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.
Each input shapes the cost. Click an input on the calculator to set it — explanations below match the live calculator field by field.
Split your annual AI budget across product features by ROI priority. Avoid overspending on shiny features at the cost of high-ROI utility ones.
budget
Below: live sliders. Move them to see numbers change in real time. * Output uses the generic compute model — for precise numbers use the full calculator below.
Each input shapes your cost. Move the slider — see the impact.
Open the full calculator — pick a model, enter your tokens, see per-call, daily, monthly, and annual cost.
🚀 Open the full calculator →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.
Same calculator, three different 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
Cost isn't the only dimension. Click any constraint — 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 →
Pre-loaded scenarios for the most common applications. Click a tab to see realistic numbers — then the "Try this scenario" button 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
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.
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 →