unitcostai

AI Coding Agent Cost

What does one AI coding workflow cost per active developer each month?

How this tool works

This tool models the full workflow cost behind an AI coding agent or coding assistant: generation, code retrieval, reranking, embedding refresh, vector lookups, cache savings, and infrastructure overhead with a daily pricing snapshot. Use it once you know the coding workflow you want to operate or price and need the cost per active developer behind that workflow.

Use this when

  • You already know the coding workflow shape and need cost per active developer.
  • You want workflow economics, including repository context, reranking, cache, and infra.
  • You need to compare modeled workflow cost with an internal chargeback or resale seat price.

How It Works

  1. Pick the coding model and set the monthly request volume per active developer.
  2. Model repository context, reranking, vector lookups, and fixed embedding refresh as explicit cost blocks.
  3. Compare cost per developer, break-even internal price, and optional margin before you widen rollout or change routing.

Formulas

embedding_ingestion_share = embedding_ingestion_monthly / monthly_active_users

cost_per_developer_month = generation + retrieval + reranking + embedding_ingestion_share + vector_db + cache + infra

optional_margin_pct = (seat_price_or_chargeback - cost_per_developer_month) / seat_price_or_chargeback * 100

Assumptions and Units

  • Currency: USD
  • Token unit: token
  • Cache component is negative when savings reduce total cost
  • Embedding refresh is amortized across active developers before it appears in per-developer totals
  • Pricing source: daily pricing snapshot in repo, no runtime scraping

Example Scenario

Use this for a repo-aware internal coding assistant or any coding workflow where one premium coding turn can be cheap in isolation but expensive after repeated follow-ups and repository context are included.

FAQs

What should I change first if margin looks weak? Usually request volume, prompt/context size, and premium-model share move faster than small list-price differences.

Is this the right page for Claude Code, Codex, Cursor, or Copilot plan comparison? No. Use AI Coding Plan Comparison for broad fixed-plan decisions, or Codex vs Claude Cost for that direct head-to-head. This page is for workflow-side cost per active developer.

Related resources: AI Coding Plan Comparison, Codex vs Claude Cost, PR Review Cost, PR Review Cost, Fixed Plan vs API Pricing for Coding Tools, AI Coding Agent Cost per Developer, Internal AI Assistant Cost per Employee, Model Selection: Quality vs Unit Cost, How Model Switching Changes Margin, How Many Tokens Per Request?, RAG Cost Components Explained.

Pricing snapshot: 2026-05-27Provider: OpenAIModel: GPT-5.3 Codex

Decision Signal

Healthy

Current workflow margin is 95.1%. Use this to compare modeled workflow cost with your current seat price or chargeback assumption.

Step 1 Provider and Model

Switch model assumptions using prices from the selected snapshot.

Step 2 Quick Mode

Use plain-language assumptions first. Open Advanced assumptions only if needed.
Starting pointApply the generic baseline or a more conservative downside scenario. If neither is selected, you are working from custom inputs.
Quick workflowStart with the closest workflow shape, then fine-tune the assumptions below.

Repo-aware coding workflows where active developer usage, context load, and model routing move unit cost fast.

Optional Advanced assumptions

Tune retrieval, reranking, embeddings, vector, caching, and infra.
Show advanced inputs

Only adjust these once your Quick Mode assumptions feel realistic.

Scenario actions

Copy scenario URL

Paste into ChatGPT or Claude, or share with a teammate.

Save and track this scenario

Track pricing drift on this scenario and get an email if the latest result changes.

How tracking works

After you click Save and track, we carry this exact calculator state into the tracked-scenarios page so you can sign in and confirm the save.

We save your assumptions and the pricing snapshot used for this result.

When a newer pricing snapshot lands, we recompute the same scenario, show what changed, and email you if the latest result moved.

1 tracked scenario free, then $12/mo or $120/yr for up to 25 tracked scenarios.

Cost per developer/month

$2.3954

Gross margin

95.1%

Estimated monthly AI cost

$526.98

Estimated monthly gross profit

$10,253.02

Top Cost Drivers

Most sensitive variables when each is moved up by 10%.
Requests Per User Month10.0%
Rerank Docs6.4%
Cache Hit Rate-2.8%

Totals

Summary metrics for monthly unit economics and margin.
Cost per request
$0.02178
Cost per developer/month
$2.3954
Gross margin %
95.1%
Break-even price
$2.3954
Cost per request$0.02178
Cost per developer/month$2.3954
Gross margin %95.1%
Break-even price$2.3954

Component Breakdown (USD/developer/month)

Each cost component is computed independently and summed.

Largest cost block: reranking, not generation.

GenerationModel input/output token spend for requests.
$0.7854
RetrievalExtra model input spend from retrieved context chunks.
$0.2541
RerankingReranker cost based on docs scored per request.
$1.98
Embeddings IngestionAmortized per-user share of the fixed monthly corpus embedding refresh cost.
$0
Vector DbVector database query cost across all requests.
$0.002
CacheSavings from cache hits. Negative means lower total cost.
$-0.6756
InfraNon-model infra overhead per request.
$0.0495
GenerationModel input/output token spend for requests.$0.7854
RetrievalExtra model input spend from retrieved context chunks.$0.2541
RerankingReranker cost based on docs scored per request.$1.98
Embeddings IngestionAmortized per-user share of the fixed monthly corpus embedding refresh cost.$0
Vector DbVector database query cost across all requests.$0.002
CacheSavings from cache hits. Negative means lower total cost.$-0.6756
InfraNon-model infra overhead per request.$0.0495
Sensitivity RankingChange in total cost when one variable is increased by 10%.
VariableDelta cost %
Requests Per User MonthUser activity level per month.10.0%
Rerank DocsDocs reranked per request.6.4%
Cache Hit RateFraction of requests served by cache.-2.8%
Output TokensGenerated tokens per request.1.8%
Retrieved ChunksRetrieved chunk count per request.0.8%
Tokens Per ChunkAverage chunk size in tokens.0.8%
Input TokensPrompt-side tokens per request.0.8%
Vector Queries Per RequestVector query count per request.0.0%
Monthly Active UsersActive-user estimate used to amortize fixed monthly embedding refresh.-0.0%

Assumptions and Units

Explicit assumptions to keep outputs reproducible and auditable.
  • CurrencyUSD
  • Token unittoken
  • Pricing snapshot2026-05-27
  • Selected model rowOpenAI/GPT-5.3 Codex
  • Volume basisBusiness totals and fixed monthly terms use monthly active developers as the denominator
  • Embedding refreshAmortized per developer from the fixed monthly code/repo refresh term
  • Cache componentNegative value means cost savings

Recommended Next Step

Use these links to lower top cost drivers without guessing.

Optimize the biggest modeled cost driver first. Compare infra only after model, retrieval, reranking, or context changes stop being the better lever.

Choose the right comparison next

PR Review CostAI Coding Plan ComparisonCompare Model CostsFixed Plan vs API Pricing for Coding Tools

Compare infra providers

View Infra Recommendations

Sources and Snapshot

Pricing comes from the current dated snapshot.

Active Pricing Row

Selected model

OpenAI / GPT-5.3 Codex

  • Input tokens$1.75 / 1M
  • Output tokens$14 / 1M

Shared retrieval defaults

  • Embedding input$0.02 / 1M
  • Rerank docs$1 / 1K

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