Maximise budget visibility with AI spend forecasting
SAVI builds a running forecast of your AI spend across every provider, model, and team - so budget decisions are based on where you are heading, not where you have been. Budget confidently, commit to the right capacity, and stop reacting to end-of-quarter surprises.
Continuously optimise your AI budget planning and eliminate spend surprises
With SAVI's personalised forecasting, you'll always know your next best move. Analyse spend trajectories by model and team, simulate budget scenarios, and discover where your AI investment is tracking against plan - in real time. SAVI helps FinOps teams prioritise budget decisions by projecting the cost impact of every potential change. After a budget is approved, engineers see exactly what headroom they're working within - and can verify the impact of releases in real time.
Companies routinely underestimate unplanned AI spend
Since AI providers charge for every token processed - not just the ones that produce useful output - you must forecast at the model and team level to govern effectively.
Over-committing and under-budgeting both cost you
Over-committing leads to unused capacity and inefficiency. Under-budgeting leads to emergency approvals and friction with engineering teams.
Recommendations without context drive the wrong decisions
Identifying the cost impact of every potential change helps your team determine its priorities and avoid acting on recommendations that don't fit your workload.
Pinpoint your AI spend trajectory by model, team, and provider
Companies often underestimate how much of their AI spend is unplanned. Since AI providers charge for every token processed - not just the ones that produce useful output - you must be able to forecast at the model and team level to govern effectively. With SAVI, you can continuously forecast and plan for all categories of AI spend.
- Model-level 30/60/90-day spend projections
- Team and project budget utilisation tracking
- Seasonality and growth trend detection
- Forecast vs actual variance with root-cause attribution
- Provider-level commitment coverage forecasting
| provider | forecast30d | change | share |
|---|---|---|---|
| OpenAI GPT-4o | $30,240 | +29% | 54% |
| Anthropic Claude | $10,320 | −3% | 18% |
| Azure OpenAI | $7,800 | +2% | 14% |
| AWS Bedrock | $5,040 | −6% | 9% |
| Google Vertex + others | $2,600 | 0% | 5% |
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30-Day Forecast by Provider · Total $56,000 · 98.5% accuracy · Powered by Meta Prophet · Updated every 5 min
Budget Scenario Simulator - GPT-4o +25% growth · 60% routed to Haiku · Semantic caching ON
Rightsize your AI budget commitments intelligently
An important ongoing step in AgentOps is to rightsize your AI budget. Over-committing leads to unused capacity which drives inefficiency, while under-budgeting leads to emergency approvals and friction with engineering teams. With SAVI, you can make informed budget tradeoffs by simulating the cost impact of every major decision - model upgrades, feature launches, policy changes - before you commit. SAVI always sets forecast confidence intervals so finance will not be caught off guard.
- "What-if" scenario planning across any dimension
- Growth rate modelling per team and product line
- Savings simulation from caching, model swaps, and budget policies
Make the right AI model commitments
Commitment-based discounts - AWS Bedrock Reserved Capacity, Azure AI Credits, Google Vertex committed use - can save 30-50% versus on-demand pricing. But commit to the wrong tier and that advantage disappears fast. SAVI tells you which commitment makes sense based on what you've actually used and what you're forecast to use next - not a guess.
- Personalised commitment-tier recommendations based on historical + forecasted usage
- Coverage tracking - see what % of your usage is locked into a discount vs. paying on-demand
- Alerts before a commitment expires or under-utilises, so you don't lose the discount by accident
| model | provider | utilisation | status |
|---|---|---|---|
| GPT-4oBatch commitment | Azure OpenAI720hr/mo reserved | 88% | Healthy |
| Claude SonnetStandard | AnthropicPay-as-you-go | On-demand | Flexible |
| Bedrock TitanReserved | AWS Bedrock1yr commitment | 62% | Review |
| Gemini ProCUD | Google VertexCommitted use | 95% | Healthy |
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Commitment Utilisation · June 2026 - Bedrock commitment at 62% · Consider downgrading · Est. $2,840/mo recovery
Cost-Saving Recommendations · Active · Total monthly savings identified: $17,320 / month
Don't act on every recommendation - act on the right ones
Cost-saving recommendations are a starting point for a conversation with engineering, not an instruction to execute blindly. SAVI ranks recommendations by impact, keeps a full history of what was tried and what happened, and shows you the before/after cost of every change once it's live. Snooze what's not ready. Tune what doesn't fit your workload. Reject what doesn't make sense - and keep the record either way.
Forecasting
Do you know what your AI spend will be next month?
11 questions. Answer honestly.
Can you forecast your AI spend at the model and team level with better than 90% accuracy?
Can you track AI spend trends over time and identify whether they correlate with business growth?
How do you evaluate and prioritise cost-saving recommendations across your AI portfolio?
Are cost optimisation recommendations passed "over the fence" to engineering, or reviewed collaboratively?
Do you review AI budget decisions with stakeholders who understand both the applications and the architecture?
Are you able to track the cost impact of implemented changes - before and after - at the model level?
If a cost-saving recommendation can't be actioned immediately, can you snooze it for an agreed period without losing track of it?
Do you continuously evaluate how new model releases from providers affect your existing cost structure?
Can you identify unused or under-utilised commitment-based AI discounts with daily resolution?
Do you know when a commitment utilisation rate declines, stops being used, or needs attention when it deviates from the norm?
Are stakeholder teams notified early enough about expiring AI commitments to plan new purchases before pricing reverts to on-demand?
This checks one slice of your AI estate. Take the full AI Cost Ownership Assessment →
Your AI gap score
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Take the next step
Schedule a discovery call to see how SAVI's AI spend forecasting can ensure your FinOps success - and give your CFO the confidence they need.