How much it really costs to adopt AI in a company: the three 2026 tiers
The most common question we get is also the one least answered with actual numbers: how much does it cost to adopt AI in a company? The honest answer is that there are three very different cost tiers — ready-made subscriptions, API automation and AI embedded in your product — and most SMBs should start with the cheapest one.
Tier 1 — ready-made subscriptions ($20 to $200 per person/month). ChatGPT, Claude and Cursor handle writing, analysis, code and research with zero project work: create the account, set the data policy, start. For a 5-person team the whole pilot costs under $500/month and the return shows up (or doesn't) within weeks. This is where most SMBs should start — and also where many already pay for more than one subscription without monitoring usage on any of them.
Tier 2 — internal automation via API (a few hundred to a few thousand $/month). Ticket classification, document extraction, context-aware email: here you pay per token, and prices vary more than 100× across models. With cost-based routing (cheap model for light tasks, frontier only where needed), a typical SMB automation lands in the hundreds of dollars per month — without routing, the same workload can cost 5 to 10× more.
Tier 3 — AI embedded in your product (an engineering project + recurring usage cost). Here the dominant cost isn't tokens: it's engineering, security (the OWASP LLM Top 10 becomes a requirement), observability and compliance. This is the tier where good consulting and rollout pay for themselves — not for what they build, but for what they prevent: architecture rework, data leakage and vendor lock-in. Rule of thumb: only enter tier 3 after tiers 1 and 2 are measured and paying off.
Start at tier 1, measure usage, and only move up a tier with numbers in hand. The biggest waste we see isn't overpaying — it's paying without measuring.