How observability and FinOps discipline are essential for value
An AI pilot can look inexpensive right up until it succeeds. Usage grows, inference traffic becomes unpredictable, data moves through more services, telemetry multiplies, and engineering teams spend more time figuring out why costs changed.
That is why cloud total cost of ownership is becoming less of a procurement calculation and more of an operational question: What is the system doing, what value is that activity producing, and what is it costing while it runs?
AI is making cloud economics more dynamic
The shift is already visible in FinOps. The FinOps Foundation’s 2026 survey found that 98% of respondents now manage AI spend, up from 31% two years earlier, while FinOps for AI has become the discipline’s top forward-looking priority. Practitioners cite visibility, allocation and determining AI value as persistent challenges.
That matters because AI workloads can make familiar cloud-cost variables harder to forecast. Demand can swing with user behavior, model usage and experimentation, while the supporting architecture may include compute, storage, networking, databases, APIs, monitoring and security services.
A monthly invoice can tell an organization what it spent. It cannot, by itself, explain which application behavior, deployment decision, service dependency or business transaction created that spend.
The lowest cloud bill is not necessarily the lowest TCO
Microsoft makes a useful distinction in its Azure Well-Architected Framework: a cost-optimized workload is not simply the cheapest workload. Long-term optimization requires continuous monitoring, repeatable processes and explicit tradeoffs among cost, security, resilience, scalability and operability.
That wider definition matters for AI. Cutting compute aggressively may reduce the infrastructure bill while increasing latency. Sampling too much operational data could lower monitoring costs but make production incidents harder to diagnose. Underprovisioning a customer-facing AI service can save money until demand spikes and reliability suffers.
The more useful metric is therefore not cost alone, but cost relative to workload behavior and business value.
Depending on the application, that might mean tracking infrastructure cost per successful transaction, cost per customer interaction, cost per inference, or the operational cost required to maintain a particular service-level objective.
Cloud TCO needs operational context
To manage that equation, organizations need to connect financial data with operational signals. CPU utilization or a cloud charge becomes more meaningful when teams can see which service generated it, what changed immediately beforehand, whether latency or error rates moved at the same time, and which users or business processes were affected.
Microsoft’s current monitoring guidance describes observability as an architectural capability rather than an afterthought. It recommends correlating telemetry across infrastructure and applications, while also explicitly modeling telemetry ingestion, retention and storage costs.
That creates a practical feedback loop:
- Measure workload behavior, including infrastructure, application, dependency and user-experience signals.
- Connect behavior to ownership and business context, using consistent tags, service names and workload boundaries.
- Find expensive patterns, such as idle capacity, abnormal traffic, noisy services or unexpectedly high telemetry volume.
- Change the architecture or operating policy, then measure whether cost, performance and reliability actually improve.
This is where observability and FinOps increasingly overlap.
Even observability has to earn its keep
There is an uncomfortable irony in cloud optimization: the systems used to understand a complex environment can themselves become a source of cost.
Microservices, containers and AI applications can produce enormous numbers of metrics, logs and traces. Microsoft specifically recommends configurable logging, appropriate sampling and aggregation, retention policies and tiered storage so monitoring data does not grow without limits.
Splunk addresses the same problem with Metrics Pipeline Management inside of Splunk Observability Cloud. Teams can aggregate high-cardinality metrics, keep important data available in real time, archive lower-value metrics or drop data that provides insufficient monitoring value. Splunk states that its archived metrics tier is billed at one-tenth the cost of regular real-time metrics.
That is an important TCO principle: collecting everything forever is not the same as having good visibility. The objective is to preserve the signals needed to make decisions while reducing data that adds expense without equivalent operational value.
Where Splunk on Microsoft Azure fits
Splunk Cloud Platform became generally available natively on Microsoft Azure in November 2024, alongside Splunk Enterprise Security and Splunk IT Service Intelligence. Organizations purchasing eligible Splunk offerings through Azure Marketplace can also apply that expenditure toward a Microsoft Azure Consumption Commitment, according to Splunk.
For Azure operations, Splunk Observability Cloud integrates with Microsoft Azure monitoring and can provide visibility across infrastructure and applications, while Splunk’s Microsoft integrations can ingest operational data from Azure and other Microsoft services.
The value for TCO is not that an observability platform replaces Azure Cost Management. It is that financial decisions can be informed by deeper runtime evidence: utilization, service dependencies, latency, errors, deployment changes and telemetry patterns.
Instead of asking only, “Which Azure service cost more this month?” teams can investigate the operational reason behind the change.
AI can reduce the human side of TCO too
Operational labor belongs in the TCO conversation. Every hour engineers spend manually correlating dashboards, searching logs or reconstructing an incident is part of the cost of operating the environment.
Splunk’s AI Assistant in Observability Cloud uses natural-language interaction and, in its newer agentic implementation, purpose-built investigation workflows that can gather context across services, infrastructure, logs, traces and metrics. The goal is to reduce manual investigation effort and accelerate troubleshooting rather than simply add another chatbot to the toolchain.
Specsavers offers a useful example of the broader economics. The company uses Splunk Observability Cloud to instrument its Azure-based patient management platform with metrics, logs and traces; across its larger Splunk deployment, it reports 10-times-faster mean time to resolution and 25,000 hours saved each month through automation.
Those outcomes are not the same as a lower Azure infrastructure bill. They illustrate why TCO must include the cost of operating complex technology, not just consuming it.
TCO is becoming a continuous feedback loop
AI is making cloud spending faster-moving just as enterprises are demanding clearer evidence of AI value. Treating TCO as a quarterly exercise in invoice review will increasingly leave technology leaders explaining costs after architectural decisions have already been made.
The better approach is to make cost observable alongside performance, reliability and business outcomes.
For organizations building AI and cloud workloads on Azure, that means designing visibility into the architecture from the start, controlling the cost of the telemetry itself and giving engineering and FinOps teams enough shared context to decide which expenses create value and which are simply noise.
The important question is no longer just “How much does our cloud cost?”
It is “What are we getting for every dollar while the system is running?”
