AI Operating Cost Simulator

Simulate AI operating cost at scale.

Simplified inference-based simulation
using configurable assumptions.

1
Your use case
2
Choose a model
3
Your estimate
Organisation size & AI maturity

Select both to preset task volumes. Size determines user count, maturity determines workflow mix.

Organisation size
AI maturity — select all that apply
AI assists individuals
AI runs in processes
AI drives autonomously
Size AI users Assistive Workflow Agentic
1–201248/day20/day30/day
20–10045225/day80/day150/day
100–5002001 200/day300/day600/day
500+4503 150/day800/day1 500/day
AI users: estimated number of employees actively using AI, based on adoption rate by organisation size. Everyday tasks scale with AI users. Workflow and agentic volumes are system-level triggers, not per-user.
Token estimates are directional assumptions based on typical enterprise workflow patterns, representative prompt structures, and published token guidance from Anthropic, OpenAI and Google. They are designed for scenario exploration — not deterministic forecasting.
Select or adjust tasks & daily volume

Select which workflows to include and adjust volume per task. One run = one AI request.

Cost model
Total cost =
( Base model cost × Batch × Cache )
× ( Orchestration depth × Context growth × Retry )
× Volume
Green factors reduce cost. Amber factors amplify cost. Volume is runs/day × working days.
Token estimates are directional — based on typical enterprise usage patterns.
Cost is driven by:
· Prompt size and document/context volume · Number of API calls per workflow trigger · Workflow complexity and orchestration depth · Automated workflows generate significantly higher token usage than single AI requests
Everyday tasks — single API call per run
Task Input Output
Summarise a document~8k~600
Write a first draft~1.5k~1.8k
Draft an email or message~400~350
Ask a question~4k~500
Process information~3k~400
Translate content~3k~2.8k
Compare or evaluate options~5k~700
Analyse and categorise content~4k~800
Automated workflows — multiple API calls per trigger
Workflow API calls Total tokens
Simple workflow3–4 calls~18k
Complex workflow8–10 calls~80k
Developer tasks — single API call per run
Task Input Output
Code generation~5k~1.2k
SQL query~800~400
RAG Q&A~6k~700
API documentation~4k~2k
All input estimates include system prompt overhead (typically 200–800 tokens/call). Actual costs vary with prompt design, document length and retry frequency.
This simulator models inference cost only — the direct cost of API calls to language models. It does not include infrastructure costs (cloud hosting, vector databases, monitoring), implementation and integration effort, compliance and audit logging, multi-agent orchestration overhead, embedding generation, fine-tuning, guardrails and content filtering, or human-in-the-loop process costs.

Seat-based AI subscriptions (e.g. Copilot, ChatGPT Team, Gemini Workspace) may include bundled usage and are therefore not reflected in inference-based estimates. For larger organisations with complex AI deployments, total AI operating cost will typically exceed inference cost significantly.
Working days / month
Working days 22
122 (typical)31
Model
Model selection affects inference cost only. Output quality, latency and data residency vary by provider.
Optimisations
Batch API −50% input cost
Async batch processing — non-real-time tasks
Prompt caching off
How much of your input is static (system prompt, document base)? The higher the share, the more caching saves.
0% static
Drag right to model how much of your input context is reused across requests.
ℹ Optimisation strategy by AI type
AI type Batch value Cache value Primary strategy
Assistive Low High Cache-first
Workflow High Medium Batch-first
Agentic Medium High Hybrid
Sensitivity parameters below model the cost amplification effects that batch processing and prompt caching are designed to reduce.
Sensitivity parameters

Operational factors that increase AI workload and operating cost as workflows become more advanced.

These settings mainly affect workflow and agentic AI systems.

Retry rate 0%
How often workflows fail and need to run again. Every retry repeats the same AI work and increases operating cost.
Typical: Stable production systems: 2–10%. Above 20% often indicates workflow instability or poor orchestration.
Orchestration depth 1×
How many coordinated AI steps are involved in completing a task. More steps means the system repeatedly processes instructions, context and prior outputs.
Typical: Simple workflows: 1–1.5×. Multi-step workflows: 2–2.5×. Complex autonomous systems: 3×+.
Context growth 1×
How much information accumulates as workflows continue running. Longer workflows and agent chains require more context to be carried forward between steps, increasing cost over time.
Typical: Low accumulation: 1–1.5×. Document-heavy workflows: 2–3×. Long-running autonomous systems: 3–4×.
Compare two models

Side-by-side cost comparison of two models. Price-only scenario modelling. Opens OpenRouter for deeper benchmarking, including context window, latency, and capability data.

Model A
vs
Model B
Productivity impact estimate
What level of workflow efficiency improvement do you expect from AI adoption?
Active AI users 0 people
Avg. fully-loaded cost per person kr / yr
Estimated efficiency improvement 10%
5% — limited assistance20% — integrated into workflows40% — highly automated
Cost over time
Projected monthly AI operating cost as usage and workflow complexity grow.
Expected monthly growth
Scenario comparison

Configure a scenario, save it as A. Reconfigure and save as B. The delta shows the impact of the change.

calc.buchemeier.no · AI Operating Cost Simulator
⏳ Loading prices and rates…
Monthly cost estimate
kr 0
Model Claude Sonnet 4.6
Active AI users —
Everyday tasks / day —
Simple workflows / day —
Complex workflows / day —
Developer tasks / day —
Total runs / day —
Working days 22
Batch off
Cache off
Retry rate —
Orchestration depth —
Context growth —