Quick Summary
- 1PoC: $8K–$25K to validate one workflow on real data in 4–6 weeks.
- 2Production app: $40K–$120K — most teams underestimate evals and observability.
- 3Token + infra spend: $500–$20K/month depending on traffic and model mix.
- 4Hidden cost #1: red-teaming, PII redaction, audit logs and rate limiting.
Every founder asks the same question: "how much for an AI feature like ChatGPT?" The honest answer requires breaking the cost into engineering, ongoing token spend, and operational overhead. Here's how we scope AI development projects in 2026.
Phase 1 — Proof of Concept ($8K–$25K, 4–6 weeks)
One workflow, on real customer data, with an evaluation set. The goal is a yes/no on whether the use case actually works — not a launch. Most PoCs are a RAG bot, a document AI extractor, or a copilot embedded in your product.
Phase 2 — Production App ($40K–$120K, 10–16 weeks)
This is where budgets blow up. A production AI app needs:
- Auth, RBAC and tenant isolation
- Input/output guardrails and prompt injection defences
- PII redaction, audit logs, and data retention policies
- An evaluation harness running on every prompt change
- Observability — Langfuse, OpenTelemetry, cost dashboards
- Caching, rate limiting and fallback model routing
- Human-in-the-loop review for high-stakes outputs
Skip any of these and you'll either leak data, blow the bill, or ship something that hallucinates in front of customers.
AI App Cost Breakdown (2026)
| Website Type | Price Range | Best For |
|---|---|---|
| PoC / Demo (4–6 weeks) | $8K – $25K | Validate the use case with one workflow on real data. |
| Production AI App (10–16 weeks) | $40K – $120K | Auth, guardrails, evals, observability, and an SLA. |
| Enterprise AI Platform | $150K+ | Multi-use-case platform, fine-tuning, on-prem/VPC deploy. |
| Ongoing token & infra spend | $500 – $20K / month | OpenAI / Anthropic / Bedrock + vector DB + monitoring. |
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Phase 3 — Ongoing operational spend
Token costs depend wildly on traffic and model mix. A typical mid-market AI feature lands at:
- $500–$2K/month for an internal copilot with 50 active users
- $3K–$8K/month for a customer-facing RAG product at 5K MAU
- $10K–$20K+/month for high-volume document AI or contact-centre deflection
Add $200–$1.5K/month for vector DB (Pinecone / Qdrant) and $300–$1K for monitoring.
Hidden costs nobody quotes
- Eval set creation: 40–80 hours of SME time to build a "golden" test set.
- Red-teaming: 2–4 weeks of adversarial testing before a public launch.
- Model migrations: every new GPT/Claude release requires re-tuning prompts and re-running evals.
- Compliance: SOC2, HIPAA, or EU AI Act review can add 4–8 weeks for regulated industries.
How to control the cost
- Start with the cheapest model that passes your eval — most apps don't need GPT-4 class.
- Cache aggressively. Semantic cache hits cost ~$0.
- Route by complexity — cheap model for triage, expensive model only when needed.
- Fine-tune or distil once volume justifies it.
contact us for a free AI cost-modelling call on your specific use case.
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