Understand consumption, forecast demand, and evaluate contract commitments with scenario-based financial intelligence. Contopty combines contract terms, adoption assumptions, and optional usage history; explore up to 10,000 Monte Carlo scenarios to support budgeting, commitment decisions, and vendor negotiations.
Built for complex AI, cloud and consumption-based agreements.
AI Platform Enterprise Agreement
Modeled spend percentiles
| P50 | $21.9M |
|---|---|
| P90 | $34.1M |
| P95 | $38.8M |
Review commitment level
Overage probability is the chance modeled usage exceeds the included allowance.
Understand the past. Forecast the future. Make better decisions.
Understand how software is used, model how demand may evolve, and evaluate the financial consequences of your next decision.
Past
What actually happened?
Historical usage data is optional and is not required to run a Contopty analysis. When provided to our engine, past consumption data helps identify usage patterns, changes in demand, and provides additional context for financial planning.
Next: XGBoost Machine Learning
Advanced machine learning for future consumption forecasting.
Future
What happens next?
Estimate future consumption based on eligible users, adoption rates, daily activity, and usage per task. Contopty models how these assumptions affect expected spending, overage risk, and contract commitments. Alternatively, forecast demand using Low, Most Likely, and High usage estimates.
Decision
What's the right financial decision?
Compare modeled contract alternatives based on expected spending, overage exposure, and financial risk. Understand the trade-offs between cost and risk before deciding whether changing your contract is worthwhile.
Historical intelligence → Future demand → Financial decision
Forecasts are scenario ranges, not guaranteed outcomes.
Software commitments are financial decisions. Contopty helps teams evaluate demand assumptions, understand potential overage exposure, and compare contract alternatives through transparent, scenario-based analysis.
CFOs and Finance teams
Budget ranges and exposure, not single-point guesses.
Procurement leaders
Evidence for commitment levels and negotiation priorities.
IT Finance and SAM teams
Visibility into consumption against contracted capacity.
78%
of surveyed IT leaders reported unexpected charges related to AI or consumption-based pricing.
Source: Zylo, 2026 SaaS Management IndexActual usage can exceed forecasts, creating unexpected overage costs.
Overestimated demand can leave companies paying for capacity they never consume.
Overage rates, allowances and expiry rules make financial decisions harder to evaluate.
PDF · DOC · DOCX
Contopty extracts pricing, commitment and usage terms and flags unclear terms for review.
Confirm before modeling
Review commitments, allowances, rates, fees, currency, rollover rules and unresolved terms before analysis.
Usage assumptions · User adoption · Optional historical usage
Model future consumption using expected usage ranges or user adoption assumptions. Historical usage data can also be included optionally to provide additional context when available.
Up to 10,000 Monte Carlo scenarios
Estimate expected spend, P50, P90 and P95 outcomes, overage probability and unused commitment across a configurable simulation model.
Optional usage-history analysis is available today. A planned XGBoost-based forecasting layer would extend this capability by learning from suitable historical data and relevant business signals to suggest future usage estimates. Improvements would need to be demonstrated on later, unseen data.
Available today: usage assumptions and optional history-informed modeling. Planned: machine-learning forecasting.
Contopty protects stored contract documents with application-level AES-256-GCM encryption. Each document is encrypted before being persisted and uses its own encryption key. Stored documents remain encrypted and are only decrypted temporarily when authorized processing requires access to them.
More on securityContract documents are encrypted with AES-256-GCM before being stored.
Each stored contract uses its own randomly generated encryption key, protected by a separate server-side master key.
Original contract documents are not retained as plaintext in storage.
Turn contract terms, usage assumptions, and optional historical usage into modeled spend and risk before making a commitment decision.
Understand commercial exposure before signing.
Forecast downside and protect budgets.
Connect contract structures to real consumption.
Understand the economics of AI and cloud adoption.