How to Estimate AI Coding Costs Before Starting a Project: Step-by-Step Framework

Why Estimate Before You Start

AI coding costs can surprise teams who dive in without a budget. A project that seems like it will cost $200/month in AI tokens might actually cost $2,000 once you account for iteration, debugging, code review, and retries. This framework gives you a structured way to estimate costs before writing the first line of code, so you can choose the right model tier and set realistic budgets.

Step 1: Classify Your Tasks

Different task types consume vastly different token volumes. Start by listing the types of work your project involves:

For a typical web application project: expect 60% greenfield features, 15% bug fixes, 10% refactoring, 10% tests, and 5% reviews during the initial build phase.

Step 2: Select Your Model Tier

Match model capability to task complexity. Not every task needs the most expensive model:

A smart routing strategy uses premium models for 10-20% of tasks and economy models for 30-40%, with mid-tier handling the bulk. This alone can cut costs by 40-60% compared to using one model for everything.

Step 3: Estimate Monthly Task Volume

Count how many of each task type you expect per month. For a solo developer building a medium-complexity web app:

• Greenfield features: 15-25 per month (early phase) or 5-10 (maintenance)
• Bug fixes: 10-20 per month
• Refactors: 3-5 per month
• Code reviews: 20-40 per month
• Test generation: 10-15 per month

Multiply task count by average tokens per task type to get total monthly token volume. Example: 20 greenfield features x 120K avg = 2.4M tokens for features alone.

Step 4: Apply the Cost Formula

The core formula for monthly AI coding cost:

Monthly Cost = (Tasks x Avg_Tokens x Price_Per_Token) x (1 - Cache_Savings) x Retry_Buffer

Where:

• Cache_Savings: Prompt caching typically saves 10-25% on input tokens (system prompts, repeated context)
• Retry_Buffer: 1.2-1.4x (20-40% overhead for failed attempts, corrections, and iterations)

Worked example for a solo developer building a SaaS product, using Claude Sonnet 4.6 for features and DeepSeek V4 for routine work:

Apply adjustments: $38.48 x (1 - 0.15 cache savings) x 1.3 retry buffer = $42.53/month. That is the realistic budget for this developer.

Step 5: Factor in Routing and Caching Savings

Two techniques can significantly reduce your estimated budget:

• Model routing: Automatically directing simple tasks to cheap models. Effective routing reduces average cost per token by 40-60%. If your initial estimate assumes one model for everything, divide by 1.5-2x after implementing routing.
• Prompt caching: Caching system prompts and repeated context saves 10-25% on input costs. Most impactful for long sessions with stable system prompts.
• Context management: Compacting or offloading old context reduces input tokens on long sessions by 50-60%.

Combined, these optimizations can reduce your raw estimate by 50-70%. But start with the unoptimized number as your ceiling, then work down.

Quick Reference: Budget by Project Type

Based on the framework above, here are typical monthly budgets for common project types (solo developer, routed model strategy):

These estimates assume active development phases. Maintenance phases typically cost 20-30% of initial build phase budgets. Use the AI Cost Estimator to get a personalized estimate for your specific project parameters and model preferences.