night; the measurement discipline is buildable NOW, by practitioners, without waiting for a standards body to finish the vocabulary.

Full analysis, ranking table, confidence intervals, and the honest caveats https://www.realtimecost.com/benchmark

Coming from an infrastructure background, I was accustomed to real time alerting on hardware events. Since moving into the cloud, I’ve noticed the industry accepts a 24-72 hour delay in billing data (that assumes you’re being more proactive than just looking at the monthly bill). I was using Cloudability at the time and even it was behind (because the provider data themselves is behind). Buy I was able to build a real time alerting software to send me notices as soon as a resource usage event was occurring (with the expected price impact). I’m considering open-sourcing the main functionality (monitoring/alerting) on GitHub and having a purchasable upgrade for additional features (multiple users, support, anomaly detection, tagging analysis, AI/LLM token forecasting, MCP for BYOLLM, etc). Any thoughts on this approach?

Coming from an infrastructure background, I was accustomed to real time alerting on hardware events. Since moving into the cloud, I’ve noticed the industry accepts a 24-72 hour delay in billing data (in other words- if you have a price spike it started at least 24 hours ago). So I was able to build a real time alerting software to send me notices as soon as a resource usage event was occurring (with the expected price impact). I’m considering open-sourcing the main functionality (monitoring/alerting) on GitHub and having a purchasable upgrade for additional features (multiple users, support, anomaly detection, tagging analysis, AI/LLM token forecasting, MCP for BYOLLM, etc). Any thoughts on this approach?

Coming from an infrastructure background, I was accustomed to real time alerting on hardware events. Since moving into the cloud, I’ve noticed the industry accepts a 24-72 hour delay in billing data (in other words- if you have a price spike it started at least 24 hours ago). So I was able to build a real time alerting software to send me notices as soon as a resource usage event was occurring (with the expected price impact). I’m considering open-sourcing the main functionality (monitoring/alerting) on GitHub and having a purchasable upgrade for additional features (multiple users, support, anomaly detection, tagging analysis, AI/LLM token forecasting, MCP for BYOLLM, etc). Any thoughts on this approach?

You ever notice how all of these horror stories of clouds spend typically occur over a weekend? It’s because billing data lags behind usage (24-72 hrs depending on your Cloud provider). It’s because people are actually paying attention first thing Monday morning and whatever state things were in Friday (when attentiveness is down) has now hit the dashboard (that assumes you’re looking at the right dashboard and not just waiting for the monthly bill). If your daily spend is $10k, a 72-hour billing delay (standard for AWS/Azure Rating Latency) results in $30,000 of unrecoverable spend before an alert even fires.

I was getting asked by our CFO about the bill and retroactively looking at reports (Cloudability and native Azure/AWS) but the approach of playing investigator was annoying. Coming from an infrastructure background I expected to be alerted when things happened not find out after the fact only (didn’t monitoring software solve this like 10 years ago?!?!). I built my own solution for our use case… But I’m wondering why no one else is bothered by this.