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Overage Forecaster - When Will You Breach Your AI Budget?

🚀 Open the calculator

Meet Carlos Mendez. Engineering manager owning the AI cost line. "We're 11 days into the month and at 47% of monthly budget. Are we going over?"

🔥 Last month we hit budget on day 28. Three more 'last months' and finance kills the project.

The story

Month-to-date overage forecasting is daily FinOps for AI. Cloud has it solved (AWS Cost Explorer, third-party tools). AI is mostly post-mortem - 'oh we breached budget last month.' That's too late.

Carlos's team is at 47% of budget on day 11. Naive projection: 47% × (30/11) = 128% of budget. But days 1-3 had a load test (artificial spike). Days 8-11 had a holiday (artificial dip). Actual run-rate is closer to 105% of budget - slight overage, manageable.

This calc takes month-to-date spend, factors in trend + day-of-week patterns + known anomalies, and projects the end-of-month number. If you'll breach, it tells you which day.

About this calculator: Overage Forecaster - When Will You Breach Your AI Budget?

Project when your AI spend hits the budget cap. Models trend + variance + vendor pricing. Get the breach date and the optimization runway you have left.

Inputs you control

Input Impact on result Range Typical
Monthly budget cap ($) The budget number from finance. The line that matters. 100 – 500K 10000
Month-to-date spend ($) Pull from current month's invoice or vendor dashboard. 0 – 500K 4700
Days elapsed in month Today's day-of-month. 1 – 31 11
Trend multiplier (1.0 = flat) Adjust for known patterns. >1.0 if usage is accelerating (recent feature launch). <1.0 if dipping (post-launch spike normalizing). 1.0 if you have no signal - let the calc project linearly. 0.5 – 2 1

Outputs computed for you · model: overage_forecast

Output How inputs affect it
Monthly cost ($) computed from inputs
Annual cost ($) monthlyUsd × 12

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.

What you're looking at

Each input shapes your cost. Move the slider — see the impact.

10,000

The budget number from finance. The line that matters.

Estimated: —
4,700

Pull from current month's invoice or vendor dashboard.

Estimated: —
11

Today's day-of-month.

Estimated: —
1

Adjust for known patterns. >1.0 if usage is accelerating (recent feature launch). <1.0 if dipping (post-launch spike normalizing). 1.0 if you have no signal - let the calc project linearly.

Estimated: —

Ready to run the numbers?

Open the full calculator — pick a model, enter your tokens, see per-call, daily, monthly, and annual cost.

🚀 Open the full calculator →

Reading your result

Projected EoM is the headline. Naive projection is MTD × (30/days_elapsed). Trend-adjusted is naive × multiplier. Both are useful - naive is conservative, trend-adjusted is realistic.

Breach day tells you the runway. If you'll breach on day 26, you have 15 days to optimize. If breach on day 35 (i.e., not this month), you have time to bring optimization in next month's plan.

Overage amount is the conversation. Sub-10% overage is usually absorbed quietly. 10-25% triggers a finance conversation. 25%+ becomes board-level if budget is large.

Watch the day-of-week effect. B2B usage drops weekends - weekend MTD undercounts run-rate. Late-month projections from early-week data tend to under-project.

What "good" looks like:
  • Within budget: EoM projection ≤ 100% of budget
  • Mild overage: 100-110% - finance won't notice, you should
  • Material overage: 110-125% - quarterly conversation
  • Crisis overage: 125%+ - kill features or raise budget mid-cycle

Cheapest 3 vendors right now

Verified 20 hours ago
  1. 1
    GPT-5 Mini
    $0.250 in · $2.00 out ·
  2. 2
    Command
    $1.00 in · $2.00 out ·
  3. 3
    devstral-2
    $0.400 in · $2.00 out ·

Three real scenarios

Same calculator, three different team sizes. Click a tab to see how the numbers shift.

$11,400 / month ≈ $136,800 / year

Day 5, $1,900 spent, projects to $11,400/month. 14% over. You have 25 days to ship optimization or talk to finance. This is the right time to act.

Healthy range: Day 5 projection at 114% - early warning

See inputs used
monthlyBudgetUsd
10,000
monthToDateSpentUsd
1,900
daysElapsed
5
trendMultiplier
1

Trade-offs

Cost isn't the only dimension. Click any constraint — see how recommendations change.

What matters most to you? Click any dimension — recommendations update.

Best fit for "cost":

  1. Hard rate-limit on non-critical workloads Last-resort breach prevention
  2. Route to cheap models when over 80% of budget Auto-throttle pattern

Overage prevention should be automated, not manual. Set rate limits at 90% MTD to give yourself buffer. Set tier-downgrade rules at 95% to accept quality dip rather than budget breach.

Use cases

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.

$22,500 / month ≈ $270,000 / year

Weekly review use case. Day 14, $10.5K spent, light trend deceleration (post-launch normalization). Projects $22K - comfortably under $25K budget. Report green; no action needed.

Healthy range: Projected ~$22K - 12% under budget

See inputs used
monthlyBudgetUsd
25,000
monthToDateSpentUsd
10,500
daysElapsed
14
trendMultiplier
0.95

What this calculator can't tell you

Honest limitations — every model is wrong; some are useful. Where this one falls short:

For these, use: Annual Forecaster for the 12-month picture. Budget Planner to allocate before the month starts.

Where to go next

12-month projection (full year) →

When does annual budget breach? Plan optimization across the year.

 Allocate budget across use cases →

Prevent overage by sizing the budget right at month start.

 Hedge vendor pricing surprises →

Multi-vendor abstraction protects against mid-month surprises.

Methodology

Source
/ai-cost-economics
Extraction
Linear projection + trend multiplier validated against 6 months of aicost.ai snapshot data.
Editorial gate
8-layer defense — see aicost.ai/ai-cost-economics
Last verified
6/4/2026, 8:00:00 PM

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.

3 years of pricing history

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 →
📖 Data sources & methodology 161 text models · 9 embeddings · 24 vision · 41 audio · 8 vector DBs across 10 vendor pages · last verified 2026-06-05

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-06-05.
  • Vendor-published values have no mark. Inferred/extrapolated values are marked with * and listed below.
  • Batch API discounts are 50% off standard rates across providers that offer Batch mode.
  • Prompt caching discounts vary by provider (typically 80-90% off cached input tokens).
  • Regional data-residency surcharges (Anthropic 1.1x, OpenAI 1.1x, Google regional tiers) are NOT included in base rates.
  • Long-context pricing tiers apply when input exceeds model threshold.
  • Embedding prices are input-only (no output tokens generated).

Primary sources

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.

Anthropic
2026-06-05
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 578 days captured
Anthropic Docs
2026-06-05
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 578 days captured
OpenAI
2026-06-05
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 579 days captured
Google AI
2026-06-05
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 554 days captured
Google Vertex
2026-06-05
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 554 days captured
DeepSeek
2026-06-05
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 493 days captured
xAI
2026-06-05
https://x.ai/api
Daily snapshot since Nov 2024 · 411 days captured
Mistral
2026-06-05
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 552 days captured
Cohere
2026-06-05
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 578 days captured
Voyage AI
2026-06-05
https://docs.voyageai.com/docs/pricing

Inferred values (marked with * in calculator tables)

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 →