Somewhere in a shared Teams channel, a Fabric capacity admin is looking at the Capacity Metrics app and noticing Spark consumption is down 15% overnight. Same notebooks. Same schedules. Same engineers shipping code with the same amount of caffeine.
A quick thread later, the answer is clear: nothing is wrong. Microsoft introduced new billing operations, and AI usage is now visible in its own category.
That’s not a cost increase. That’s better instrumentation.
What actually changed
On February 13, 2026, Microsoft announced two new billing operations for Fabric: AI Functions and AI Services.
Previously, AI-related usage in notebooks was grouped under Spark operations. Calls made through fabric.functions, Azure OpenAI REST API, the Python SDK, and SynapseML were all reported in Spark. Text Analytics and Azure AI Translator calls from notebooks were also reflected there.
Now those costs are separated:
- AI Functions covers Fabric AI function calls and Azure OpenAI Service usage in notebooks and Dataflows Gen2.
- AI Services covers Text Analytics and Azure AI Translator usage from notebooks.
Both are billed under the Copilot and AI Capacity Usage CU meter.
Important: consumption rates did not change. You pay the same for the same work. What changed is visibility.
Why this reporting update is a win for operators
If you’ve ever tried to explain Spark trends that include hidden AI consumption, this update helps immediately.
Picture an F64 capacity. You historically allocated 70% of CU budget to Spark because that’s what Capacity Metrics showed. But Spark previously included AI consumption, so the category was doing two jobs at once.
Now Spark and AI can each tell their own story. That’s useful for:
- more accurate workload attribution
- cleaner alerting by operation type
- better planning conversations with finance and platform teams
In other words: same total spend, sharper signal.
The migration checklist
There’s nothing to deploy and no code changes required. The opportunity is operational: update your monitoring and planning so you can benefit from the new detail right away.
1. Audit your AI function usage
Before the new operations appear in your Metrics app, find AI calls in your codebase. Search notebooks for:
fabric.functionscalls- Azure OpenAI REST API calls (look for
/openai/deployments/) openaiPython SDK usage within Fabric notebooks- SynapseML OpenAI transformers
- Text Analytics API calls
- Azure AI Translator calls
If there are no hits, this billing split likely won’t affect your current workloads. If there are many hits (common in mature notebook estates), estimate volume now so your post-change analysis is faster.
2. Baseline your current Spark consumption
Export the last 30 days of Capacity Metrics data for Spark operations and save it.
This is your before-state. After rollout, validate that total consumption (Spark + new AI operations) aligns with historical Spark totals. If it aligns, you’ve confirmed a reporting change. If not, you have a clear starting point for investigation.
3. Adjust your alerting thresholds
If you monitor Spark CU consumption via Capacity Metrics, Azure Monitor, or custom API polling, update thresholds after the split.
Recommended approach:
- take your current Spark threshold
- subtract estimated AI consumption from step 1
- set that as the revised Spark threshold
- add a separate alert for the Copilot and AI meter
If AI estimates are still rough, start with a conservative threshold and tune after a few weeks of separated data.
4. Update your capacity planning models
Add a dedicated row for AI consumption in any spreadsheet, Power BI report, or planning document that allocates CU budget by operation type.
The Copilot and AI Capacity Usage CU meter already existed for Copilot scenarios, but this may be the first time many Spark-first teams see meaningful workload usage there. Adding it now makes future reviews easier.
5. Set up a validation window
Choose a date after March 17 (when the new operations start appearing) and compare pre/post totals:
- pre-change: Spark total
- post-change: Spark + AI Functions + AI Services
Expect close alignment (allowing for normal workload variation and rounding). If variance is more than a few percent, open a support ticket. Microsoft described this as a reporting-only change with no rate modifications.
6. Share a quick team note before questions start
One short update prevents a lot of confusion:
“Microsoft is separating AI consumption from Spark billing into dedicated operations. Total cost is unchanged. Spark will appear lower, and Copilot and AI will appear higher. This improves visibility and tracking.”
That gives engineers context and helps finance teams interpret new categories correctly on day one.
Post-rollout checks that keep things clean
Consumption variance check. If post-change totals (Spark + AI Functions + AI Services) differ significantly from pre-change Spark trends, compare equivalent workload windows and rule out schedule, code, or capacity changes.
Expected operation visibility. If you confirmed AI usage in step 1 but AI Functions shows zero, check regional rollout timing from the Fabric blog before escalating.
Why separated AI spend is valuable
This platform-side categorization update gives teams a better lens on where capacity is being used.
Once AI usage is measurable independently, you can answer higher-quality questions:
- Which AI workflows are creating the most value per CU?
- Which calls are production-critical versus experimental leftovers?
- Where should you optimize first for performance and cost?
That is exactly the kind of visibility mature platform teams want.
What this signals about Fabric billing
As Fabric workloads evolve, billing categories will continue to become more descriptive. That’s a good thing. Better category design means better operational decisions.
The admin in that Teams thread got clarity quickly: Spark wasn’t shrinking, observability was improving. Once the team updated dashboards and alerts, they had a more useful capacity model than they had the week before.
That’s the real upgrade here.
This post was written with help from anthropic/claude-opus-4-6
Christopher Finlan 