Free vs Self-Hosted Models: A Break-Even Framework for Agent Workloads

The cheapest model for agent workloads is not determined by the model with the lowest price per token. Rather, it is the model whose failure modes are acceptable. For agent workloads, hosting becomes a break-even problem rather than a benchmark problem. This article presents a three-variable framework – volume, failure cost, and operational time – and provides a runnable script to calculate the most cost-effective option based on your specific numbers.

The example used is MonkeyCode, an open-source project that offers free model access and a free server option, which makes it a suitable substitute for the broader category of free endpoints. MonkeyCode's free model access removes the cost of inference, while the free server option eliminates control-plane costs. Consequently, the only factor left to consider is whether the free tier's constraints – allowance, rate limits, and latency – are suitable for your workload.

Since the project is open source, you can review the code paths that consume tokens before making any commitments. The timing of this article is not coincidental. Agent loops are highly demanding in terms of token usage. A single task with five tool calls can consume fifty thousand tokens, and retries multiply this number. Teams often rely on pricing sheets to choose a hosting option, only to discover the true cost during the first incident review.

Benchmarking models solely by cost per token does not provide the full picture. The solution is to model your workload before selecting a platform. The first variable in this framework is volume. Estimate the number of tokens per task, multiply it by the number of tasks per day, and add a margin for retries. To obtain this estimate, log the token usage of ten representative tasks, take the median, and multiply it by 1.5.

If the median is unstable, your workload is too diverse to be modeled with a single number; in such cases, split it into task classes and run the calculator for each class separately. The second variable is failure cost. A bad completion in a code-review loop might require a retry, costing two to three times the original tokens.

A bad completion that reaches production can lead to a human investigation, which incurs significant costs, such as hours of work. If failures are relatively inexpensive to catch, a free tier can be beneficial. However, if failures propagate, you need a trusted model and an SLA that can be enforced. The third variable is operational time, which is often overlooked.

Self-hosting appears to be free until you consider GPU amortization, queue tuning, and the occasional 2 a.m. out-of-memory crash. A free server option shifts the control-plane burden to the provider, narrowing the comparison to inference alone. The break-even calculator presented in this article models three hosting options: a free managed tier with a token allowance, a paid API, and a self-hosted stack.

The default allowance is set to 10 million tokens, reflecting MonkeyCode's free tier at the time of writing (August 2026). The calculator turns the three workload variables into a monthly hosting cost for each option and then reports failure exposure separately. It is important to note that this model is a decision aid and not a guarantee.

To use the calculator, run the following command: `python break_even.py --tokens-per-day 2_000_000` This command calculates the monthly hosting cost for each option based on your provided input.