Model AI costbefore you ship
Move from “How many tokens did we burn?” to “What will this architecture cost in production?”
Simulate real AI workloads — including execution depth, retry amplification, context growth, and burst traffic — before deployment.
A structural cost modeling tool for production planning — not a runtime monitoring dashboard.
Want to understand the thinking behind ModelIndex? Read the blog →
No signup · Free · Built for real production systems
From idea to decision
ModelIndex helps teams reason about AI choices the way they behave in production — not in benchmarks or demos.
1. Start with how the model will be used
Customer support, RAG search, internal tools — real workloads, not abstract tasks.
2. Model real production behavior
Cost ranges that account for retries, context growth, and burst traffic.
3. Compare tradeoffs across models
See where cost, reliability, and failure modes diverge as usage scales.
4. Decide before you ship
Choose a model knowing what breaks — and when — before surprise bills show up.
What does this look like in practice?
Before averages and benchmarks, it helps to ground cost expectations in a real production setup.
A quick reality check
Dominant driver: Execution depth
Estimated monthly cost
$4.6k – $9.8k
Directional range including retry amplification, context accumulation, and burst volatility.
Early estimates break first on depth amplification and retry loops.
Explore AI Agent →Canonical Production Patterns
Real AI workloads follow structural patterns. Each canonical scenario highlights the dominant driver that shapes cost behavior.
AI Agent
→Multi-step tool execution where cost scales with execution depth.
Dominant driver: Execution depth
Customer Support
→High-volume conversational workflows sensitive to request scale.
Dominant driver: Request volume
Internal Copilot
→Habit-driven internal knowledge assistance with longer prompts.
Dominant driver: Habit intensity
AI Search (RAG)
→Retrieval-heavy systems dominated by injected context size.
Dominant driver: Context size
Why teams use ModelIndex
ModelIndex is not a benchmark, leaderboard, or pricing page.
It’s a decision tool for reasoning about production AI cost and tradeoffs.
Opinionated by design
We don’t list everything. We focus on what actually works in production.
Cost-first thinking
We model how usage, retries, context, and spikes affect spend.
Built for decisions
Compare scenarios and tradeoffs — not raw benchmarks.
Make AI decisions with confidence
Start with a recommendation. Validate it with cost. Decide before you ship.