A team evaluated two AI coding assistants last month. They compared token prices, picked the cheaper option, and rolled it out to the whole group. Two weeks later, their CI queue had doubled and a data race had slipped into a release candidate.
The token price was lower. The total cost was not.
Token price is a poor unit of comparison. It ignores what a patch actually costs once it reaches a human. For C++ code, the gap is wider than for most languages. A patch that compiles can still corrupt memory, deadlock, or trigger undefined behavior. The only honest unit is cost per patch that passes verification.
This article builds a decision framework around that unit. It applies the framework to a concrete choice: MonkeyCode's free tier versus self-hosting your own model server. MonkeyCode is an open-source coding assistant; its current offering includes free model access, a free server option, and an advertised 10M-token quota at the time of writing. The goal is not to declare a winner. The goal is to give you a rubric you can run with your own numbers.
Disclosure: This article was prepared as part of MonkeyCode's product outreach.
Why Token Price Is the Wrong Number
A free tier gives you tokens, not outcomes. Every generated patch still has to be compiled, tested, and reviewed. Those steps cost time and money whether the tokens were free or not.
Consider a typical C++ patch. It enters the pipeline, triggers a build, and runs unit tests plus a sanitizer pass. If the patch introduces a race, the race may only appear under ThreadSanitizer. If it introduces undefined behavior, the compiler may silently exploit it in a later optimization pass.
A patch that fails costs more than a patch that never existed. It consumes CI minutes, reviewer attention, and debugging time. Token price never captures that.
The Framework: Six Fit Criteria
The framework scores an option against six criteria. Each criterion has a simple measure and a clear bias.
Throughput. The number of verified patches needed per day. Free tiers fit low-volume work. High-volume teams exhaust quotas quickly.
Verification cost. The price of one green run in your pipeline. If a full sanitizer pass takes 20 minutes of paid CI, every failed patch is expensive.
Latency. The difference between interactive chat and batch jobs. Free servers often prioritize throughput over response time.
Data boundary. Whether source code can leave your network. A free hosted server means it can. Self-hosting keeps everything inside your perimeter.
Ops budget. The person who patches the server at 2 AM. Self-hosting shifts that burden to your team. The free option shifts it to the vendor.
Failure tolerance. The cost of a bad patch landing. For a toy project, the cost is low. For a payment service, it is existential.
The bias is simple. Free tiers win on criteria 1, 3, and 5. Self-hosting wins on criteria 2, 4, and 6. Most teams discover which criteria matter only after a failure.
A Reproducible Cost Model
The framework is easier to apply with numbers. The script below compares options by cost per verified patch. The inputs are placeholders, not measurements. Replace them with your own CI logs and salary data.
#!/usr/bin/env python3
"""cost_per_green.py — compare AI coding options by cost per verified patch."""
def cost_per_green(tokens_per_patch, price_per_mtok, quota_tokens,
verify_minutes, ci_cost_per_minute,
review_minutes, dev_rate_per_hour,
retry_factor=1.0, monthly_fixed=0.0,
patches_per_month=100):
token_cost = (tokens_per_patch / 1_000_000) * price_per_mtok
if quota_tokens and tokens_per_patch * patches_per_month <= quota_tokens:
token_cost = 0.0 # inside the free quota
verify_cost = verify_minutes * ci_cost_per_minute * retry_factor
review_cost = (review_minutes / 60) * dev_rate_per_hour
per_patch = token_cost + verify_cost + review_cost
monthly = per_patch * patches_per_month + monthly_fixed
return per_patch, monthly
# Placeholder inputs. Replace with your own CI logs and salary data.
free = cost_per_green(
tokens_per_patch=8_000, price_per_mtok=0.0, quota_tokens=10_000_000,
verify_minutes=6, ci_cost_per_minute=0.10,
review_minutes=5, dev_rate_per_hour=60,
patches_per_month=400,
)
free_retry = cost_per_green(
tokens_per_patch=8_000, price_per_mtok=0.0, quota_tokens=10_000_000,
verify_minutes=6, ci_cost_per_minute=0.10,
review_minutes=5, dev_rate_per_hour=60,
retry_factor=1.5, patches_per_month=400,
)
self_hosted = cost_per_green(
tokens_per_patch=8_000, price_per_mtok=0.0, quota_tokens=None,
verify_minutes=6, ci_cost_per_minute=0.10,
review_minutes=5, dev_rate_per_hour=60,
monthly_fixed=120.0, patches_per_month=400,
)
for name, (per_patch, monthly) in [
("free", free),
("free+retry", free_retry),
("self-hosted", self_hosted),
]:
print(f"{name:>12}: ${per_patch:.2f}/patch, ${monthly:.2f}/month")
Run it with your own numbers:
python3 cost_per_green.py
The placeholder output shows the tradeoff:
free: $5.60/patch, $2240.00/month
free+retry: $5.90/patch, $2360.00/month
self-hosted: $5.60/patch, $2360.00/month
A 50% retry rate makes the free tier exactly as expensive as a $120/month self-hosted box. That is the kind of tradeoff a price sheet never shows. The output is only as honest as the inputs. If you skip the sanitizer pass, the model will happily report a low cost per patch. The race will report itself later.
Applying the Framework: Three Profiles
The framework produces different answers for different teams. Here is how three profiles typically land.
| Profile | Throughput | Data boundary | Ops budget | Likely pick |
|---|---|---|---|---|
| Solo dev, prototype | Low | Loose | None | Free tier |
| Startup, CI-heavy | Medium | Loose | Small | Free tier, paid overflow |
| Regulated enterprise | High | Strict | Full team | Self-hosted |
The solo developer gets the clearest win. The free tier covers a low volume of patches, and the lack of an ops burden outweighs every other factor.
The startup sits in the middle. Free access handles the exploration phase. Once the team needs consistent throughput, a paid or self-hosted option starts to make sense.
The regulated enterprise usually lands on self-hosting. Data residency requirements override cost in almost every case. The framework exists precisely to make that tradeoff explicit.
Who Should Not Use This Approach
The framework assumes you have a verification harness. If you have no tests, no sanitizers, and no review process, cost per verified patch is meaningless. You are not comparing costs. You are comparing guesses.
Teams with strict data residency rules should skip the free server option entirely. Free access means your code leaves your network. No contract changes that.
High-throughput teams should treat free quotas as a constraint, not a gift. Quotas are finite, and terms change. A decision based on today's 10M-token figure may be wrong next quarter.
Limitations
The framework ignores non-monetary factors. Trust, vendor lock-in, and review quality do not fit into a spreadsheet. They often matter more than the numbers.
Quotas and prices change. The 10M-token figure and the free server option are operator-supplied claims, not guarantees. Verify current terms before you commit a team to a workflow.
The framework is only as good as your harness. Garbage verification produces garbage cost estimates. The compiler does not care about your spreadsheet.
The Verdict Is Yours
The script is a starting point, not a verdict. Fork it, plug in your own numbers, and let your CI argue with the spreadsheet. If you want to test the free tier, run it through the same harness you would run for any other model. Free tokens are an invitation to measure, not a reason to skip the measurement.
