AI ROI Calculator.
Companies are moving fast with AI. Many still can’t answer a basic question: what does this initiative really cost, and is it paying off?
This calculator helps you see both sides of the ROI equation.
Focus on one AI initiative at a time, for example:
- Customer support chatbot for tier-1 tickets
- AI-powered sales prospecting and outreach
- Automating software development tasks (coding, testing, docs)
- Code review and quality assurance
- AI copilots embedded in SaaS products
Have your AI initiative in mind? Let’s calculate the ROI.
Current Investment
What does this work cost you today?
Start with your baseline. What are you spending now on people and technology to get this work done?
Total Current Cost: $0/year
Expected Value
What value will AI create?
Enter the annual value you expect from AI across different areas.
Total Expected Value: $0/year
AI Infrastructure Costs
What will it cost to run AI?
Tip: Estimate the costs associated with this specific project.
Total AI Cost: $0/year
Your True AI ROI
Here is the complete picture
Calculating ROI comes down to knowing both sides of the equation.
How Mavvrik Helps
Mavvrik brings visibility and control to your entire AI cost footprint.
Profitable innovation or expensive experimentation?
Up to 80% of AI costs are missed by traditional cloud FinOps. Traditional FinOps tools weren't built for AI.
They miss the model calls, orchestration layers, and agentic workflows that make up the true cost of your AI initiatives.
Full-Stack
Visibility
AI spend goes far beyond what shows up in your cloud bill. See every cost layer in one place.
- Infrastructure costs like compute, GPUs, and storage
- GenAI service costs like model APIs, fine-tuning, and embeddings
- Agentic workflow costs like orchestration, retries, and supporting pipelines
Attribution Intelligence
Knowing what you spend isn't enough, you need to know why. Trace every dollar back to the team, product, feature that drove it.
- Team and project attribution so no AI spends goes unowned
- Feature-level cost allocation across shared infrastructure
- Anomaly detection that flags cost spikes before they become surprises
Unit Economics & Measurements
Turn cost data into the business case leadership needs. Go from "we think AI is working" to "here's what it's returning."
- Cost-per-outcome metrics tied to real business results
- ROI trending over time so you can prove - and improve - value
- Budget guardrails that keep AI investment aligned with expected returns
FAQs
Your questions, answered.
How can I calculate ROI for my AI initiatives?
Start with the standard formula: ROI = (Value – Cost) / Cost.
Define measurable value across productivity gains, revenue impact, or cost reduction, then capture all related AI costs including GPUs, API tokens, vector queries, and team time. Tools like Mavvrik’s AI ROI Calculator make it possible to model ROI for a single AI initiative in minutes.
What costs should be included in AI ROI calculations?
True AI ROI goes far beyond GPU and model API costs. It also includes vector databases, orchestration frameworks, data-pipeline compute, observability tools, and human effort. These indirect costs often account for 50–80% of total AI spend and are the primary reason AI ROI is difficult to measure.
Why do AI ROI calculations differ from traditional cloud ROI?
Traditional Cloud FinOps is based on predictable units like compute hours and storage. AI introduces volatile cost drivers—tokens, embeddings, agent actions, and retrieval queries—that change with each model or workflow update. Accurate AI ROI requires embedded telemetry and cost attribution at the feature, product, or application level.
How do I know if my AI initiative is profitable?
An AI initiative is profitable only when outcomes are directly tied to costs. Track productivity, revenue, or cost savings against total AI spend, including infrastructure, APIs, and human resources. If cost-to-serve exceeds the value delivered, the initiative is eroding margin, even if usage is growing.
How often should I re-evaluate AI ROI?
Re-evaluate AI ROI at least quarterly, and immediately after changes to models, pipelines, or GPU strategy. Token pricing shifts, usage spikes, or new orchestration layers can materially change ROI overnight. Continuous monitoring helps prevent small inefficiencies from compounding into margin erosion.
How does Mavvrik help improve AI ROI?
Mavvrik embeds financial control directly into AI and hybrid infrastructure. It unifies token, GPU, and data-pipeline visibility; attributes costs to teams and products; and automates chargeback to enforce accountability. That transparency helps finance, product, and engineering teams make informed decisions about which AI initiatives to scale or stop.
What data backs this ROI framework?
This framework builds on the State of AI Cost Governance research by Mavvrik and Benchmarkit, plus benchmarks from BCG, McKinsey, Google Cloud, and Gartner. It combines global research with real telemetry from Mavvrik customers to create a practical, finance-grade model for measuring return.
What’s the best way to start tracking AI ROI?
Begin by baselining current AI spend and mapping measurable outcomes to costs using Mavvrik's AI ROI Calculator. Then implement continuous telemetry with a FinOps platform like Mavvrik to capture cost signals in real time. Review ROI quarterly to identify which applications are driving real value, and which are silently draining profit.
NO RISK PROOF OF VALUE
Ready to take control of your AI and hybrid costs?
Get a personalized demo and discover how Mavvrik helps:
Track every dollar across cloud, on-prem, AI, and SaaS in one platform.
Eliminate surprises with real-time cost allocation, anomaly detection, and predictive forecasting.
Automate cost control with chargebacks, budget guardrails, and AI-driven insights.
Request a Demo