The question every executive asks first and every engineer hates: 'How much does AI cost?' The honest answer is 'it depends' — but that answer is useless for budgeting. After building 40+ production AI systems, we can give you concrete ranges, the factors that move you between them, and the budgeting mistakes that cause projects to fail.
The Cost Ranges
AI development costs fall into four tiers: Proof of Concept ($30K-$80K) — a working demo that validates the technical approach. Typically 4-8 weeks, one engineer, no production infrastructure. Minimum Viable Product ($80K-$200K) — a deployable system serving a limited user base. Includes data pipelines, model serving, basic monitoring, and a simple UI. Timeline: 8-16 weeks, team of 2-3. Production System ($200K-$500K) — a scalable, monitored system with proper infrastructure, testing, and operational tooling. Includes fallback rules, canary deployments, and business KPI tracking. Timeline: 16-32 weeks, team of 3-5. Enterprise Platform ($500K-$2M+) — a multi-tenant, compliant platform with advanced features like real-time personalisation, multi-model routing, and enterprise security. Timeline: 6-18 months, dedicated team.
What Drives the Cost
Five factors dominate AI development costs: (1) Data quality — cleaning, labelling, and engineering data typically consumes 40-60% of total project effort. If your data is clean and well-structured, costs drop dramatically. (2) Integration complexity — connecting to existing systems, APIs, databases, and workflows. A standalone AI tool is cheaper than one integrated into a complex enterprise stack. (3) Latency requirements — real-time inference is more expensive than batch processing. Sub-100ms latency requires infrastructure optimisation that adds 30-50% to serving costs. (4) Compliance requirements — HIPAA, SOC 2, GDPR add 20-40% to development costs due to additional security, audit, and documentation requirements. (5) Accuracy requirements — 90% accuracy is much cheaper than 99% accuracy. Each additional percentage point requires exponentially more data, testing, and fine-tuning.
Warning
The biggest budgeting mistake: underestimating data costs. Data preparation is not a preliminary step — it is the majority of the work.
Ongoing Costs People Forget
Development is a one-time cost. Operating an AI system is recurring: model inference ($500-$5,000/month for moderate traffic), infrastructure ($200-$2,000/month for compute and storage), monitoring and observability ($100-$500/month), model retraining (quarterly, $5K-$20K per cycle), and incident response (budget 10-20% of development cost annually). Total first-year cost is typically 1.5-2x the development cost. This is why the 'build vs buy' decision matters — a SaaS AI tool at $500/month may be cheaper than building a custom system that costs $200K to build and $50K/year to operate.
How to Budget Accurately
Start with a discovery phase ($5K-$15K, 2-4 weeks) that produces a detailed technical specification and cost estimate. This small investment prevents the $100K mistake of under-scoping. Then budget in phases: discovery, MVP, production hardening. Do not commit to the full project budget upfront — AI projects have genuine uncertainty, and the architecture may change based on what you learn during development. Use a time-and-materials model rather than fixed-price for AI work. Fixed-price contracts incentivise the wrong behaviour: cutting corners on data quality and testing to hit a deadline.
Conclusion
AI development is not cheap, but it is predictable if you budget correctly. The key is understanding that data preparation dominates costs, ongoing operations are recurring, and the biggest risk is under-scoping, not over-spending. Budget for discovery first, build in phases, and always include operational costs in your TCO calculation.
Key Takeaways
- AI development ranges from $30K (POC) to $2M+ (enterprise platform)
- Data quality and preparation consume 40-60% of total project effort
- Ongoing costs (inference, infrastructure, retraining) are 1.5-2x development cost in year one
- Budget for a discovery phase first — it prevents the $100K under-scoping mistake
- Use time-and-materials, not fixed-price, for AI work with genuine uncertainty