Guides → Playground & Guide → AI Budget Planner - Allocate Spend Across Use Cases
Meet Daniel Liu. VP Product at a 100-person SaaS. "I have $180K annual budget and 6 AI features competing for it. How do I allocate without screwing the team that needs it most?"
🔥 Last year I gave it all to the chatbot. Search team got nothing and built a worse experience that hurt retention.
AI budget allocation is product strategy, not finance. The wrong allocation produces predictable failures - the loud feature gets funded, the high-ROI utility feature starves. Six months later: the loud feature isn't moving metrics, the utility one is degraded, and you can't tell why.
Daniel's 6 features compete for $180K. The chatbot gets the loudest meeting attention but moves retention 0.5%. Search powers 40% of traffic but is 'just retrieval.' Recommendations drives 12% of revenue but is 'old AI.' The framework here is to score each feature on ROI (not enthusiasm), allocate by priority, and reserve buffer for the highest-leverage feature to grow.
This calc takes your annual budget, your features list with ROI scores, and produces a defensible allocation. Plus reserves a 15-20% buffer for the inevitable optimization needs.
Here are the inputs that move the result the most. Play with the sliders and check it out. The number updates live.
Splits your annual budget across features after holding back a reserve. Top-priority share fixed at 35%.
💡Per feature = (budget − reserve) ÷ 12 ÷ features. The reserve and top-priority allocation are in “Why this number”.
Same calculator, three team sizes. Click a tab to see how the numbers shift.
Daniel's situation. $180K total → $27K buffer → $153K allocatable. Top feature gets $54K (35%), 3 mid features get $22K each ($66K total), 2 low features get $16.5K each ($33K total). 6 features properly funded with buffer for surprises.
Healthy range: Top: ~$54K, mid 3: ~$22K each, low 2: ~$10K each
Smaller budget, fewer features, big bet on top. $85K allocatable, $42.5K to top feature, $21K to each of 2 supporting. Works when one feature dominates roadmap importance.
Healthy range: Top: $42.5K, 2 supporting: $21K each
Early-stage with 10 experimental features. Higher buffer (25%) for inevitable pivots. Top feature gets only 25% - concentration matters less when nothing has proven yet.
Healthy range: Higher buffer, lower per-feature allocation
Same calculator, different applications. Sizes above, workloads here. Pick the one that looks like yours.
Pre-loaded scenarios for the most common applications. Click a tab to see realistic numbers, then hit "Try this scenario" to load it into the calculator above.
Going into board with 5 AI initiatives. $250K budget. 20% buffer ($50K) signals discipline. Top feature 40% of allocatable shows you have a clear bet. Mid 3 at ~17% each. One low at 5% (kill candidate).
Healthy range: Defensible 5-feature allocation
Vendor raised prices 30% mid-year. Annual budget effectively shrunk. Buffer drops from 20% to 10% to maintain feature funding. Plan to ship optimization to restore buffer by Q4.
Healthy range: Shrunk buffer post-shock
7 features competing. With 40% top share + buffer, only top 3-4 features get meaningful funding. Rest at $5-8K each - visibly underfunded. Forces the prioritization conversation explicitly: 'these 3 are getting funded, these 4 need to find another path.'
Healthy range: Clear top-3 + visible kill candidates
Open the full calculator. Pick a model, enter your tokens, see per-call, daily, monthly, and annual cost.
🚀 Open the full calculator →Split your annual AI budget across product features by ROI priority. Avoid overspending on shiny features at the cost of high-ROI utility ones.
Each input shapes the cost. Click an input on the calculator to set it. The explanations below match the live calculator field by field.
| Input | Default | Typical ballparks |
|---|---|---|
annualBudgetUsd
moves the needle
|
180,000 | Pilot · ~$6K/yr = 6,000 · Team · ~$60K/yr = 60,000 · Department · ~$600K/yr = 600,000 · Enterprise · ~$3M/yr = 3,000,000 |
numberOfFeatures
moves the needle
|
6 | — |
bufferReservePct
moves the needle
|
15 | — |
topPriorityShare
|
35 | — |
Ballparks are broad industry starting points (sourced ranges; * = rough estimate) — your result gets more accurate as you replace them with measured numbers.
Try them live in the calculator;
API & agent users get the same data from the MCP resource aicost://input-reference/budget-planner.
Your top-priority feature should get 30-40% of allocatable budget. Concentrating budget on the highest-ROI feature beats spreading it. Diminishing returns kick in around 50%+.
Mid-priority features get equal-ish slices. 3 features at 18-22% each is typical for the middle tier.
Low-priority features should be at zero or under-explore allocation. If a feature can't justify >5% of budget, it shouldn't be funded with company-wide AI dollars - let the team find another path.
The 15-20% buffer is non-negotiable. Vendor pricing changes 30-50% per year. New high-ROI feature requests come monthly. Without buffer, every surprise becomes a fight over existing allocations.
Honest limitations. Every model is wrong; some are useful. Where this one falls short:
For these, use: Annual Forecaster for the time-axis. Scale Projection for stress testing. Full TCO Wizard for sensitivity.
Cost isn't the only dimension. Click any constraint to see how recommendations change.
Don't optimize the lowest-funded feature - concentrate optimization on the top 1-2. They're where the savings move the needle.
Hallucination cost is highest on customer-facing features. Allocate premium tier budget there. Internal-facing? Cheap tier with disclaimers is fine.
Don't shave compliance-tier feature budget to fund non-compliance features. Customer breach > budget shortfall by orders of magnitude.
Privacy posture should be the default for everything customer-facing. Doesn't drive significant budget pressure.
Voice agents, real-time features may justify a second vendor relationship for latency. Adds operational complexity but worth it for the right feature.
5% of budget for multi-vendor router (LiteLLM, custom) protects all your other allocations from vendor pricing surprises. High-leverage spend.
1 AI engineer's time should be a separate line, not allocated to features. Otherwise the highest-budget feature accidentally pays for company-wide MLOps work.
Tradeoff analysis is where most AI projects go sideways. Talk to a CFO-grade AI cost analyst →
The gaps we just listed are real, and they are the expensive ones: your actual prompts, your switching cost, your MLOps overhead. An AICost expert spends the hour on your AI and cloud costs, not a generic playbook. You leave with a written report: the way forward, in 30 days of concrete steps.
Not sure yet? The $39 AICost Blueprint credits toward a Session, and the Session fee credits toward any plan. You never pay twice for the same ground. See all pricing →
You have seen the shape of it. Open the calculator with your model, your tokens, your volume.
🚀 Open the full calculator →Once allocated, forecast each feature's monthly burn over the year.
Validate per-feature unit economics →Check that each feature's allocation produces healthy margin.
Stress-test budget against vendor surprises →What if your primary vendor raises 50%? Buffer enough?
Author: Subu Vdaygiri, Founder & CEO of CloudIntelligence.ai. 17 years Fortune 100 (Ingram Micro, Siemens). Wharton CTO program · Kellogg CPO program · 10× AWS+Azure certified.
Why this matters: pricing for major vendors has dropped 40-90% in the last 24 months. A budget set 12 months ago is probably wrong by 30%+.
View 3-year history for →
Last-verified date is the most recent successful daily snapshot
(aicost_pricing_snapshots) or, when no snapshot exists yet,
the latest successful crawler run (aicost_crawler_runs).
10 of 10
vendors are currently verified. Aggregator services (TokenCost, AI Pricing Guru, etc.)
are not listed.
Derived from industry conventions, not directly published by the vendor. Typical conventions: cached input = 10% of base (90% off), Batch API = 50% of base (50% off).
| Vendor / Model | Field | Why it’s inferred |
|---|---|---|
| Anthropic — Claude Sonnet 4.6 | cachedInput |
Derived at 10% of input rate — Anthropic publishes 90% cache-hit discount on this tier. |
| Anthropic — Claude Sonnet 4.5 | cachedInput |
Derived at 10% of input rate; same 90% cache-hit convention as Sonnet 4.6. |
| Anthropic — Claude Sonnet 4.5 | batchInput |
Derived at 50% of standard input — Anthropic documents uniform 50% Batch discount. |
| Anthropic — Claude Sonnet 4.5 | batchOutput |
Derived at 50% of standard output — Anthropic documents uniform 50% Batch discount. |
| Anthropic — Claude Haiku 4.5 | cachedInput |
Derived at 10% of input rate — Anthropic 90% cache-hit discount convention. |
| OpenAI — GPT-5.4 Mini | cachedInput |
Derived at 10% of input — OpenAI documents automatic 90% discount on cache hits across GPT-5.x tier. |
| OpenAI — GPT-5.4 Nano | cachedInput |
Derived at 10% of input — OpenAI 90% cache-hit convention. |
| OpenAI — GPT-5.4 Nano | batchInput |
Derived at 50% of input — OpenAI Batch API uniform 50% discount. |
| OpenAI — GPT-5.4 Nano | batchOutput |
Derived at 50% of output — OpenAI Batch API uniform 50% discount. |
| OpenAI — GPT-5.4 Pro | cachedInput |
Derived at 10% of input — OpenAI 90% cache-hit convention. |
| OpenAI — GPT-5.4 Pro | batchInput |
Derived at 50% of input — OpenAI Batch API uniform 50% discount. |
| OpenAI — GPT-5.4 Pro | batchOutput |
Derived at 50% of output — OpenAI Batch API uniform 50% discount. |
| OpenAI — GPT-5.2 | cachedInput |
Derived at 10% of input; no residency uplift. |
| OpenAI — GPT-5.2 | batchInput |
Derived at 50% of input. |
| OpenAI — GPT-5.2 | batchOutput |
Derived at 50% of output. |
| OpenAI — GPT-5 | cachedInput |
Derived at 10% of input. |
| OpenAI — GPT-5 | batchInput |
Derived at 50% of input. |
| OpenAI — GPT-5 | batchOutput |
Derived at 50% of output. |
| OpenAI — GPT-5.5 Pro | cachedInput |
Derived at 10% of input — OpenAI does not publish a cached rate for *-pro models; using the family convention. |
| OpenAI — GPT-5.5 Pro | batchInput |
Derived at 50% of input. |
| OpenAI — GPT-5.5 Pro | batchOutput |
Derived at 50% of output. |
| OpenAI — GPT-5.2 Pro | cachedInput |
Derived at 10% of input — pro-tier convention. |
| OpenAI — GPT-5.2 Pro | batchInput |
Derived at 50% of input. |
| OpenAI — GPT-5.2 Pro | batchOutput |
Derived at 50% of output. |
| OpenAI — GPT-5.1 | batchInput |
Derived at 50% of input. |
| OpenAI — GPT-5.1 | batchOutput |
Derived at 50% of output. |
| OpenAI — GPT-5 Pro | batchInput |
Derived at 50% of input. |
| OpenAI — GPT-5 Pro | batchOutput |
Derived at 50% of output. |
| OpenAI — GPT-5 Nano | cachedInput |
Derived at 10% of input. |
| OpenAI — GPT-5 Nano | batchInput |
Derived at 50% of input. |
| OpenAI — GPT-5 Nano | batchOutput |
Derived at 50% of output. |
| Google — Gemini 3 Flash | cachedInput |
Derived at 10% of input — Google caching discount convention ~90%. |
| Google — Gemini 3.1 Flash-Lite | cachedInput |
Derived at 10% of input — Google caching convention. |
| Google — Gemini 3.1 Flash-Lite | batchInput |
Derived at 50% of input — Google Batch API uniform 50% discount. |
| Google — Gemini 3.1 Flash-Lite | batchOutput |
Derived at 50% of output — Google Batch API uniform 50% discount. |
| Google — Gemini 2.5 Pro | cachedInput |
Derived at 10% of input. |
| Google — Gemini 2.5 Flash | cachedInput |
Derived at 10% of input. |
| Google — Gemini 2.5 Flash-Lite | cachedInput |
Derived at 10% of input — Google caching convention. |
| Google — Gemini 2.5 Flash-Lite | batchInput |
Derived at 50% of input — Google Batch API uniform 50% discount. |
| Google — Gemini 2.5 Flash-Lite | batchOutput |
Derived at 50% of output — Google Batch API uniform 50% discount. |
| Google — Gemini 2.0 Flash | cachedInput |
Derived at 25% of input per Google 2.0 family caching rates. |
| Google — Gemini 2.0 Flash | batchInput |
Derived at 50% of input — Google Batch API uniform 50% discount. |
| Google — Gemini 2.0 Flash | batchOutput |
Derived at 50% of output — Google Batch API uniform 50% discount. |
| Google — Gemini 2.0 Flash-Lite | cachedInput |
Derived at 10% of input — Google caching convention. |
| Google — Gemini 2.0 Flash-Lite | batchInput |
Derived at 50% of input — Google Batch API uniform 50% discount. |
| Google — Gemini 2.0 Flash-Lite | batchOutput |
Derived at 50% of output — Google Batch API uniform 50% discount. |
| xAI — Grok 4 (legacy) | cachedInput |
Extrapolated at 25% of base. |
Pricing is cross-verified against the
LiteLLM community registry
when available. Daily snapshots are kept in aicost_pricing_snapshots;
every change is logged to aicost_price_changelog with old & new
values for full audit trail. Read the full methodology →