Zero dollars per seat is not zero cost. When an AI coding tool enters your delivery path, the invoice you don't receive hides three line items that do arrive: dependency suggestions your team never verified, review time absorbed by senior engineers, and the migration cost when the free tier changes terms. This week the DEV community has been discussing slopsquatting — supply-chain attacks that weaponize AI-hallucinated package names. That is exactly the kind of cost that never shows up in a pricing table.
This article gives you a 4-gate scorecard for deciding whether a free AI coding tier (models at no charge, optionally a hosted server at no charge) belongs in your team's workflow — with worked numbers, a break-even check, hard gates, an owner, an expiry, and exit criteria. Treat the scorecard as a conversation tool, not objective truth.
The three hidden costs of "free"
- Verification cost. Every suggested dependency, config flag, or API call needs a human check. If a suggestion invents a plausible package name, the check is not optional — it is the difference between a merged PR and a compromised build.
- Review displacement. Free access tends to increase code volume before it increases code throughput. Senior review hours are finite and expensive.
- Exit cost. A pilot that has no archive rule becomes an undocumented dependency. When the tier changes, you pay the migration under time pressure.
The 4-Gate Scorecard
Score each gate 0–2. Definitions first, then a filled example.
| Gate | Variable | 0 points | 1 point | 2 points |
|---|---|---|---|---|
| G1: Dependency verification | % of AI-suggested packages verified against a registry before merge | <50% or unmeasured | 50–90% | >90%, with an automated check in CI |
| G2: Review absorption | Senior review minutes per accepted AI-assisted PR vs. baseline | >2× baseline | 1–2× baseline | ≤ baseline |
| G3: Task-route fit | Share of pilot tasks that are low-blast-radius (tests, docs, scripts) | <30% | 30–60% | >60% |
| G4: Exit readiness | Time to remove the tool from the delivery path | >2 weeks or unknown | 1–2 weeks | <1 week, documented |
Decision rule: total ≥ 6 with no gate at 0 → pilot continues. Any gate at 0 → pause that route, fix, re-score in two weeks. Total ≤ 3 → stop.
Filled example
A 6-person platform team runs a 4-week pilot on test-generation and migration-script tasks only:
- G1: CI job checks every new dependency name against the registry API; 96% verified → 2
- G2: Review minutes per accepted PR: 18 vs. baseline 15 → 1
- G3: 70% of routed tasks are low-blast-radius → 2
- G4: Tool is invoked through a single internal wrapper; removal is one config change → 2
Total: 7 → continue, with G2 flagged for a two-week re-score.
Break-even check (the number that can reverse the decision)
Define V = hours saved per developer per week on routed tasks, and R = fully loaded hourly cost of a senior reviewer. The pilot breaks even when:
team_size × V ≥ extra_review_hours × R / R_dev + verification_overhead_hours
Worked example: 6 developers, V = 1.5h, reviewer cost ≈ dev cost, extra review = 3h/week total, verification overhead = 1h/week. Savings = 9h; costs = 4h. Net +5h/week — continue. But if V drops to 0.5h (a plausible outcome once novelty fades), savings = 3h against 4h of cost — stop or re-route. Measure V weekly; do not estimate it once and forget it.
Where free model access and a free server actually help
The economics above are why free tiers matter for pilot design, not for procurement victory laps. A no-cost tier lets you run the scorecard with real task traffic instead of a 3-seat demo, and a self-hostable free server option matters if your code can't leave your network — which is itself a hard gate for many teams.
Disclosure: This article was prepared as part of MonkeyCode's product outreach. MonkeyCode is one example that currently offers free model access and a free server option, which makes it a reasonable candidate for exactly this kind of bounded, low-blast-radius pilot. I am deliberately not claiming quotas, model quality, or permanence — treat availability as of this writing and re-verify before your pilot starts.
Hard gates, owner, expiry, exit
- Hard gates (non-negotiable): no AI-suggested dependency merges without registry verification; no routing of secrets, auth, or payment code paths during the pilot.
-
Owner: one named engineering manager owns the scorecard and the weekly
Vmeasurement. - Expiry: the pilot authorization expires in 4 weeks. Continuation requires a re-score, not inertia.
- Exit criteria and archive rule: if the pilot stops, remove the wrapper config, archive all prompts and accepted diffs in the team wiki within 5 working days, and record the final score where the next evaluator can find it.
Limitations and who should not use this approach
- The scorecard assumes you can measure review time and task throughput. Teams with no instrumentation should instrument first, pilot second.
- Regulated environments (code export restrictions, audit requirements) should treat the dependency-verification gate as necessary but not sufficient — get security review before any pilot.
- If your bottleneck is requirements clarity or release process, an AI coding tier — free or paid — will amplify the bottleneck, not fix it.
- A 4-week pilot with 6 people produces directional evidence, not statistics. Don't scale on it alone.
The question that matters
Before your next standup, ask the team: which variable would reverse your decision — the measured hours saved V, the review-time ratio, or the verification rate? If nobody can name one, you're not running a pilot; you're running a subscription with extra steps.
If you're setting up this kind of gated pilot and want a zero-budget way to generate real task traffic for the scorecard, MonkeyCode's free tier is one place to start — just bring your own measurements.
Sources and further reading: the slopsquatting discussion trending on DEV this week (search "slopsquatting" on dev.to), and this account's earlier pieces on pilot gates and decision ledgers.
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