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Stop overpaying for
AI you don't need

Describe what you're building. ModelSense recommends the right model for every task — with reasoning, cost estimates at your scale, and projected savings vs running GPT-4o for everything.

ModelSense recommendation engine
📝
Your project description
ModelSense
AI-powered
🎯
Optimal model stack
+ cost estimate
UI / Frontend GPT-4o mini
Business logic Claude Sonnet
Est. savings ↓ 64%
How it works

Three steps to your perfect AI stack

No guesswork. No benchmarks spreadsheets. Just paste your idea and get a tailored recommendation in seconds.

01
📋
Describe your project

Tell ModelSense what you're building — your stack, the features, the scale. The more detail, the sharper the recommendation.

02
🧠
AI matches models to tasks

Our engine breaks your project into subtasks and maps each one to the most cost-effective model that can handle it — not just the most popular one.

03
🎯
Get your full recommendation

Each task gets a model pick with reasoning, per-token pricing, and monthly cost estimates at your scale — plus your projected savings vs running GPT-4o for everything.

Sample output

Real recommendations, not generic advice

🔍
Task-level model matching

Your project is broken into distinct AI tasks. Each task gets the most cost-effective model that can handle it — heavy reasoning tasks get a capable model; simple completions get something cheap and fast.

💬
Reasoning for every pick

Each recommendation comes with a plain-English explanation — why that model, what tradeoffs were made, and what you'd lose by going cheaper or spend by going premium.

💸
Cost estimates at your scale

Monthly totals at light, medium, and heavy request volumes — plus your projected savings vs using GPT-4o for every task. No spreadsheet required.

ModelSense · recommendation output
Your project: "A SaaS app where users upload PDFs, ask questions, and get AI-generated summaries. ~500 DAU at launch, real-time responses."
✦ Recommended stack
MODERATE · 4 tasks
GPT-4o mini Chat UI responses
~$4/mo
Claude 3.5 Haiku PDF parsing + search
~$2/mo
Claude 3.7 Sonnet Complex summaries
~$11/mo

Best-in-class long-document reasoning at ~60% the cost of GPT-4o; handles multi-page contract context cleanly.

text-embedding-3-small Embeddings / RAG
~$1/mo
Estimated total / month
vs $62/mo using GPT-4o everywhere
~$18/mo
↓ 71% savings
Examples

Works for any AI-powered product

Sign up and run one of these through ModelSense yourself.

Who it's for

Built for builders at every level

Whether you're clicking buttons in a no-code tool or architecting a distributed system, ModelSense saves you money.

🎨
Vibe coders & no-code builders

You're building fast and don't want to read API pricing pages. ModelSense does the math and tells you exactly what to plug in.

Zero expertise needed
⚙️
Low-code developers

You know the tools but aren't sure which model is overkill for which task. Get confident picks with cost transparency.

Save hours of research
🧑‍💻
Experienced engineers

You know the landscape but want a second opinion backed by data — especially when cost optimization starts mattering at scale.

Data-backed decisions
Get better results

What to include in your description

The more context you give, the more accurate the model and cost recommendations.

8 questions to answer before you write
1

What type of application are you building?

Chatbot, document processor, code assistant, search, agent, etc.

2

What are the distinct AI tasks?

List each one — extraction, classification, generation, embeddings, summarisation, etc.

3

What is your expected usage volume?

Requests/day, monthly active users, documents/week.

4

What are your latency requirements?

Real-time user-facing (<2s) vs background batch processing.

5

What is your budget?

Monthly ceiling or per-request target.

6

Do you have any special requirements?

Multimodal input, long documents, tool calling, GDPR/data residency, open-source only, fine-tuning.

7

What quality bar do you need?

Prototype / internal tool / production / enterprise or regulated industry.

8

Single provider or multi-model?

Open to using different models per task, or need everything from one vendor.

Example structure

I'm building [type of app] for [target users].

AI tasks I need:
- [Task 1: e.g. document summarisation] — [~X req/day], [real-time / batch]
- [Task 2: e.g. semantic search] — [~Y req/day], [low latency needed]
- [Task 3: e.g. user-facing chat] — [~Z req/day], [<2s response]

Scale at launch: ~[X] daily active users, ~[Y] requests/day
Quality bar: [prototype / production / regulated / enterprise]
Budget: $[X]/month ceiling
Tech stack: [language, framework, hosting]
Constraints: [open-source only / GDPR / single provider / none]

Simple, transparent pricing

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Free
$0
Everything you need to evaluate models and costs for your project.
1 recommended model per task with reasoning
Medium-scale cost estimate
Per-1K token pricing (input & output)
Savings vs GPT-4o everywhere
API vs subscription breakeven note
All 5 providers (OpenAI, Anthropic, Google, Mistral, DeepSeek)
Up to 3 projects · 3 revisions each
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