"How much does AI development cost?" is the first question every founder asks and the last one any agency answers honestly. The reason is structural: AI development is not one product. It's three different products that share a buzzword. Pricing them as if they're the same is how budgets get destroyed.
This guide breaks AI work into three tiers with real 2026 numbers, real timelines, and real maintenance costs. If you're scoping an AI initiative, by the end you'll know which tier you're actually in and what the next twelve months will cost.
What "AI development" actually means in 2026
In 2025, anything touching an LLM API call was called "AI development." That conflation is dead. In 2026, three tiers are emerging with very different cost profiles:
- AI Integration — bolting a model call into an existing product (chatbot widget, copy generation, classification, summarisation). $5k–$15k. 2–4 weeks.
- Custom AI MVP — building a new product where the AI is the core value (RAG knowledge assistant, agent workflow, AI-augmented internal tool). $20k–$80k. 6–12 weeks.
- Production AI Platform — multi-tenant, fine-tuned, observability-instrumented, SOC-2-aware. $80k–$500k+. 4–12 months.
The mistake we see most often: a founder hires for tier 1, expects tier 3 output, and gets neither.
Tier 1: AI Integration ($5k–$15k, 2–4 weeks)
You have an existing Next.js / Rails / Django app. You want a feature that does one of: chatbot answers about your knowledge base, summarises long inputs, generates marketing copy, classifies tickets, transcribes audio.
What's included at this price:
- One model integration (typically GPT-4o-mini or Claude Haiku for cost reasons)
- Single prompt template with evaluation against ~20 hand-curated examples
- Production deployment with rate limiting and basic error handling
- Token usage dashboard so you know what you're spending
- 2–4 weeks of post-launch tuning
What's not included:
- Custom fine-tuning
- Multiple model fallbacks
- Vector search / RAG (that's tier 2)
- A complete observability stack (LangSmith, Helicone, custom dashboards)
- Agentic workflows or tool use
A real example: a SaaS customer-support widget that classifies incoming tickets, drafts a suggested reply, and routes urgent ones to humans. Two weeks of work, ~$8k, model cost about $40/month at 5k tickets/month. Support deflection in the first month was 30–40%, which paid back the build in 90 days.
Where this tier breaks down: the moment you say "we want it to do X and Y and maintain a long conversation and search our docs" you've moved into tier 2. Budget triples. Be honest with yourself about scope before you hire.
Tier 2: Custom AI MVP ($20k–$80k, 6–12 weeks)
You're building a product where AI is the core value, not a feature. The most common shapes:
- RAG knowledge assistant for an internal team's documentation (Notion, Confluence, Slack)
- Domain-specific agent that performs a multi-step task (lead enrichment, calendar scheduling, ops queries)
- AI-augmented internal tool (CRM with auto-summary, project tool with auto-prioritisation)
- Vertical AI app (HR resume screening, legal contract review, healthcare triage front-end)
What's included at $20k–$80k:
- Two or three model integrations (you'll need a cheap-and-fast model for high-volume calls and a smart-and-expensive one for hard cases)
- Embeddings pipeline + vector DB (Pinecone, pgvector, or Weaviate)
- Retrieval tuning and evaluation harness
- Conversation memory / state handling
- Production deployment on Vercel + your DB of choice
- Custom UI for the AI-specific interactions
- 4–6 weeks of post-launch evaluation and prompt iteration
Cost composition (typical $50k MVP):
- Engineering (front-end + back-end + AI logic): $32k
- Embeddings setup + vector DB integration: $7k
- Evaluation harness + prompt iteration: $6k
- Deployment + observability stack: $3k
- Project management + design + reviews: $2k
Ongoing run costs you should budget separately:
- Model API: $50–$500/month at MVP-stage usage (low traffic, ~10k–50k requests/month)
- Vector DB: free–$70/month at small data scale (under 1M vectors)
- Observability: free (LangSmith hobby) or ~$50/month (Helicone)
- Infrastructure: under $50/month on Vercel Hobby + Supabase Free until you scale
A real example: an internal-ops RAG assistant trained on a manufacturing client's three-year SOP archive (Notion + PDFs). Build was $55k over 9 weeks. Model cost about $120/month at 6k queries/month. Saved approximately 12 hours/week of operations-team time looking up procedures — paid back in 4 months.
Where this tier breaks down: when you need fine-tuning, when you're handling regulated data (HIPAA, PCI-DSS, SOC-2), when you have more than 50k users, or when failure has business-critical consequences. That's tier 3.
Tier 3: Production AI Platform ($80k–$500k+, 4–12 months)
You're building or migrating something that runs at scale and where AI quality is the difference between product-market fit and product-market death. The most common shapes:
- Multi-tenant SaaS with AI features for every customer's private data
- Agent platforms that take real-world actions (book appointments, send emails, push to production)
- Fine-tuned vertical models for legal, medical, financial domains
- Edge AI with hardware-specific constraints (mobile, robotics)
- AI-native products rebuilt from the ground up
What's included at this scale:
- Multi-model strategy with hard SLAs on response time and accuracy
- Fine-tuning pipeline (LoRA / QLoRA on base models like Llama 3.1 or Mistral)
- Custom retrieval with hybrid search (BM25 + vector) + re-ranking
- Comprehensive evaluation suite — not just LLM-as-judge but golden datasets with human annotators
- Multi-tenant data isolation
- SOC-2 / HIPAA / regional compliance posture
- Cost optimisation: model routing, caching, prompt compression
- Full observability (LangSmith, Helicone, OpenTelemetry traces)
- 24/7 monitoring + on-call rotation
Cost composition (typical $200k 6-month platform):
- Engineering (4 senior engineers, 6 months): $140k
- AI / ML specialist for fine-tuning + evals: $30k
- DevOps / infra setup + observability: $15k
- Compliance audit prep: $10k
- Product + design + project management: $5k
Ongoing run costs at scale (10k+ MAU, hundreds of thousands of model calls/month):
- Model API: $2k–$20k/month depending on volume + which models you route to
- Fine-tuning compute (one-time + occasional refreshes): $500–$5k per cycle
- Vector DB at scale: $200–$2k/month
- Observability + logging: $300–$1500/month
- Engineering retainer for ongoing tuning: $5k–$25k/month
A real example: a Vellumarc client building a vertical SaaS for property managers, multi-tenant with AI for lease document analysis, tenant communication automation, and maintenance ticket triage. Build was $250k over 8 months. Currently at 1.2M tokens/day across 800 paying customers. Total monthly run cost $8.4k including model + infra + observability. Average revenue per customer covers AI infrastructure 12×.
Maintenance costs nobody mentions
The number one budget-killer is not the build. It's everything after. Plan for:
- Prompt drift — model providers update their base models every 3–6 months. Your prompts need re-evaluation each time. Budget 2–4 engineering days per quarter per major prompt template.
- Evaluation set rot — your golden dataset becomes stale as user behaviour shifts. Budget a quarterly review and 20–40 new annotations per major feature.
- Hallucination drift — what worked at 50k users may fail at 500k. Budget evaluation re-runs at each 10× growth checkpoint.
- Vendor cost shifts — model providers reprice (OpenAI cut prices ~80% from 2023 to 2026 then re-introduced premium tiers). Budget annual cost-modelling reviews.
- Compliance shifts — DPDP Act 2023 in India, EU AI Act, US executive orders all evolve. Budget legal review annually if you handle personal data.
A working rule: budget 25–35% of build cost annually for AI maintenance after year one. If you built a $80k MVP, plan for $20k–$28k/year just to keep it working at the quality it shipped with.
How Vellumarc scopes AI projects
We start every AI engagement with a free 30-minute scoping call. Three things happen on that call:
- We figure out which tier you're actually in. The most common outcome is that founders think they need tier 3 and actually need tier 1.
- We give you a no-commitment cost range based on what we've shipped for similar shapes.
- We tell you the cheapest possible version that would solve your core problem, even if it's smaller than what you asked for.
If we agree to a build, our pricing is transparent and quoted in USD with itemised line items. Engagements start at $5k for tier 1 integrations and scale to $500k+ for tier 3 platforms. You can book a free AI audit here — we'll scope your project in one call and email you a written cost range within 48 hours.
If you want to compare model providers before committing, our OpenAI vs Anthropic vs open-source guide breaks down cost-per-token and capability differences. If you're specifically interested in the RAG architecture that underpins most tier 2 projects, see our production-grade RAG systems guide.
The honest bottom line
In 2026, AI development costs are wider apart than they've ever been. The same buzzword covers a $5k feature integration and a $500k production platform. Budget by tier, not by buzzword. Pick the smallest scope that solves the actual problem. Plan for 25–35% of build cost in annual maintenance. And work with a team that will tell you when you're overscoping.
The AI work that creates value isn't the work that sounds impressive in a board meeting — it's the work that quietly removes friction from your business one workflow at a time. Match the cost tier to the workflow value and the math works out.
Vellumarc is a senior India-based AI development team shipping production AI for global brands. Direct engineering hours, USD pricing, no juniors learning on your project. Get a free audit to scope yours.