How Much Does AI Integration Cost for Small Business? Honest Pricing Guide (2026)

Everyone wants to talk about AI's potential. Nobody wants to talk about what it costs. Here's a transparent breakdown from someone who actually builds these systems.

Why AI Pricing Is So Opaque

Try searching "how much does AI cost for my business" and you'll find:

• Consulting firms saying "it depends" (translation: it's expensive)
• SaaS tools quoting per-seat pricing that doesn't include customization
• Agencies with no public pricing (translation: they charge whatever they think you'll pay)
• Blog posts from 2023 that are wildly outdated

Let's fix that. Here's what AI integration actually costs in 2026, broken down by project type, with the factors that move the price up or down.

The Four Types of AI Integration Projects

1. AI Readiness Audit: $3,000 – $5,000

What you get: A structured assessment of your data landscape, workflow pain points, and a prioritized list of AI opportunities with estimated costs and ROI.

Timeline: 1–2 weeks

What drives the price:

• Number of data sources to inventory (5 sources = lower end, 20+ = higher)
• Complexity of your tech stack
• Number of stakeholder interviews needed

Who needs this: Companies that know they want AI but aren't sure where to start. About 50% of audits lead to implementation projects — because once you see the ROI math, the decision makes itself.

Skip this if: You already know exactly what you want built. Go straight to implementation and save the $5K.

2. RAG & Knowledge Base: $15,000 – $50,000

What you get: A system where your team can ask questions in plain English and get answers sourced from your internal documents, with citations.

Timeline: 4–8 weeks

Price factors:

Factor	Lower End ($15K)	Higher End ($50K)
Data sources	1–2 (e.g., one file share)	5+ (databases, APIs, wikis, email)
Document volume	<10,000 documents	100,000+ documents
Access controls	All users see everything	Role-based permissions
Interface	Simple web chat	Slack/Teams integration + web
Hosting	Cloud (managed)	On-premises (compliance)

The most common project: Mid-size company with a SharePoint/Google Drive knowledge base, 10K–50K documents, basic access controls, Slack integration. Typical price: $22,000 – $30,000.

3. Custom AI Agents: $25,000 – $75,000

What you get: AI systems that don't just answer questions — they take actions. Query databases, generate reports, process incoming data, automate multi-step workflows.

Timeline: 6–12 weeks

Price factors:

• Number of integrations — Each API/tool the agent interacts with adds complexity
• Workflow complexity — Linear flows (A→B→C) are cheaper than branching decision trees
• Reliability requirements — Higher stakes = more testing, guardrails, and human-in-the-loop checkpoints
• Data sensitivity — Agents handling PII or financial data need additional security layers

Examples with rough pricing:

• Sales agent that queries CRM and drafts outreach: $25K–$35K
• Operations agent monitoring inventory + generating POs: $35K–$50K
• Compliance agent scanning documents for regulatory issues: $45K–$65K
• Multi-agent system coordinating intake, analysis, and routing: $55K–$75K

4. Managed AI Operations: $2,000 – $8,000/month

What you get: Ongoing monitoring, optimization, and iteration of your AI systems.

Price factors:

• $2K–$3K/mo: Monthly monitoring, quarterly knowledge base updates, bug fixes
• $4K–$6K/mo: Weekly optimization, new data source integration, prompt tuning, monthly reporting
• $6K–$8K/mo: Active development of new features, multiple system management, priority response

Why this matters: AI systems aren't "set and forget." Knowledge bases need updating. Retrieval accuracy drifts. New use cases emerge. The companies that get the most ROI from AI treat it like a living system, not a one-time project.

The Hidden Costs Nobody Mentions

LLM API Costs

Every AI query costs money. Here's what to expect:

• Light usage (50–200 queries/day): $50–$200/month
• Medium usage (200–1000 queries/day): $200–$800/month
• Heavy usage (1000+ queries/day): $800–$3,000/month

These costs have dropped 80%+ in the last two years and continue to fall. What cost $500/month in 2024 costs under $100 today.

Infrastructure

Your AI system needs to live somewhere. Budget $100–$500/month for cloud hosting (PostgreSQL + application server + vector storage). On-premises is more upfront but eliminates ongoing hosting costs.

Internal Time

Your team will spend time during build: identifying documents to ingest, testing the system, providing feedback. Budget 2–5 hours/week from your champion during the build phase. This time is not wasted — it's what makes the difference between a system that works and one that sits unused.

The ROI Framework

Before committing budget, run this calculation:

1. Time saved per person per day (be conservative — 15–30 minutes is typical)
2. Number of people using the system
3. Fully loaded cost per hour (salary ÷ 2080 × 1.3 for benefits)
4. Annual savings = time saved × people × hourly cost × 250 working days
5. Payback period = project cost ÷ annual savings

If the payback period is under 6 months, it's almost certainly worth doing. Under 12 months, probably worth it. Over 12 months, think harder — or start with an audit to find higher-ROI opportunities.

Red Flags in AI Pricing

• No fixed pricing — "We'll bill hourly and see where it goes" means they can't scope it, which means they haven't built enough of these
• 6+ month timelines for basic projects — A RAG knowledge base does not take 6 months. If someone quotes that, they're staffing junior developers who are learning on your dime
• Separate strategy and implementation — If the same firm can't assess AND build, you're paying for a handoff
• No production examples — If they can't show you a working system they've built, they're consultants with engineering ambitions
• "AI-powered" everything — If their solution to every problem is AI, they're selling a hammer and everything looks like a nail