Free Compute or Self-Hosted? A Break-Even Guide for Agent Workloads

The sticker price of compute tells you almost nothing about what one finished task actually costs you. What decides the argument is cost per verified task, and that number crosses over at a point you can calculate before you commit. So isn't the smarter question not "which option is cheapest" but "which option is cheapest for the shape of workload I actually run?"

What the free option actually is

MonkeyCode is an open-source project, and the operator states that it currently offers free model access together with a free server option, including a free token allowance reported at the time of writing as 10 million tokens. Disclosure: This article was prepared as part of MonkeyCode's product outreach. I have not benchmarked the platform myself, and I will not invent model names, latency figures, or throughput numbers for it.

Free allowances move constantly, so please treat that 10 million token figure as a snapshot rather than a contract. Confirm the current quota, the available models, and the free server limits on the project's own page before you design anything around them. If you cannot verify the terms today, plan for them to change tomorrow.

1. Define one unit of work that survives contact with reality

Before comparing anything, you need a unit that either passes a checker or fails it, because "cheap per call" is not the same thing as "cheap per finished job." Teams that skip this step usually pay twice for the same task and never notice the second invoice. Here is the smallest harness I would trust, and it is deliberately boring.

# verify.py — a task counts only if the checker passes for it
from collections import defaultdict
from dataclasses import dataclass

@dataclass
class Attempt:
    task_id: str
    tokens_in: int
    tokens_out: int
    wall_seconds: float
    passed: bool

def attempts_per_success(attempts):
    tries, won = defaultdict(int), defaultdict(bool)
    for a in attempts:
        tries[a.task_id] += 1
        won[a.task_id] = won[a.task_id] or a.passed
    wins = [t for k, t in tries.items() if won[k]]
    return sum(wins) / len(wins) if wins else float("inf")

Notice what this measures and what it refuses to measure. It counts attempts per successful task, not attempts per call, and it ignores any attempt that never produced a passing artifact. That single change is usually what turns a fuzzy argument about free compute into a number two engineers can agree on.

2. Collect your own p and token spend before you argue

Run roughly thirty tasks that look like your real queue, and log four fields per attempt: task id, input tokens, output tokens, and wall seconds. Do not cherry-pick easy tasks for the free endpoint and hard ones for the paid endpoint, because that comparison is worthless and everyone in the review will spot it.

A free endpoint that needs 2.1 attempts per success can cost more in engineer time than a paid endpoint that needs 1.05, simply because waiting and re-checking is work. Ask yourself honestly: who is watching that retry loop, and what is their hour worth to your team?

3. Turn the measurements into a crossover point

The script below takes your measurements, not mine, and returns cost per verified task for each route. Every default is illustrative only, so replace them with numbers you actually collected, including your own ops overhead and any overflow price beyond the free allowance.

# tco.py — inputs are YOUR measurements; defaults are placeholders, not quotes
def cost_per_verified_task(attempts_per_success, usd_per_attempt,
                           ops_usd_per_task=0.0, wall_minutes_per_task=0.0,
                           engineer_usd_per_hour=0.0):
    waiting = wall_minutes_per_task / 60 * engineer_usd_per_hour
    return attempts_per_success * usd_per_attempt + ops_usd_per_task + waiting

def overflow_cost(tokens_used, free_tokens, usd_per_token_beyond_free):
    return max(0, tokens_used - free_tokens) * usd_per_token_beyond_free

def break_even_tasks(fixed_monthly_usd, usd_per_task_a, usd_per_task_b):
    gap = usd_per_task_b - usd_per_task_a
    return float("inf") if gap <= 0 else fixed_monthly_usd / gap

Run it from the shell with the same arguments your logs produced, and keep the output in the commit message for whichever route you pick.

python3 tco.py --attempts-per-success 1.00 --usd-per-attempt 0.00 \
  --ops-usd-per-task 0.00 --wall-minutes-per-task 0.0 --engineer-usd-per-hour 0.0

4. Decide with fit criteria, not with vibes

A crossover only tells you where the lines meet, and it says nothing about whether an option is allowed to serve your workload. Use the table as the second filter, applied after the arithmetic, because compliance and residency questions cannot be paid away with a cheaper token.

Signal	Free tier fits when	Self-hosted fits when	Managed paid fits when
Data residency	Workload is public or non-sensitive	Regulated data must stay on your network	Contract and region controls are the product
Throughput shape	Bursty, interactive, low sustained volume	Steady load near your hardware ceiling	Spiky peaks you refuse to capacity-plan
Ops headcount	Nobody wants to run GPUs	You already run inference infrastructure	You want zero operational surface
Reproducibility	Version pinning is acceptable and easy	You control the exact build	Vendor offers stable pinned versions
Budget shape	Exploration and prototypes	Long-lived, predictable baseline load	Variable cost is preferable to capex

Debugging workflow: five checks in order

1. Is the failure actually a checker bug? Re-run the failing task by hand once, because a broken assertion quietly inflates your attempts-per-success and makes every option look expensive.
2. Are retries doubling token spend? Sum tokens across attempts per task, not per call, and see whether the retry loop is the real cost center.
3. Is the queue the bottleneck rather than the model? Compare wall seconds against model seconds; if queueing dominates, you are buying patience, not intelligence.
4. Are you computing the same task twice across two routes? Duplicated work across a free endpoint and a paid fallback will make the cheaper route look worse than it is.
5. Has the workload shape changed since you measured? Re-measure monthly, because a crossover computed on last quarter's task mix is a story about a workload you no longer run.

Limitations and who should skip this approach

This framework assumes you can define a passing checker, which rules out exploratory work where "done" is a judgment call rather than a test. It also ignores cold-start behavior, network variance, and any quota that resets in ways your logs cannot see, so a single week of measurement is weaker evidence than you might wish.

If your workload touches regulated data, or if an outage costs more than the compute ever will, a free tier is not a candidate at all, no matter how the arithmetic lands. Similarly, if you cannot spare a few hours to build the checker, you are not ready to compare routes, because every number you produce will be anecdotal.

What would your own thirty-task suite say about the route you are using right now? Build the harness, log the attempts, and let cost per verified task make the argument for you. If you want to try the free tier first, find the MonkeyCode open-source repository, run the protocol above on your real queue, and only then decide whether to keep it or move on.