Engineering teams across industries are building the future with AI. Whether it is training large models, running real-time inference, or experimenting with new architectures, cloud infrastructure is the engine that makes it possible. But as AI adoption accelerates, a new challenge is surfacing: AI cost management.
The reality is clear: your work may be groundbreaking, but without visibility and cost controls, it is also expensive and often misunderstood by the business. We have seen it repeatedly: engineering teams spinning up GPUs without budget awareness, finance teams questioning massive bills after the fact, and innovation stalling under the weight of reactive cost controls.
Maybe you have experienced it yourself. Another surprise cloud bill arrives, AI workloads blow past forecasts, and you begin to wonder if controlling these costs is even possible.
The truth is that it is possible. With the right tools and a disciplined FinOps approach, AI cost management does not have to be unpredictable or chaotic.
In this blog, we’ll debunk the five most common myths about AI cost management and show how the right platform can help you bring predictability, visibility, and control to AI spending.
Why AI costs are not as unpredictable as you think
The belief that AI costs are impossible to control has deep roots. Rapid innovation, variable workloads, and the lack of native tools for GPU cost optimization have all contributed to this perception. Most of all, the pace of change in cloud computing and the complexity of AI workloads have left many leaders feeling that AI cost management is out of reach.
Let’s clarify some key terms.
- AI spend refers to the total cloud spending attributed to artificial intelligence workloads.
- Cloud AI budget is the planned allocation of resources and funding for AI initiatives running in the cloud.
- GPU cost optimization means monitoring, analyzing, and controlling the costs associated with running workloads on GPUs in the cloud.
- Machine learning costs include all expenses related to developing, training, and deploying machine learning models, such as compute, storage, and data transfer.
Despite the challenges, more organizations are taking control. According to the State of FinOps 2025 report, the majority of FinOps practices now include AI cost oversight in their scope, reflecting the expansion of AI workloads across infrastructure and teams.
5 AI myths debunked
Let’s analyze the top 5 AI cost management myths.
Myth 1: AI costs are impossible to predict
The misconception
“AI workloads are so variable, they can’t be forecasted.”
The reality
While model training can be erratic—especially during periods of experimentation—it’s important to note that AI workloads also include inference, which typically drives the bulk of long-term spend. Unlike training, inference traffic can often be modeled with a high degree of accuracy.
The FinOps approach
- Use unit economics to measure cost per token, per prediction, or per API call.
- Model inference against product usage to forecast demand.
- Separate experimental (R&D) training costs from production inference workloads.
Takeaway
AI costs are not inherently unpredictable. With clear boundaries and metrics, they become manageable and forecastable, like any other cloud workload.
Myth 2: Managing GPU hours means managing AI costs
The misconception
“If we optimize GPU usage, we’ve optimized AI spend.”
The reality
GPUs may be the most visible line item, but they’re only part of the picture. AI cost management requires accounting for storage, data pipelines, network egress, orchestration layers, and idle inference endpoints.
The FinOps approach
- Track the full AI lifecycle: data prep, training, deployment, monitoring, and retraining.
- Identify silent cost drivers like overprovisioned storage or dormant endpoints.
- Incorporate AI workloads into your cloud cost allocation strategy.
Takeaway
True optimization comes from a holistic, lifecycle-driven FinOps strategy.
Myth 3: Cloud providers always offer the best AI pricing
The misconception
“Using AWS, Azure, or GCP means we’re already getting the best price.”
The reality
By default, on-demand pricing is almost always the most expensive path. Discounts, reservations, and spot pricing can make a significant impact—if leveraged strategically.
The FinOps approach
- Take advantage of commitment-based discounts for AI /ML workloads.
- Develop a negotiation strategy for enterprise agreements, especially for GPU availability.
- Explore multi-year capacity planning aligned with forecasted AI workloads.
Takeaway
The price you see isn’t the price you have to pay. FinOps teams that actively manage pricing models unlock meaningful savings. Pro tip: Engage your procurement and finance teams to help with negotiations.
Myth 4: Multi-cloud reduces AI costs by default
The misconception
“Running AI workloads across multiple clouds guarantees cost savings.”
The reality
While multi-cloud can offer resilience and GPU availability, it can also introduce complexity and hidden costs, especially around data egress and duplication.
The FinOps approach
- Choose workload placement strategies based on strengths (e.g., one cloud for model training, another for serving).
- Minimize egress by co-locating data and compute.
- Track total cost of ownership, not just per-hour compute pricing.
Takeaway
Multi-cloud can serve innovation, but without cost governance, it can increase spend.
Myth 5: AI is too new for cost governance
The misconception
“We’ll add cost controls later, once AI matures.”
The reality
“Shadow AI” initiatives are already driving multi-million-dollar cloud bills. Delaying governance increases risk and reduces the ability to scale responsibly.
The FinOps approach
- Tag workloads from day one for cost visibility.
- Set policy guardrails around instance types, regions, and quotas.
- Empower engineering teams with dashboards and alerts that tie usage to cost.
Takeaway
AI may be new, but cost governance is not optional. The earlier you expand the scope of your FinOps practice to include AI, the better.
How specialized AI cost management platforms deliver control and visibility
Modern AI cost management platforms bring two critical capabilities to engineering and FinOps teams:
- Predictive analytics, which uses historical usage data to forecast future spending
- Real-time anomaly detection, which surfaces cost spikes and inefficiencies as they happen
With these capabilities, engineering and technology professionals can track variable AI-related expenses by project, department, or team and support cloud AI budget goals with real-time data.
Empowering engineering leaders to align innovation with financial accountability
Managing AI and machine learning costs requires more than dashboards. It depends on real-time visibility, integrations with existing workflows, and a culture of shared accountability.
Modern FinOps platforms like Ternary support:
- Multi-cloud visibility to unify AI and cloud spend across providers
- AI cost attribution for precise allocation by project, team, or service
- Forecasting and budget accuracy with real-time, data-driven insights
- Human-tunable anomaly detection to cut noise and surface only actionable alerts
- Jira-integrated case management to streamline collaboration without disrupting workflows
With purpose-built FinOps tools, engineering leaders can maintain velocity while gaining more profound insights. Moreover, teams across Finance, IT, and Engineering can collaborate more effectively to ensure AI initiatives stay aligned with business priorities.
Conclusion
Unpredictable AI costs are no longer inevitable, and with the right tools, you can achieve clarity, prevent budget overruns, and build a culture of shared responsibility.
As FinOps and AI continue to evolve, proactive cost management will define the most innovative engineering teams. Those who act now will be positioned to lead in both technology and financial stewardship.
Get started with AI cost management.
