Costs outside the token price

A request that fails after the model has started writing is still billed. So is an answer that stops at the output limit. In both cases you pay, and then you pay again to finish.

A listing with a low cap on output, or one that times out under load, costs more than its rate says. Count the retries in your logs before you call a rate low.

Rate limits cap the requests and tokens you can send in a minute. Below your peak, they turn a low price into a queue. A batch that should take an hour takes a day.

Limits are seldom on the price page. Find them in the documentation, or ask, and write them beside the price.

A lower-priced model that gets a task wrong has not saved money. Someone checks the answer, or a second model does, or the request is sent again to a larger one. The low rate is paid in addition, not instead.

This is the argument for testing. Cost per correct answer is the figure that matters, and only your own prompts can measure it.

Every provider you add brings a key, a client library, a bill and a set of limits. If chasing the lowest listed price means a new integration each month, the saving goes to engineering time.

An OpenAI-compatible API lowers that cost, because the client you have already speaks it. One API with many models lowers it further.

Runcrate serves 124 models through one OpenAI-compatible API, paid from one prepaid balance that also covers GPUs and storage. It has a CLI, an SDK and an MCP server that an agent can drive. When a workload grows steady enough that tokens are no longer the low-cost way, the same account rents a GPU: 26 GPU models from $0.23/hr.

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