Beyond the Buzz: What AI Really Costs for SMBs
AI is everywhere. Small and Medium Businesses are embracing AI to have a meaningful impact.
Tools like ChatGPT make it feel like Artificial Intelligence is free, fast, and easy to use. But when small and mid-sized businesses (SMBs) try to bring AI into their own operations, it is important to understand what is really required to make this happen.
So, what does AI really cost? And how does your choice of infrastructure, cloud or on-premise, impact your budget?
Let’s break it down clearly, without the hype. Below you will find some ballpark pricing on the cost of typical AI projects. For more specific guidance, reach out to us at NorthBound Advisory, and we can talk about your specific needs.
Typical SMB AI Requirements
Free tools like ChatGPT work because you're using a shared, public platform with no integration into your systems or data.
But if you want AI to:
Use your company’s private data
Automate real processes
Give accurate, domain-specific insights
Integrate with your CRM, ERP, or email
Comply with privacy or security rules
...then you're not using off-the-shelf AI. You’re building something custom. And that comes with a cost.
What Drives the Cost of AI?
AI costs aren’t just about software. They come from multiple areas:
1. Initial Investment (CapEx)
Consulting & Planning: $1K – $10K
Data Prep: $2K – $20K+
Development & Integration: $5K – $100K+
Infrastructure Setup (Cloud or Servers): Varies greatly
👉 Total Typical Range: $10K – $49K for SMB-ready solutions
Custom ML models or on-prem systems can go well over $100K
2. Ongoing Monthly Costs (OpEx)
Cloud usage fees (compute, storage, APIs)
Model maintenance and retraining
Security, updates, and support
Internal IT or external partners
👉 Plan for $500 to $5,000+ per month, depending on complexity
3. Key Cost Multipliers
AI complexity (chatbot vs. predictive model)
Volume and messiness of your data
GPU/server requirements
Skillset and staffing (AI/ML engineers = $$$)
Integration effort with your systems
On-Premise vs. Cloud: Comparison
| Feature | On-Premise | Cloud |
|---|---|---|
| Upfront Cost | High ($50K–$500K+) | Low (starts under $10K) |
| Monthly Cost | Lower (if fully utilized) | Variable (pay-as-you-go) |
| IT Burden | High (staff, maintenance, upgrades) | Low (vendor-managed infrastructure) |
| Scalability | Limited to physical hardware | Instant, elastic scaling |
| Speed to Launch | Slow (hardware procurement, setup) | Fast (on-demand services) |
| Data Privacy | Maximum control (data stays on site) | Shared responsibility with vendor |
| Best For | Regulated, high-volume use cases | Most SMBs starting out with AI |
Example 1: AI Chat with Your Company Documents (Private GPT / RAG)
What it does: Lets staff ask questions using your internal documents (policies, SOPs, manuals), using retrieval-augmented generation (RAG).
Build Cost (Same for both models)
$10K–$50K
Includes: Document ingestion, vector database setup, LLM integration, user interface
Cloud Hosting
Monthly: $500–$5,000+
LLM API usage (e.g., OpenAI, Azure)
Managed vector DB (e.g., Pinecone, Weaviate)
| ✅ Fast to deploy | ✅ Scalable | ❌ Recurring usage fees | ❌ Data goes through third-party APIs |
|---|
On-Prem Hosting
Upfront Infra: $30K–$100K+
GPU server (for hosting open-source LLMs)
Self-hosted vector DB (e.g., Qdrant, Milvus)
Monthly: ~$1,000–$3,000 (power, cooling, IT staff)
| ✅ Maximum data control | ❌ High upfront hardware cost | ❌ Requires IT/MLOps team |
|---|
Example 2: No-Code Process Automation + AI Agent
(e.g., n8n or Make.com + LLM for decision support)
What it does: Automates business workflows with AI assistance (e.g., "If this quote is overdue, escalate and alert me").
Build Cost
$3K–$15K
Includes: Workflow design, chatbot prompts, API integration, testing
Cloud Hosting
Monthly: $100–$2,000+
LLM API usage
SaaS automation tools (e.g., n8n Cloud $24–$60/month)
| ✅ Fast and simple | ✅ No internal server management | ❌ Ongoing SaaS/API costs |
|---|
On-Prem Hosting (n8n only; LLM still cloud)
Infra: $500–$5,000+ server setup
Self-hosted n8n instance
Still uses cloud AI APIs (OpenAI, etc.)
Monthly: $100–$1,000+
IT time for patching
API fees remain
| ✅ More control over workflow automation | ❌ Still dependent on cloud for AI | ❌ Requires light server admin skills |
|---|
Example 3: Off-the-Shelf App with Built-in AI (e.g., CRM, ERP)
What it does: Adds AI into everyday tools—e.g., smart sales forecasts or automated content suggestions
Build Cost
$0 – $5K
Mostly config and onboarding; vendor handles dev
Cloud SaaS (Vendor-Hosted)
Monthly or Annual Subscription: $1,000–$20,000+
AI included in enterprise plans
| ✅ Turnkey | ✅ High performance | ❌ Subscription cost | ❌ Locked into vendor's AI roadmap |
|---|
On-Prem (Rare & Not Recommended for AI)
Infra: $10K–$50K+
App license + servers
Limited to simple CPU-based AI features
Monthly: $500–$1,500+
Performance often suffers without GPUs
Limited feature parity with cloud
| ✅ Local data control | ❌ Weak AI performance | ❌ Higher setup and IT burden |
|---|
Example 4: Custom ML Model for Predictive Analytics
What it does: Forecast sales, predict churn, detect machine failures—trained on your proprietary data
Build Cost
$50K–$250K+
Includes: Data prep, model training, evaluation, deployment
Cloud Hosting
Monthly: $1,000–$10,000+
GPU compute (training/inference)
ML platform services (e.g., SageMaker, Azure ML)
| ✅ Scalable, faster time to value | ✅ No infra management | ❌ Ongoing compute fees can balloon if unoptimized |
|---|
On-Prem Hosting
Infra: $50K–$200K+
GPU server cluster
Storage + networking
Monthly: $2,000–$8,000+
Staff, power, hardware refresh cycle
| ✅ Full model/data control | ❌ High upfront cost | ❌ Complex operations |
|---|
Final Recap Table
| AI Solution | Build Cost | Cloud Monthly | On-Prem Infra (One-Time) | On-Prem Monthly |
|---|---|---|---|---|
| AI Chat w/ Docs (Private GPT / RAG) |
$10K–$50K | $500–$5K+ | $30K–$100K+ | $1K–$3K |
| AI Agent + Workflow Automation (e.g., n8n + LLM) |
$3K–$15K | $100–$2K+ | $500–$5K+ | $100–$1K+ |
| Packaged App with Built-In AI (CRM, ERP, etc.) |
$0–$5K | $1K–$20K/year | $10K–$50K+ | $500–$1.5K+ |
| Custom Predictive ML Model | $50K–$250K+ | $1K–$10K+ | $50K–$200K+ | $2K–$8K+ |
Conclusion: Know the Real Cost Before You Build
AI has incredible potential to unlock new efficiency, insight, and innovation in your business, but only if you go in with your eyes wide open.
While ChatGPT and other public tools make AI feel "free," real-world AI that works with your systems, your data, and your workflows has real cost drivers, especially around infrastructure, data, and maintenance.
Your biggest decision? Choosing between cloud and on-premise hosting.
Cloud is the most practical starting point for most SMBs: low upfront cost, fast to launch, and easy to scale.
On-premise may make sense if you have strict data privacy needs, existing infrastructure, or very high-volume, always-on AI workloads, but expect a high capital investment and internal IT overhead.
Bottom line for SMBs: Start simple. Start smart.
Choose AI solutions that match your real business needs, capacity, and budget, not the hype.
If you're unsure which path makes the most sense, contact NorthBound Advisory. We can help assess the cost, build, and hosting options that align with your goals.
To explore this topic a bit deeper, checkout a 8-minute Podcast from Rick and Amanda as they explore this Blog in greater depth.
