Feeding the black hole: About AI spend management in the age of Claude and Copilot

Key Takeaways

• AI software now consumes roughly 10–20% of many enterprise software budgets and is growing faster than traditional SaaS, with projections reaching 20–30% by 2026–2027.
• Token-based and usage-based AI pricing creates far more variability than license-based SaaS, making cost visibility and forecasting difficult for IT teams.
• AI infrastructure spend is projected to reach around $2.5 trillion by 2026, underscoring the urgency of formal AI cost management.
• The strategic response requires centralized governance and visibility, rigorous cost optimization, strong processes and culture, and long-term TCO planning.
• IT leaders must treat AI spend as a managed portfolio, with clear owners, dashboards, and controls and not as an opaque R&D line item.

The next challenge in SaaS Management: AI Spend

By 2026, the hype is entering reality. LLMs, copilots, AI features embedded in SaaS consume 10–30% of total software spend, often without matching visibility or governance. This rapid growth is reshaping how IT teams approach cost management entirely.

The contrast with traditional software subscriptions is stark. Stable, seat-based SaaS pricing models (think annual per-user licenses for CRM or collaboration tools of traditional SaaS vendors) offer predictable software costs. This can be easily handled SaaS Management platforms. On the other side, AI vendors like OpenAI, Anthropic, and Cohere bill per token processed, API calls, inference minutes, or throughput tiers, causing monthly invoices to fluctuate wildly.

Token-based and compute-based billing means monthly AI costs can spike with usage bursts. A marketing campaign’s generative content explosion can trigger 3x token spikes during product launches, far exceeding run-rate forecasts. Traditional IT budget models simply weren’t designed for this volatility.

Hidden costs compound the challenge, adding 15–30% annually beyond visible invoices. These include model retraining to combat data drift, prompt engineering for efficiency, monitoring for bias and hallucinations, and compliance audits for regulated data processing.

Many organizations distribute AI tools across business units (marketing, sales, HR, finance), leading to fragmented ownership and shadow AI spend similar to SaaS sprawl but with higher volatility.

Pricing Variability in AI vs. Traditional SaaS

AI pricing structures are significantly more volatile and multidimensional than classic per-seat SaaS models your finance teams know well.

AI vendors often combine several pricing dimensions. It starts with the amount of tokens processed, the model tier (e.g., GPT-4 vs. smaller models), latency SLAs, and dedicated capacity. This creates complex hybrid pricing models that are harder for IT finance to model accurately.

Burst billing exemplifies the risk. A sales team’s AI lead generator during quarter-end could triple token usage via retries and long contexts, yielding one-off invoices 3–5x above normal run-rate. These spikes are unmodelable with traditional IT tools.

Unlike most SaaS vendors, many AI vendors still refine their pricing every 6–12 months, introducing new models or deprecating old ones. Companies like Anthropic and OpenAI constantly change and adapt their pricing model. This adds uncertainty for long-term budgeting that license management processes weren’t designed to handle.

Key Strategies for Managing AI Spend

Who decides what gets built? How is cost forecast? How many tokens get distributed? What if a project eats up all tokens faster than expected? How is usage data monitored and controlled?

The framework that follows ties together:

• Centralized governance and visibility
• Concrete optimization tactics
• Process and culture shifts
• Long-term TCO planning specific to AI workloads

This approach is aimed at IT leaders, CTOs, and CIOs who must integrate AI spend into existing IT cost management disciplines without stifling innovation or creating bottlenecks that slow business operations.

Centralized Governance and Visibility

The first priority is to see and govern AI spend as a portfolio, not as isolated pilots buried in departmental budgets. Without company wide visibility, runaway costs become inevitable.

Build a centralized inventory:

• All LLM APIs and foundation models
• Vector databases and MLOps platforms
• Internal AI applications
• Embedded AI features in existing SaaS tools
• AI powered tools used across departments

Implement centralized dashboards that aggregate cost and usage data from cloud providers (AWS, Azure, GCP), AI vendors (OpenAI, Anthropic, Mistral), and internal chargeback systems into a single view.

Assign an accountable owner for AI cost management. One person or team, could be a FinOps lead or IT finance manager, who are responsible for policies, reporting, and escalation of anomalies across the AI portfolio.

Centralized governance should also define approval levels for new AI services, standard contract terms, and minimum logging requirements for any AI deployment.

Cost Optimization Tactics for AI Workloads

Once visibility is in place, IT teams can systematically reduce costs and stabilize AI spend through infrastructure optimization, rollout patterns, and contracts.

Run phased rollouts: Require all new AI features to go through a limited pilot with clear cost-per-outcome metrics (e.g., cost per resolved ticket, cost per generated document) before scaling to enterprise-wide deployment.

Optimize models and prompts:

• Use smaller or cheaper models where possible
• Cache responses for common queries
• Reduce unnecessary context length to lower token usage

Negotiate transparent contracts: Secure clear token tiers, caps, and reporting, plus commitments around price stability windows (e.g., 12 months) and options to downgrade without prohibitive penalties. Most vendors will negotiate pricing when you demonstrate benchmark data from competitors.

Process and Culture for AI Cost Management

Sustainable AI cost control requires embedding cost awareness into processes and culture, not just one-time optimization efforts. This prevents lost value from redundant subscriptions and shadow spending.

Establish formal approval workflows for onboarding new AI tools:

• Require business cases with expected usage patterns
• Include estimated monthly cost and ROI assumptions
• Mandate data risk assessments before deployment

Distribute budget ownership to business units that sponsor AI initiatives while IT sets guardrails and provides shared tooling. This encourages responsible usage without creating central bottlenecks.

Schedule quarterly cross-functional access reviews with IT, finance, security, and line-of-business leaders to:

• Audit active AI tools
• Prune redundant tools and low-value experiments
• Reallocate budgets to proven use cases

Make cost metrics visible to product managers, engineers, and data scientists so they treat cost as a design constraint alongside performance and accuracy. Encourage teams to experiment with cost saving opportunities. This can shift can implement good habits like using cheaper models for non-critical paths. Those learnings must be shared across the organization.

Long-Term Planning and Total Cost of Ownership

AI projects must be evaluated across a multi-year lifecycle, including build, operate, maintain, and retire phases, to avoid chronic underestimation of total cost.

Perform TCO analysis for AI initiatives including:

• Infrastructure and managed services
• Vendor fees and API costs
• Staffing for ML operations
• Data pipelines and storage
• Security and compliance requirements
• Ongoing retraining and monitoring costs

Invest in internal FinOps and ML cost expertise. Dedicated roles or teams who understand both technical architectures and financial impacts, able to model scenarios and advise project teams on data driven decisions will create ownership and accountability.

Build 3–5 year budget plans for major AI programs with assumptions around:

• Model usage growth projections
• Anticipated vendor price changes
• Planned efficiency gains through optimization

Include maintenance estimates in every AI business case: annual model review cycles, data quality improvement programs, and scheduled security testing for AI endpoints. This prevents the underestimation that leads to budget overruns and ensures long term success.

Cost Control Practices for AI Spend

Active cost control (namely monitoring and interventions during execution) is critical because AI spend can drift quickly with changes in usage.

Implement real-time alerts for spend anomalies:

• Daily or weekly thresholds by project
• Sudden spikes in token usage
• Unexpected increases in GPU hours

Enforce quota limits

• Hard caps on tokens per day
• Concurrency limits on inference endpoints
• Guardrails on new environment creation

Apply degradation strategies in production when usage threatens to exceed budgets:

• Temporarily fall back to smaller models
• Serve cached responses for common queries
• Queue non-critical requests for off-peak processing (more and more vendors allow to run during night in order to reduce cost)

Codify cost control practices in infrastructure-as-code and platform configurations, minimizing reliance on manual interventions and reducing time consuming oversight.

Cost Reporting for AI Initiatives

Turn raw cost and usage data into actionable insights for executives, IT leaders, and product teams.

Produce weekly or bi-weekly cost reports by AI project and team, highlighting:

• Total cost for the period
• Cost per transaction
• Trends compared to previous periods

Include variance analysis explaining deviations from budget or forecast higher-than-expected user adoption, model changes, or infrastructure misconfigurations.

Present reports in formats accessible to non-technical stakeholders: clear charts, concise summaries, and callouts where corrective actions are needed. This approach mirrors how organizations handle expense reports for other spending categories.

Integrate cost reporting into regular governance forums so AI spend is reviewed alongside performance, security, and product metrics.

Security and Asset Controls for AI Environments

Uncontrolled AI assets and access create both financial and security risks, particularly when multiple teams spin up software applications independently.

Integrate asset management practices with AI inventories: Every model, endpoint, dataset, and AI-related SaaS tool should be cataloged with an owner, purpose, and lifecycle status. A SaaS management platform like Corma can be helpful here for tracking saas costs and managing software licenses across your tech stack.

Enforce strict access controls:

• Role-based access for model and data usage
• Least privilege principles
• Separation of duties for deployment vs. configuration vs. monitoring

Require multi-factor authentication (MFA) for all model management consoles, cloud dashboards, and admin interfaces. This reduces the risk of unauthorized configuration changes that could drive up costs or expose data.

Implement automated deprovisioning when projects are retired or former employees leave, ensuring credentials and tokens to AI services are revoked to avoid both cost leakage and security exposure from unused licenses.

Comparing AI Spend to Traditional Software Costs

Understanding structural differences between AI and traditional software spending helps CFOs and CIOs adjust their expectations and controls.

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AI cost drivers (tokens, compute, storage for training data) scale with business activity and model quality demands, making them more tightly coupled to revenue-generating operations than many legacy IT systems.

Finance teams should treat AI spend more like cloud based software or utilities, with dynamic monitoring and forecasting, rather than like static licensing costs and service levels.

Risks and Consequences of Uncontrolled AI Spend

Poor AI cost management creates not only budget overruns but also compliance and reputational risks that affect customer retention.

Compliance risks: Untracked AI tools processing personal or regulated data might violate privacy regulations or internal policies, leading to fines or mandated remediation projects.

Shadow AI dangers: Teams quietly adopting external LLM APIs can bypass established security and procurement processes, increasing chances of data leakage and contractual non-compliance, essentially SaaS purchasing without oversight.

Vendor lock-in: Over-commitment to a single model provider without exit strategies limits negotiation power and keeps costs elevated. This reduces your ability to negotiate pricing effectively.

Reputational exposure: Incidents like data exposure via AI prompts, biased outputs in customer-facing tools, or system outages from misconfigured AI workloads can damage brand trust rapidly.

Reputational Damage: How AI Incidents Spill Beyond Budgets

AI mishaps can quickly impact brand trust, not just IT budgets. Consider these scenarios:

Scenario 1: Customer data exposure An AI-powered support chatbot inadvertently reveals sensitive customer information due to poor prompt safeguards. This prompts public backlash and regulatory scrutiny, damaging employee experience and customer trust simultaneously.

Scenario 2: IP leakage An internal generative tool trained on proprietary documents leaks confidential strategy when output is accidentally shared externally via unsecured channels. This represents real business value lost through inadequate controls.

Build crisis playbooks specific to AI incidents:

• Clear escalation paths
• Communication templates
• Predefined remediation steps for data exposure
• Response protocols for harmful outputs or outages

Proactive cost and asset control through central inventories, access policies and logging make it far easier to respond quickly to incidents and demonstrate responsible governance to stakeholders.

Tools and Technologies to Help Manage AI Spend

While process and governance are foundational, purpose-built tools can significantly improve efficiency and help reduce costs effectively.

AI spend management platforms like Corma aggregate model, token, and infrastructure costs, providing dashboards, alerts, and optimization recommendations. Such SaaS management tools help organizations manage subscriptions across cloud based software and AI services.

FinOps tooling and cloud tagging for AI workloads (start by tagging by project, model, team, and environment) enables granular reporting and chargeback. This provides the visibility needed to cut costs strategically.

CASBs (Cloud Access Security Brokers) or cloud-native controls detect and manage shadow AI usage, identifying unauthorized use of public LLMs from corporate networks. This addresses saas spend that occurs outside procurement processes.

AI-specific observability tools track latency, error rates, and token usage alongside cost, enabling teams to balance performance and budget while identifying cost reduction opportunities.

Ensure tooling integrates with existing ITSM, procurement, and security stacks so AI cost management becomes part of the standard IT operating rhythm, delivering practical strategies rather than isolated solutions.

Conclusion: put some light on the black hole

AI is rapidly becoming one of the largest and most volatile components of IT spend, demanding the same rigor as cloud and SaaS management if not more...).

Token-based pricing, dynamic infrastructure needs, and maintenance costs mean AI cannot remain an ungoverned innovation experiment in large enterprise organizations. The rapid growth of AI spending requires strategic decisions today.

The strategic response:

• Centralized governance and visibility
• Systematic cost optimization
• Well-defined processes and culture
• Long-term TCO-focused planning

Your 90-day action plan:

1. Inventory all AI tools across business units
2. Stand up at least a basic spend dashboard
3. Assign an AI cost owner
4. Review high-cost projects for optimization opportunities
5. Eliminate redundant tools and unnecessary applications

Organizations that master AI cost management now will be best positioned to scale AI safely and profitably over the coming years, delivering real business value while maintaining control over spending. Finance leaders who implement these practical strategies will improve efficiency and demonstrate clear business impact from AI investments.

FAQ

How is AI spend typically different from other IT or SaaS costs?

AI spend is more usage-driven (tokens, compute time, storage) and less tied to fixed software licenses, causing higher month-to-month variability than most traditional SaaS tools. Successful AI initiatives can drive their own cost growth as adoption increases with positive for value but challenging for budget predictability. AI also carries ongoing maintenance and risk management costs, like monitoring for bias and data drift, that many legacy systems don’t require.

What is a practical first step to get AI costs under control?

Start with a rapid discovery exercise: compile a list of all AI-related services and tools in use, including external APIs, cloud-based models, and embedded AI in existing platforms. Pull the last 6–12 months of invoices from cloud providers and AI vendors to identify top cost drivers and potential quick wins. Nominate a single owner often to coordinate this effort and define initial reporting standards. This is usually either the IT or FinOps team.

How often should we review AI spend and usage?

Weekly or bi-weekly internal reviews work best for high-cost or high-risk AI workloads to catch anomalies early. Monthly consolidated reports for IT leadership should summarize trends and highlight projects exceeding thresholds or delivering exceptional ROI. Quarterly strategic reviews should align AI spend with broader business priorities and update budgets and roadmaps accordingly.

Can we rely on vendors’ usage dashboards for AI cost visibility?

Vendor dashboards are useful but often siloed, covering only that provider’s perspective and lacking full context across cloud, internal costs, and multiple AI providers. Aggregate vendor data into a centralized internal dashboard or spend management platform to get a complete and comparable view. Internal tagging and chargeback models are still needed to map vendor-level spend to business units and specific projects.

How do we justify AI cost management investments to executives?

Frame AI cost management as protecting and amplifying AI ROI: better visibility and control enable scaling successful use cases while pruning waste. Use concrete examples showing how small misconfigurations can generate tens of thousands in unplanned monthly spend on GPUs or tokens. Highlight that strong governance also reduces compliance and reputational risks, which are increasingly important to boards and regulators.

How can Corma help manage AI cost?

Corma helps manage AI spend by taking SaaS Management a step further. It provides real-time visibility into AI tool subscriptions, usage analytics, and license optimization. This ensures organizations only pay for what they actually use. Its automated tracking and alerts also help prevent uncontrolled AI SaaS proliferation, while its self-service app store and approval workflows ensure cost-effective, compliant adoption of AI tools across teams.