Dev Tools

AI Monthly Budget Calculator

Set a monthly budget, see what usage that buys across LLMs, image gen, and audio.

Quick Answer

$100/month buys: ~10K Sonnet calls, ~28K Haiku, ~225K GPT-4o-mini calls (1500 in + 500 out each). Or 1250 DALL-E HD images, 33K Flux Schnell images, 280 hours of Whisper. Use mixed allocation: cheap models for volume, flagships for hard cases.

Monthly AI budget ($)

LLM calls (1500 input + 500 output tokens each)

Model	Cost / call	Calls / mo
GPT-4o calls	$0.00875	57,142
GPT-4o-mini calls	$0.00052	952,380
Claude Opus 4.7 calls	$0.06000	8,333
Claude Sonnet 4.6 calls	$0.01200	41,666
Claude Haiku 4.5 calls	$0.00400	125,000
Gemini 2.5 Pro calls	$0.00438	114,285
Gemini 2.5 Flash calls	$0.00170	294,117

Media generation

Service	Unit cost	Units / mo
DALL-E 3 Standard images	$0.040	12,500
DALL-E 3 HD images	$0.080	6,250
Flux Schnell images	$0.003	166,666
Whisper minutes	$0.006	83,333
ElevenLabs Creator (chars / 1000)	$0.300	1,666
OpenAI TTS-1 (chars / 1000)	$0.015	33,333

About This Tool

The AI Monthly Budget Calculator goes the other direction from typical cost calculators. Instead of asking “how much will this usage cost?” it asks “what usage does this budget buy?” Set your monthly cap and the tool shows how many LLM calls, images, audio minutes, or TTS characters that money buys across every major provider.

How to use this for budget planning

Start with a target monthly spend. Plug it in. The tool shows the maximum usage in each category. Compare against your expected volume — if you plan 50K calls/month and the budget only buys 30K on Sonnet, you have three options: increase the budget, downgrade the model (Haiku at $1/$5 instead of Sonnet at $3/$15), or compress prompts to reduce per-call cost.

Standard call assumptions

The LLM section assumes 1500 input + 500 output tokens per call — typical for a RAG-style application with retrieved context plus a brief response. If your calls run longer (long documents, agentic workflows), divide the “calls per month” number proportionally. A 5000-input + 1500-output call costs roughly 3.5x more, so divide the budget capacity by 3.5x.

Mixed allocation strategies

Most production AI products run a mixed model strategy. Use cheap models (Haiku, Flash, mini) for high-volume routing, classification, and first-pass generation. Reserve flagships (Opus, GPT-4o, Gemini Pro) for the 5-10% of cases that need deep reasoning. A typical mix saves 60-80% versus running everything on the flagship.

Hard caps and runaway prevention

Set hard usage limits on every provider dashboard. OpenAI lets you cap monthly spend per project. Anthropic supports tier caps. Google has quota controls. Add app-level rate limiting per user to prevent compromised keys or runaway agents from blowing through the budget in a single bad weekend. Most large surprise bills come from buggy retry loops, not honest user growth.

Per-user economics

Translate budget into a per-user cost as soon as possible. If your AI feature serves 10K monthly active users on a $500 budget, that's $0.05 per user. Compare against your unit economics. Below $0.50/user is healthy for most B2C apps. AI-native products often run $5-$50/user — make sure pricing supports that.

Drill down with LLM cost comparison, GPT cost calculator, Claude cost calculator, image generation cost, and Whisper cost calculator. For ROI math see AI ROI calculator.

Frequently Asked Questions

How do I right-size my AI budget?

Start with current usage × cost. Add 30% buffer for retries and growth. Track weekly so you can adjust before overages hit. For new products, prototype on Haiku/Flash/mini, measure actual usage on 100 real requests, then project monthly.

What does $100/month of AI buy?

On Sonnet 4.6 with typical RAG (1500 in + 500 out): ~10K calls. On Haiku: ~28K calls. On GPT-4o-mini: ~225K calls. On DALL-E HD: 1250 images. On Whisper: 280 hours of audio. Mix and match across budget categories.

Should I set hard or soft budget caps?

Both. OpenAI, Anthropic, and Google all support hard usage limits — set them. Add app-level rate limiting per user too. The worst AI bills come from runaway agents or compromised API keys hitting in a single weekend.

How do I avoid surprise bills?

Set hard caps on the provider dashboard. Monitor usage daily for the first month after launch. Build alerts at 50%, 80%, and 100% of monthly budget. Cap max_tokens aggressively — runaway output is the most common over-spend source.

What's a typical AI spend for a product?

B2B SaaS adding AI features: $500-$5K/month at early stage. Consumer apps with AI: $0.10-$0.50 cost per active user per month at scale. AI-native products: $5-$50/user. Map your number into a per-user metric early.