TL;DR
AI vs cloud cost management is one of the most pressing issues facing enterprises today. Cloud FinOps solved for scale and sprawl, but AI introduces new cost signals that break the model. AI costs are fundamentally different — they extend far beyond GPUs and LLM tokens. Every embedding call, retrieval query, agent action, orchestration step, fine-tuning run, and data pipeline execution generates a cost signal that can erode margins. This fragmented spend is pushing enterprises toward hybrid strategies, where on-prem GPUs provide predictable baseline capacity and cloud delivers elasticity. The only way to govern this complexity is to embed visibility, accountability, and governance as close to the source as possible. That’s what Mavvrik delivers.
Key takeaways:
- Cloud cost controls don’t map to AI.
- AI workloads generate dozens of hidden cost signals, far beyond GPUs and LLMs, to include tokens, embeddings, retrieval pipelines, etc.
- Hybrid strategies are increasing, with on-prem GPUs stabilizing baseline costs.
- Model pricing changes too fast for static budgets and forecast to remain accurate.
- Mavvrik embeds financial control directly at the source.
Why Cloud FinOps Worked and Its Limits for AI
In the cloud era, FinOps teams built maturity models around:
- Predictable units — compute, storage, data transfer
- Established practices — tagging, reserved instances, budgets, chargeback/showback
- Visibility at scale — dashboards unified spend across accounts and providers
This discipline enabled efficiency. But those tools and processes assumed stability. AI breaks that assumption.
The New Cost Dynamics of AI Workloads
Explosive Volatility in AI Consumption
AI workloads can scale unpredictably. A small prompt change, a model swap, or a new agent workflow can multiply GPU hours or token usage by 100x overnight. Without embedded controls, forecasting and cost governance quickly become unreliable.
Expanding Cost Units Beyond Cloud Billing
Cloud bills traditionally capture VM hours and storage. AI introduces a much broader set of cost units, including but not limited to:
- Prompt tokens (input)
- Completion tokens (output)
- Embedding vector calls
- Retrieval and VectorDB queries (Pinecone, Milvus, Weaviate, Chroma)
- API calls to external models (OpenAI, Anthropic, Gemini)
- Orchestration steps (LangChain, DSPy, Haystack)
- Data extraction and parsing (Firecrawl, LlamaParse, Docling, Scrapy)
- Agent actions and multi-step workflows
- Model routing overhead (Martian, OpenRouter)
- Embedding storage and cache refreshes
- Fine-tuning and retraining cycles
- Memory persistence (Zep, Mem0, Cognce, Letta)
- Authentication and authorization API usage (Okta, Auth0)
- Observability layers (Guardrails AI, Arize, Langfuse, Helicone)
Each introduces measurable costs that cloud-native tagging and dashboards were never designed to track. Mavvrik captures these signals directly at the source, whether GPU, token stream, or orchestration layer, providing visibility most cloud-native and legacy FinOps tools can’t.
Fragmented Infrastructure Across Cloud, On-Prem, and Data Platforms
AI workloads extend beyond traditional cloud deployments. They frequently span:
- On-prem GPU clusters
- Multi-cloud elasticity (AWS, Azure, GCP)
- SaaS APIs (OpenAI, Anthropic, Together.ai)
- Data platforms (Snowflake, Databricks, BigQuery, MongoDB, Neo4j, Firebase, Supabase)
- ETL and ELT tools (Fivetran, Datavolo, Verodat, Needle)
This fragmentation complicates cost allocation, as each platform introduces its own billing model and consumption metrics.
The Return of On-Prem GPUs for Cost Control
Renting GPUs from the cloud appears flexible but is expensive over time. Renting an NVIDIA H100 can cost 65,000 USD per year, while owning and amortizing the same hardware typically brings that to 30,000–35,000 USD. Enterprises are increasingly deploying on-prem GPU clusters to manage baseline capacity, while leveraging cloud elasticity only for demand spikes.
Volatile and Frequently Changing Model Pricing
Unlike cloud services, which typically reprice gradually, AI model pricing changes frequently.
- Providers such as OpenAI and Anthropic have adjusted per-token rates multiple times within a year.
- New models often launch at an introductory price, then reprice once adoption grows.
- The same workload can fluctuate in cost by 30 percent to 3x within a single quarter.
For enterprises, this volatility makes financial planning and forecasting extremely complex. With Mavvrik, organizations don’t just see price swings after the fact. Real-time telemetry enables proactive governance before costs spiral.
Analyst Perspectives on the AI Cost Challenge
WSJ: “AI was supposed to get cheaper. It’s more expensive than ever.” Rising token consumption is offsetting per-token price reductions (WSJ, Aug 2025).
Gartner: Generative AI spending will reach $644B in 2025, and by 2026, 75 percent of synthetic data projects are expected to fail due to cost mismanagement (Gartner).
Deloitte: Centralized dashboards that capture compute, inference, and token consumption are essential for informed investment decisions (Deloitte).
McKinsey: Embedding cost principles directly into engineering workflows (“FinOps as code”) could unlock nearly $120B in global value (McKinsey).
TechRadar: Renting GPUs versus owning them creates a significant economic gap — renting an NVIDIA H100 can cost $65K/year, compared to $30K–35K/year when amortized on-prem (TechRadar).
Evolving FinOps Principles for AI Governance
The core FinOps principles remain valid but must be adapted for AI. At Mavvrik, this evolution centers on:
- Visibility: Capture AI-specific cost units — tokens, inference jobs, retrieval calls, GPU hours — in real time across hybrid environments.
- Accountability: Attribute spend directly to models, features, teams, or even agents. Clear ownership reduces uncontrolled consumption.
- Governance: Enforce guardrails such as usage thresholds, prompt approvals, and automated shutdowns of runaway jobs.
This is financial control for AI, embedded at the source where workloads run, not applied retroactively after invoices arrive.
Practical Playbook for Infrastructure and FinOps Leaders
- Track the new AI cost units (tokens, embeddings, GPU cycles, orchestration steps).
- Create dashboards that show costs by model, agent, and feature — not just by cloud account.
- Adopt a hybrid GPU strategy: own baseline resources and use cloud for elastic demand.
- Align cost-to-serve with business outcomes such as retention and revenue lift.
- Treat AI costs as an essential KPI.
- Embed cost telemetry directly into workflows to prevent uncontrolled spending.
Key takeaway: With Mavvrik, you gain precise financial control across your entire hybrid stack, protecting margins as AI scales.
Bottom line: AI Cost Governance as a New Discipline
AI costs are not simply an extension of cloud FinOps. They represent a new financial discipline requiring visibility, accountability, and governance embedded directly into infrastructure. Without this approach, organizations face escalating costs and eroded margins.
The only way to govern AI costs is to track them at the source. That’s what Mavvrik delivers, unifying cost control across cloud, on-prem, AI, and SaaS, enabling enterprises to scale AI innovation sustainably and profitably.
FAQ: Managing AI Costs vs Cloud Costs
Q: Why can’t cloud FinOps tools handle AI costs?
A: They were designed for VM hours and storage, not tokens, agent workflows, or hybrid GPU stacks.
Q: Why is on-prem GPU capacity back in demand?
A: Owning GPUs amortizes baseline costs at nearly half the price of cloud rentals.
Q: How often do model costs change?
A: Frequently. Leading providers adjust pricing quarterly, sometimes monthly, which complicates financial planning.
Q: How does Mavvrik solve this?
A: By embedding visibility, accountability, and governance as close to the source as possible, Mavvrik transforms unpredictable AI spending into financial control.