AI engineering cost is bounded before the cycle starts.
Seat licences and token consumption both decouple from output value at scale. Wakalix structures AI engineering so spend is scoped per work item — not discovered at invoice close.
Book a stuck-problem reviewWhat it costs us — a real reference
Here’s the last three months of running our own 4-product portfolio. Every agent role that worked. Every deliverable that shipped. Every dollar we paid in AI subscriptions. This is the reference — what wakalix actually costs to operate at our scale. Customer engagements add a modest IP markup on top.
| Month | Agent roles worked | Deliverables shipped | AI subscription spend |
|---|---|---|---|
| April 2026 | 8 roles active across the team | 117 deliverables | $100 |
| May 2026 | 10 roles active across two product lanes | 239 deliverables | $100 |
| June 2026 | 12 roles active across 4 products in parallel | 601 deliverables | $100 |
| Reference scenario: ~7-member virtual team · 4 products simultaneously · ~$140/mo all-in including a 40% IP markup over the raw AI subscription cost | |||
For your team’s specific scenario, we’ll quote per engagement after a 30-minute scoping call. Nothing on this page is a list price for your team — we size to your scope.
Get a fixed quote for your team →Why AI coding cost becomes unpredictable
Coding assistants charge per seat. Agent loops charge per token. Neither model gives a team predictive visibility into what a quarter of AI-assisted engineering will cost. Prompts vary in size, context windows grow with codebase complexity, and agent loops can run far longer than expected without a scope limit built in.
Token spend and seat cost problems
- Seat costs scale with team size, not with value produced
- Token costs scale with prompt complexity and agent loop depth — both of which grow with codebase size
- Most teams have no per-task or per-feature cost attribution
- Agent loops without explicit scope limits can run until a budget ceiling is hit or a timeout fires
- No standard for "what did this AI-assisted work actually cost vs what it produced"
Why uncontrolled agent loops are risky
An agent loop without a defined scope can generate thousands of tokens investigating a problem, produce output that does not address the root cause, and repeat. Teams adopting agentic AI workflows without scope controls often find cost spikes tied to edge-case loops that had no exit condition until a budget alarm fired.
How Wakalix scopes work and controls spend
Wakalix operates on a cycle model with a defined problem, a bounded scope, and a predictable cost structure. AI engineering roles are assigned to specific tasks, not running open-ended. Token spend is tracked per problem and per role. Model selection is deliberate — the right model tier for the right task, not always the most expensive one.
What buyers should measure
- Cost per reviewable output batch — not cost per seat or cost per token in isolation
- Ratio of engineer review time to AI output volume — high review time on small output is inefficient
- Output value per cycle — did the stuck problem actually move?
- Rework rate — how much AI output required significant revision before merging?
- Unplanned token spend — agent loops that ran significantly beyond their initial scope
This is one stuck problem. If you want to see what shipping cycle-after-cycle looks like → the team.