The honest answer to “what does AI development cost?” is that anyone quoting you a number before understanding your scope is guessing. Cost is driven by four factors: integration surface (how many systems the AI must touch), accuracy bar (a marketing chatbot and a clinical tool are different universes), scale (10 users vs 10,000 concurrent calls) and operational maturity (monitoring, evals, fallbacks). Here is how each one moves the number.
What actually moves the number
Integration surface is usually the biggest multiplier: a chatbot grounded in one document set is a fraction of the work of one wired into your CRM, billing and support desk, because every integration carries auth, error handling and edge cases. Accuracy bar comes next — a clinical or financial tool needs evaluation suites, audit logging and human escalation that a marketing bot does not. Scale changes the architecture rather than the feature list: 10,000 concurrent voice calls is a different system from 100, not the same system with a bigger server. Operational maturity is the one buyers forget, and the one that decides whether the build survives contact with real users.
This is why we publish no price list. We ask what budget you are working with, then tell you honestly what is achievable inside it — including when the answer is “not this, yet”. See what drives cost per service on the /services pages, each of which has its own cost breakdown.
The line items nobody quotes
Evaluation suites (how you know the AI is still behaving after every change), monitoring and alerting, rate limiting and abuse protection, and human-escalation paths. These are 15–25% of a serious budget, and their absence is why cheap builds die in production. When comparing quotes, ask specifically what happens when the model gives a wrong answer — the quality of that answer predicts the quality of the vendor.
Build vs buy
Off-the-shelf tools win when your use case is generic (basic FAQ bot, simple scheduler). Custom wins when the AI touches your proprietary data, your systems or your margins — a booking-optimized recommendation engine or a voice agent wired into your CRM is a moat, not a utility. The expensive mistake is paying custom prices for a generic need, or forcing a generic tool to do a custom job.
How we scope at Midalaxy
Fixed milestones, working software at each one: a scoped v1 in weeks (one call type, one data source), measured against agreed metrics, then expanded. You see latency, containment and cost numbers before committing to scale. That model is how the systems in /work shipped — 10,000+ concurrent calls, NHS-grade clinical AI, 132-endpoint SaaS.
Want a number for your specific project? A 20-minute scoping call gets you one: tell us your scope and budget at /contact and we will tell you what it takes to build it well — or start with /blog/ai-voice-agents-complete-guide and /blog/rag-vs-fine-tuning to sharpen the requirements first.