How to estimate AI coding costs before you build

Q: Can you predict AI coding costs accurately before building?

Not to a single number — pre-build predictability is effectively noise (r<0.15). You can bound it with a Low/Expected/High range and plan against the High band.

Q: What’s the biggest driver of AI build cost?

Context re-reading. Cache reads are over 97% of agentic token volume, so the bill tracks how many turns the agent takes and how large its context grows — not the amount of code produced.

Q: Should I budget for the Expected or the High band?

The High band. Token spend has 10×+ run-to-run variance; budgeting to the Expected figure leaves you exposed when a build drifts into retry loops.

Last reviewed May 28, 2026 · SoftwareEstimator.com

Estimate an AI build in four steps. First, size the scope — by feature count or a project-type preset. Second, convert it to a token range, not a single number: agentic builds swing 10× either side of the median. Third, apply the model’s per-token price, weighting cache reads, which are over 97% of the volume. Fourth, add per-turn overhead like connected MCP servers (~18k tokens each, every turn). Then budget against the High band, not the Expected one. The most useful discipline is refusing to commit to one figure — pre-build predictability is effectively noise (r<0.15), so a Low/Expected/High range is the only honest plan.

Run your own estimate →

The four steps

Turning a vague idea into a defensible budget:

→ 1. Size the scope — features, or a preset (landing / CRUD / SaaS / mobile / framework)
→ 2. Convert to a token range — Low / Expected / High, never one number
→ 3. Price it — per-million model rates, weighting cache reads (~0.1× input)
→ 4. Add overhead — ~18k tokens per turn per connected MCP server

What blows estimates up

The High band is where unoptimized builds live: debug loops that retry endlessly, verbose terminal output streamed back into context, and sessions that never reset. One documented build hit $30,983 in a month this way.

The opposite — a test-backed verify loop, clean sessions, an operator who steers — is how Cloudflare rebuilt most of Next.js for about $1,100. Same class of work, ~30× apart. Budget for the messy case and be pleasantly surprised.

Frequently asked questions

Can you predict AI coding costs accurately before building?

Not to a single number — pre-build predictability is effectively noise (r<0.15). You can bound it with a Low/Expected/High range and plan against the High band.

What’s the biggest driver of AI build cost?

Context re-reading. Cache reads are over 97% of agentic token volume, so the bill tracks how many turns the agent takes and how large its context grows — not the amount of code produced.

Should I budget for the Expected or the High band?

The High band. Token spend has 10×+ run-to-run variance; budgeting to the Expected figure leaves you exposed when a build drifts into retry loops.

Related guides

Figures are industry-composite estimates for planning, not quotes — agentic token spend has 10×+ run-to-run variance. See the full methodology or run an estimate .