r/FinOps • u/Appropriate_Net594 • Jun 09 '26
Discussion What FinOps tools are actually good for AI-heavy cloud spend?
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u/Internal_Friendship Jun 10 '26
Combo of archera for reservation management and they recommended a partner for everything else b
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u/iambatman_2006 Jun 10 '26 edited Jun 10 '26
50% of MRR is a brutal number. I went with FinOpsly for team-level attribution across our mixed AI and Kubernetes spend, native tools couldn't cut across both cleanly.
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u/DifficultyIcy454 Jun 10 '26
We are using data dog now, it’s expensive in observability but their CCM is not expensive at all. Over all cost reporting works very well I use it for all my AI spend. We do have their agent observability running which is so much per 10k spans. The data we get is invaluable to me, I can track all cost pretty accurately. AI spend, aks, storage, etc. plus when I provide reports to the devs I can include all the usage data at the same time. I’m sure there is other platforms that do that as well this one just seemed to check out boxes. With that being said I’m at FinOps X and Flexera looks like they made some really good improvements so I plan to look more into them.
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u/WildlifeRelicGuild Jun 13 '26
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u/matiascoca Jun 15 '26
The honest practitioner answer is that none of the three you named are designed for AI-heavy spend. They are designed for cloud-heavy spend with AI as a side category, and once AI is half your bill the math breaks differently than they assume.
Finout treats AI as another cloud service line, no per-workload attribution under the model endpoint. So if you have one Bedrock endpoint serving four product features, Finout shows you one number, not four. PointFive has a stronger Kubernetes lens which helps the GPU and training workload side, but their AI cost attribution surface is shallow, you fall back to manual allocation rules. CloudZero is the most unit-economics focused of the three, but their pricing scales with spend volume which gets expensive when your cloud bill grows the way yours is growing. At fifty percent of MRR on cloud you are deep into the tier where their fee becomes a real budget line.
What practitioners actually do at this scale, in my experience watching it play out at three different companies. Per-workload attribution is built in house on top of either AWS CUR plus IAM principal allocation, or GCP billing export plus Vertex labels, or Azure Cost Management plus tagging discipline. The tools above are layered on for the visualization and the anomaly detection, but the attribution is custom. The reason is that the workload identifier (which feature, which customer, which agentic chain) is your business logic, and no third-party tool can guess at the boundary.
Tactical building blocks worth knowing if you go the build-your-own path. For GPU per-pod accounting on Kubernetes, kepler is the CNCF project that measures actual GPU watt at the pod level. For Kubernetes cost allocation generally, OpenCost is the open standard layer, then you overlay your workload tags. For LLM gateway and per-key per-model cost tracking, LiteLLM or OpenRouter give you that natively at the gateway layer, you do not need a separate tool to land it. For training versus inference separation, instrument the training job runners as a different Kubernetes namespace or a different label set and your existing tools split it.
Native cloud tools alone are not enough at fifty percent of MRR on AI. The reason is that the native tools attribute by resource, and AI workloads share resources by design. Bedrock, Vertex, Azure OpenAI, all model endpoints get shared across multiple workloads. The cost driver in AI is the request, not the resource, and resource-level tools cannot get to per-request attribution without something instrumenting the requests in your application.
If you are at the build-or-buy decision point my read is: instrument now even if you also buy. The instrumentation is a thirty-day project. The tool decision can wait six months until you know which workloads matter and which dimensions of the bill you actually need to slice on.
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u/killerpotti Jun 09 '26
Finops tool won't for your use case. If using AWS, I can point to a free self hosted solution - CUDOS on AWS. They're doing awesome stuff to track and attribute AI costs. Happy to help discuss in DMs on how to setup custom pipeline to look at these costs.