Bring Your Own Key, Credits, or Local Models: Choosing How to Power Your Coding Agent

Use vendor credits when you want the shortest route from install to useful work, BYOK when you need direct control of models and provider billing, and local models when keeping code on your machine or avoiding per-request spend is worth the operational tax. Don’t pick based on the word “unlimited”: the power source changes your available models, feature set, audit trail, failure modes, and the person who gets paged when an agent stops working.

Pick the power plane before you pick the model

A coding agent has two separate decisions hiding in one model selector: which model does the work, and who supplies the credential, routing, and invoice. Credits collapse those decisions into one vendor account. BYOK separates them. Local models replace most of the external model path with a process and hardware you operate.

For an individual developer, use this as the default decision rule:

• Choose credits or a subscription when you want to open the agent, select a supported model, and start a task without managing provider keys or separate invoices.
• Choose BYOK when you already have a provider account, need a particular model or cloud deployment, or want usage to land in an existing cost center.
• Choose local when source-code locality, offline work, or repeated low-stakes tasks matter more than getting the strongest hosted model on every prompt.
• Choose a gateway, not personal BYOK, when a team needs revocation, budgets, routing, and logs that survive a developer leaving.

That last point is worth making early. A shared spreadsheet of personal API keys is not a team model strategy. Anthropic’s gateway guidance describes the useful version: provider credentials stay server-side, developers receive revocable gateway credentials, and the organization gets centralized usage attribution, budgets, and audit logs. It also names the cost: the gateway is infrastructure that must keep up as the agent adds capabilities.

Credits: pay for fewer moving parts

Credits are the right default if your real problem is getting through a ticket, not assembling an LLM supply chain. In Cline, for example, the provider menu distinguishes its signed-in usage billing and subscription options from cloud BYOK and local runtimes. The usage-billing path is one sign-in, no provider-key management, and access to multiple providers from the same account.

That’s a legitimate advantage. A new laptop, a new repository, and a 30-minute debugging session are all better when you aren’t also deciding whether an API key belongs in a password manager, shell profile, CI secret store, or cloud IAM role. Put a small, deliberate amount of credit on the account first. Treat the first week as a measurement period: note which tasks you delegate, how often you cancel runs, and whether you routinely need a model the product does not expose.

Credits are bad at precise cost attribution and policy control once the tool becomes team infrastructure. You may see one agent invoice while the underlying model mix changes, or find that a feature you rely on is tied to the product’s built-in serving path. Also, “bring your own key” often doesn’t replace every feature. Cursor’s API-key documentation, for instance, says BYOK is for standard chat models; specialized features such as Tab Completion continue on Cursor’s built-in models. Check this before assuming your key powers the whole product.

BYOK: use it when the provider relationship matters

BYOK is for developers who care which account is charged and which endpoint receives the request. It’s the practical choice when security has already approved Bedrock, Vertex, Azure OpenAI, or a direct provider account; when you want to test two models against the same repository task; or when you need the provider’s own quotas, invoices, and access controls.

Set it up as a disposable experiment, not a permanent secret scattered across machines. In a tool such as Cline, select the provider, paste a restricted key, select the exact model ID, and run one representative task: a failing test, a small refactor, or a documentation update with a concrete acceptance check. If you use an OpenAI-compatible service, the three values to verify are the base URL, API key, and model ID. A wrong base URL can look exactly like a model failure.

# Keep a key out of shell history and scope it to one terminal session.
read -s ANTHROPIC_API_KEY
export ANTHROPIC_API_KEY
claude
unset ANTHROPIC_API_KEY

BYOK is bad at being boring. You own rate limits, expired cards, quota errors, provider outages, and the awkward point where a model name works in chat but not in agent mode because it lacks the tool-calling behavior the agent expects. Don’t give a long-lived owner key to a desktop extension just because it’s convenient. Use a project or developer-scoped credential where your provider supports it, and rotate it when someone leaves the project.

For a team, stop here and put a gateway in front of the providers. That gives you one endpoint in developer configuration and lets finance or platform engineering route models, impose budgets, and revoke access without asking every developer to edit local settings. It’s less exciting than a model bake-off, which is exactly why it works.

Local models: make locality the reason, not the fantasy of free inference

Run local when you have a reason that survives a little friction: a repository cannot leave the workstation, developers work on unreliable networks, or you have enough repeatable tasks to justify hardware and model operations. Cline supports local Ollama or LM Studio runtimes without an API key, and Ollama exposes an OpenAI-compatible local endpoint at http://localhost:11434/v1. That makes local serving a usable option in agents that already understand an OpenAI-style API.

Start with a model that explicitly supports tools, then test the agent loop rather than only asking it to write a function. For a concrete baseline, Ollama lists qwen3-coder:30b as a 19 GB download with tool support. Pull it, run a short task, and measure the thing that matters: can it inspect files, issue a correct command, interpret test output, and make a small diff without wandering?

ollama run qwen3-coder:30b

# In Cline: Settings → API Provider → Ollama
# Then select qwen3-coder:30b and run a small test-fix task.

Local is bad at hiding constraints. Nineteen gigabytes on disk is not the same as enough memory for responsive inference with your editor, browser, containers, and test suite open. Context length settings, quantization, GPU memory pressure, tool-call reliability, and concurrent requests are now your problem. Keep hosted access for tasks where the agent must reason across a large unfamiliar codebase or recover from several failed commands. Local is often excellent for private code search, mechanical edits, test generation, and throwaway investigation; it is not an automatic replacement for a hosted agent on the hard work.

Use a two-lane setup

The setup that tends to last is not ideological. Keep one low-friction hosted lane for consequential work and one controlled lane for experiments or private routine tasks. Label them in the agent UI: hosted-review, byok-build, and local-scratch is enough. Don’t let the agent silently choose between them if cost, data handling, or quality matters.

Then write down one routing rule you can follow before coffee: use the hosted lane for production changes, the BYOK or gateway lane when a project has an approved provider and chargeback requirement, and the local lane for code that must stay local or work you would not mind redoing. Revisit that rule after ten real tasks, not after reading another benchmark chart.