A free hosted coding assistant usually costs less to start; a self-hosted open-weight model offers more control over where inference runs, but you pay in compute, setup, and maintenance. The right choice depends on how much you use it, what data it sees, and how well it handles your actual coding work—not the download price alone.
What “free” means in each setup
Three different things can be called free: downloading model weights at no charge, running a local runtime without a software fee, and using a provider’s free hosted allowance. They are not equivalent. OpenAI says its gpt-oss weights are available to download and use under Apache 2.0, but the person running them is responsible for compute, storage, and any third-party hosting costs (OpenAI’s gpt-oss FAQ).
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A hosted free tier removes the need to provision inference hardware, but it is bounded by the provider’s allowance and terms. Local use may avoid per-request charges, yet it depends on hardware capacity and still consumes electricity and time. Cloud-hosted open weights can add metered infrastructure costs even though the weights themselves have no purchase price.
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Compare the costs that affect your workload
| Cost or constraint | Free hosted assistant | Self-hosted open weights |
|---|---|---|
| Usage allowance | Check request limits, included credits, model access, and what happens when the allowance runs out. | There may be no provider request cap for local inference, but throughput is limited by your hardware and runtime. |
| Inference infrastructure | The provider supplies the compute for the hosted service. | You supply local compute or pay for rented infrastructure; storage and electricity also matter. |
| Setup and upkeep | Usually less infrastructure work, though account and plan terms still apply. | Allow for installation, updates, runtime configuration, troubleshooting, and maintenance. |
| Scaling up | Check whether higher usage requires credits, a paid plan, or a different model entitlement. | More usage may require more capable hardware or additional rented compute. |
| Support | Support and service terms depend on the provider and plan. | OpenAI describes its open-weight deployments as self-managed and self-serviced, and says it does not provide hands-on implementation or debugging for self-hosted or third-party-hosted setups. |
GitHub’s published Copilot Free plan lists 2,000 completions and 50 chat requests. GitHub measures Copilot usage in AI Credits and states that 1 AI Credit equals US$0.01; check its Copilot plan and billing documentation for current terms. These are plan figures, not a forecast of what a particular developer will spend.
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Ollama distinguishes starter usage for a smaller set of cloud models on Free accounts from local models run on a user’s own hardware, which it describes as unlimited. That distinction is described in Ollama’s pricing information: cloud starter access is an allowance, while local inference shifts the resource burden to your machine.
Match model size to the hardware you actually have
Self-hosting is not one hardware requirement. Memory needs vary with model size, quantization, context length, and runtime. OpenAI says its 117-billion-parameter gpt-oss-safeguard-120b variant is designed to fit on a single 80 GB GPU, with an NVIDIA H100 as an example (OpenAI’s model documentation). That is a scale illustration for this specific large variant, not a requirement for every local coding model.
Before choosing a model, check its published memory and runtime requirements against the machine you have. If you need a rented GPU, include the rental rate and expected hours in your estimate; a zero-cost download does not make hosted compute free.
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“Hosted” and “self-hosted” are not complete privacy descriptions. The model service, account type, coding client, agent tools, telemetry, and any managed hosting partner can all affect the data path. Read the current terms for the exact product and account you plan to use.
OpenAI says it does not receive or process data sent to self-hosted gpt-oss models unless a user shares it or uses a managed hosting partner. GitHub says individual Copilot subscriber interactions may be used for training unless the subscriber opts out; it says Copilot Business and Enterprise customer data is not used for model training. See GitHub’s Copilot documentation for account-specific details. These statements address model-provider handling; they do not, by themselves, establish what every editor, extension, or agent integration collects.
Test coding performance on representative work
The available official product and policy information does not establish a general winner for coding quality or speed between free hosted assistants and self-hosted open weights. Results depend on the selected model, its configuration, available context and tools, your hardware, and the way the assistant fits into your editor workflow.
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Run a small evaluation using tasks you really do, rather than a synthetic prompt alone:
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- Try a routine edit, a bug investigation, and a test-writing task in the language and editor you use.
- Compare latency, relevant context use, tool support, correctness, and how much review or correction each result needs.
- For self-hosting, test the model and runtime configuration you would actually operate; different hardware or quantization can change the experience.
Track both the assistant’s output and the time you spend getting a usable result. A nominally free option can be a poor fit if its allowance runs out or its answers require substantial rework.
Best Value
Choose by constraint, not by sticker price
A free hosted tier is a better starting point when
- You want to try coding assistance without setting up or maintaining inference infrastructure.
- Your usage fits within the published allowance, or you are comfortable checking paid usage terms before exceeding it.
- The service’s account-specific data handling and model access meet your requirements.
Self-hosting is worth evaluating when
- You need more control over where inference runs and can verify the full data path of your client and tools.
- You already have suitable hardware, or have priced rented compute, storage, electricity, and maintenance.
- You are willing to configure and troubleshoot the deployment and can validate the chosen model on your tasks.
There is no responsible universal break-even figure without your request volume, model choice, hardware, electricity rate, hosting rates, and maintenance time. Compare those inputs for your own workload, then weigh them against the privacy and operational constraints that matter to you.
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