This is Part 3 of The Real Cost of Agentic AI series.
Summary
Even as Microsoft rolls out pricing and calculators for Copilot and AI services, estimating the actual costs of these “agentic AI” features remains a major challenge. This article explores why predicting spend for AI-powered services (like Copilot) is so difficult – touching on unclear usage metrics, evolving cost models, and the gap between Microsoft’s calculators and real-world usage – and what organizations can do about it.
What’s happening
Microsoft has published pricing details and even some tooling to help customers gauge Copilot costs – for example, the Copilot Studio licensing guide explains message pack rates and pay-as-you-go pricing ($0.01 per message, etc.). In some cases, Microsoft provides cost calculators or examples (we’ve seen hypothetical scenarios like “200 generative answers + 200 data queries = €64/day” to illustrate Copilot Chat costs.
Despite these efforts, stakeholders are still struggling to confidently estimate costs for AI features. The core issue is that usage is unpredictable: How often will users actually invoke Copilot? How complex will their queries be? Will usage spike initially and then level off, or grow over time? These variables make forecasting akin to weather prediction – lots of unknowns.
Moreover, some of the metrics are new or not directly observable to customers. This is reminiscent of past experiences with Power Platform’s consumption limits: when Microsoft introduced daily API call limits in 2019, no one had the tools to measure their API usage against those limits. Admins were in the dark – you “can’t manage what you can’t measure,” as I’ve earlier written about this.
We see a similar situation now: organizations know the price per Copilot “message,” but in practice, counting messages (and distinguishing simple vs. generative vs. graph-enhanced queries) is not straightforward until after you deploy. Microsoft’s cost estimates also tend to assume certain usage patterns that may not match your reality (e.g. average user asks a handful of simple questions a day). In reality, you might have a small group of power users who hammer Copilot with dozens of complex queries, skewing the consumption.
Another facet of the conundrum is rapidly changing models: Microsoft can and does adjust licensing rules as these AI services evolve (we’ve already seen multiple revisions of Copilot licensing in a short time). A calculator from six months ago might be outdated after a product GA or a pricing tweak. All this leads to a scenario where even diligent IT teams plug numbers into spreadsheets and still end up with a best-guess budget rather than a certainty.
Microsoft’s own acknowledgment of this uncertainty is that they’ve allowed pay-as-you-go, precisely so customers can start without commitment and observe actual usage. Essentially, “try it and see” has become the implied strategy for cost estimation – which, while practical, is not comforting to those who have to sign off on budgets in advance.
Why it matters
In business, being unable to estimate costs is a big red flag. If you’re a decision-maker (CIO, CFO, etc.), the first question about something like Microsoft Copilot is likely “What’s this going to cost us (and is it worth it)?” If the IT team can’t provide a solid answer, projects can stall or be denied.
This is why the cost estimation conundrum matters: it can directly affect adoption of AI capabilities. Many organizations are excited about generative AI but face internal resistance due to budget uncertainty – nobody wants a blank check situation. Furthermore, those who do go ahead and deploy Copilot widely risk bill shock if their usage assumptions were off. We’ve seen cases in the cloud world where a new service racks up unexpectedly high charges; with Copilot’s per-message billing, this is a real possibility if usage isn’t closely watched.
Another important angle is ROI measurement. Without a predictable cost, how do you measure the return on investment of Copilot or an AI agent? You might save hours of work with automation (benefit), but if the consumption bill for those automations is higher than expected (cost), the value proposition gets cloudy. This could lead to premature abandonment of a genuinely useful tool simply because the costs weren’t well understood or tracked.
Additionally, the inability to estimate costs hampers planning and governance. For example, how do you set departmental chargebacks for AI usage if you can’t predict how much each department will consume? How do you enforce a budget limit? It pushes organizations to implement their own monitoring solutions – maybe setting up alerts when consumption hits X% of a threshold – adding overhead to what was supposed to be a turnkey AI solution.
From a Microsoft partner perspective, it’s also challenging: how do we advise clients on licensing if the “it depends” has too many variables? (This is precisely why many turn to independent advisors – to interpret Microsoft’s guidance and build more realistic models.) Ultimately, mistrust can creep in. If Microsoft’s cost calculators or sales estimates turn out overly optimistic, customers may grow skeptical of adopting new features (the “once bitten, twice shy” effect). That would be a shame, because it might slow down beneficial innovation.
Therefore, solving (or at least mitigating) this estimation problem matters for building and maintaining trust between Microsoft and its customers, and for ensuring AI projects get green-lit with confidence.
My perspective
Microsoft often sells us on vision and assumes the details will sort themselves out – but when it comes to cost, that’s a dangerous approach. In my experience, the cost estimation gap is real and must be tackled head-on.
My take is that no one – not even Microsoft – can accurately predict what your Copilot usage will look like, so you have to take charge of the process. That means planning deployments in phases: start with a pilot group, closely monitor usage (Microsoft 365 admin center now offers some Copilot usage analytics – use them!), and extrapolate from there. I’ve advised clients to set an internal “budget cap” for Copilot during trial periods – e.g. “We’ll allow up to $X of consumption this quarter” – to force a re-evaluation before going further. This helps avoid open-ended commitments.
I also tend to add a safety buffer (yes, I’ve seen those Azure cost estimations that were way off) – if Microsoft says it’ll cost $1, assume it could be $2 and ensure that’s still acceptable. This is not cynicism; it’s realism grounded in having seen cost overruns in other “pay-for-what-you-use” scenarios.
Moreover, I think Microsoft could do more: for example, real-time cost dashboards, more granular controls to limit usage, or even AI-driven cost anomaly detection. Currently, it feels like we’re back in 2019 when API limits came with lots of FUD (fear, uncertainty, doubt) – the people who cared the most were the ones left worrying. I won’t be surprised if we soon see third-party tools or scripts to track Copilot message consumption because the native tools lag.
On a contrarian note, part of me wonders if Microsoft benefits from this opacity – confusing pricing can lead to over-provisioning or hesitance that funnels customers back to more expensive flat licenses. Time will tell. For now, my advice is to approach AI licensing like a cloud cost engineer: get data, monitor continuously, and iterate your estimates. And don’t go it alone if you’re unsure – it’s perfectly fine to bring in licensing specialists to sanity-check your plans (I frequently do cost modeling exercises with clients to help them feel confident).
In short, plan for the worst, hope for the best. If the bill comes in lower than expected, fantastic – if higher, at least you won’t be caught off guard. In this new world of AI, financial prudence is as important as technical prowess.
