◢ 01 · Prompt drift
Evals, regression tests, A/B
Every model update re-rolls your prompts. Teams without eval pipelines ship regressions to prod on Tuesday and roll back on Thursday — twice a quarter, every quarter.
6–12% of AI TCO Free · No sign-up · Based on public model pricing
The sticker price is only 40% of what AI will cost you.
Per-token rates are the easy part. Add infra, dev hours, vector DBs, vendor lock-in, monitoring, and human review — and the real TCO is usually 2.5× the API bill. Model it below in 60 seconds.
12 models 5 cost buckets Live math
Uncover hidden AI costs
Why calculate the true cost of AI?
- Discover 12 cost categories most teams miss — training, monitoring, compliance & more
- Compare cloud vs on-premise vs hybrid hosting models
- Get a 3-year cost projection to budget realistically
AI TCO simulator Live estimate updates as you type
1 Use case pre-fills token mix
2 Volume monthly
/ mo
tok
tok
3 Model USD per 1M tokens
/ 1M
/ 1M
4 Hidden layers usually forgotten
◢ True monthly cost LIVE
$—/ month
Annualized: — · API-only: — · Hidden: +0%
API tokens—
Infra + vector DB—
Engineering—
Ops + review—
Vendor / observability—
API Input + output tokens — —
INF Infra + vector DB — —
DEV Engineering time — —
OPS Human review + fallback — —
VEN Observability + guardrails —
True monthly TCO —
Cost per query —¢ —
Cost per 1k tok — blended
Tokens / month —M in + out
Model sanity check
Same use case, twelve different bills.
Your inputs, cross-tabulated against every model we support. The cheapest option isn't always the right one — but "right" shouldn't be off by 50×.
Monthly cost by model — API only, using customer chatbot
◢ Prices refreshed Q1 2026 · excludes volume tiers| Model | Vendor | In / 1M | Out / 1M | Cost / query | Monthly |
|---|
Break-even analysis
AI vs. the team you'd otherwise hire.
Replacing a workflow isn't about the subscription cost — it's about the fully-loaded human alternative, including benefits, tooling, and management overhead.
Would humans be cheaper at this volume?
Adjust the human baseline; we'll break down the cost-per-interaction both ways.
USD
Your simulated AI stack
$—/ month
Cost per query —
Throughput ceiling ~unlimited
Latency seconds
Quality variance ±15%
Fully-loaded human equivalent
$—/ month
Cost per query —
Agents required —
Latency ~minutes
Quality variance ±5%
AI wins — at this volume, monthly savings:
—
Where budgets leak
Six buckets nobody budgets for — until the invoice lands.
◢ 02 · Context engineering
Vector DBs, embeddings, reranking
RAG isn't "upload PDF, done". Chunking strategy, hybrid retrieval, reranker costs, re-embedding on updates — this stack is typically 25–40% of infra spend.
25–40% of infra spend◢ 03 · Vendor lock-in
Portability tax
Model-specific fine-tuning, function-calling schemas, cached prompts — all non-portable. Switching vendors later costs 3–6 weeks of engineering per non-trivial integration.
3–6 weeks switch cost◢ 04 · Safety + compliance
Moderation, PII, auditability
GDPR, DORA, the EU AI Act. Logs, redaction, jailbreak-resistant system prompts, and a classifier pass on inputs and outputs. Not optional in regulated sectors.
8–15% of AI TCO◢ 05 · Human review
HITL for the long tail
Even at 95% autonomous, the 5% you escalate demands an ops team, SLAs, and an escalation UI. Scales linearly with volume, not with compute.
~$0.40 per reviewed query◢ 06 · Opportunity + idle cost
GPU reservations, wasted calls
Self-hosting? Reserved GPU hours burn 24/7 even when traffic dips. Using APIs? Failed retries, dropped streams, and timed-out agent loops quietly rack up 8–18% token waste.
8–18% token overrun Methodology
Where the numbers come from.
We don't invent multipliers. Every assumption is sourced from a public price list or peer-reviewed benchmark — linked below.
◢ Token pricing
Vendor price pages
Input / output per-1M-token rates are pulled from OpenAI, Anthropic, Google DeepMind and Mistral price pages, refreshed quarterly. We model blended input-heavy workloads separately from generation-heavy ones.
Refreshed: Q1 2026◢ Hidden multipliers
a16z LLMOps survey
Andreessen Horowitz's 2024 LLMOps survey across 40+ companies found infra+ops+dev roughly doubles the raw API bill. Our default multipliers sit at the median of the reported range.
Source: a16z LLMOps field notes, 2024◢ Retrieval stack
Pinecone + pgvector benchmarks
For RAG use cases, vector DB + embedding cost is modeled against Pinecone Serverless and self-hosted pgvector on RDS m5.xlarge. We assume 1M indexed chunks with nightly delta updates.
Source: Pinecone pricing, AWS RDS listGet Your AI Cost Report
Complete TCO breakdown with year-by-year projections, hidden cost analysis, and budget template.
Includes CFO-ready executive summary with risk flags
How the AI integration TCO calculator works
1
Select AI components
Choose the AI services and models you plan to integrate.
2
Configure scale & usage
Set expected request volumes, data sizes, and processing frequency.
3
See total cost
Get full TCO breakdown: compute, storage, API calls, team, and hidden costs.
FAQ
Honest questions, honest answers.
Why is the "true" cost usually 2–3× the API bill?
Because the API bill is the floor, not the ceiling. You're also paying for: a vector DB (RAG), observability (Langfuse/Helicone), a moderation classifier pass, a senior engineer maintaining prompts and evals, and ops people handling the long tail. In our field data, the median ratio of hidden-to-API cost is 1.5×, meaning total ≈ 2.5× what the vendor quote said.
Does this cover fine-tuning and custom training?
Toggle the "Fine-tuning" option in Step 4. We amortize a single training run across 12 months at a mid-range LoRA cost (~$6k one-off). For full pre-training you're in another budget category entirely — book a call.
What about caching and prompt compression?
Anthropic's prompt caching and OpenAI's batch API both cut input costs 50–80% for cache-friendly workloads. The calculator doesn't apply this automatically — if your traffic is highly repetitive, shave the input price manually and you'll see the delta live. A good rule of thumb: cache covers 30–60% of input tokens for RAG workloads.
Why don't you show Azure / Bedrock / Vertex pricing?
For the same underlying model, Azure / Bedrock / Vertex rates are within ±5% of the direct vendor rate for on-demand usage. Enterprise agreements can shift this meaningfully — use the "Custom" model row and plug in your negotiated rate.
Is the human-comparison realistic?
It's a rough comparator. A real labor model should add benefits, onboarding, attrition, and management layer — we use a 1.3× loading factor by default, which lands in the published SHRM range for knowledge work. Reality will vary by country.
Can I export or share this estimate?
Hit "Copy summary" — it puts a plain-text cost breakdown on your clipboard. Your inputs also persist to local storage, so you can come back tomorrow and tweak.
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Ready to ship AI that pays for itself?
You modeled the cost. We build the feature in 6 weeks.
Fixed price, fixed scope. Model selection, RAG pipeline, evals, monitoring — production-ready, not a prototype.