Budgeting for Continue: providers, local inference and maintenance

Budget each model role

Completion, chat and code changes have different request frequencies and context sizes. A setup that completes constantly is not comparable to one that sends occasional large questions by request count alone. List the provider for each role and identify which activity appears in a cloud invoice before estimating monthly use.

Count setup and support time

Include initial configuration, distribution, connection troubleshooting and update checks. If your setup needs two hours of maintenance a month, include your own cost for those hours. This is an operating-cost assumption for your organization, not a claim about Continue’s product pricing or a universal support requirement.

Separate local hardware from usage

Comparing only API charges can overstate the advantage of a local setup. Evaluate memory needs, interference with other work and waiting time. Allocate hardware costs according to how you actually use it: a shared development machine is not necessarily a dedicated AI expense, while a new server purchased solely for inference is.

Compare accepted work, not tokens alone

Run the same defect through each candidate configuration and record checks, explainability and human correction time. Cheaper inference can be more expensive overall if each result needs substantial repair. Judge the complete workflow on your repository rather than ranking configurations solely by model names or nominal token rates.

Example: a two-developer operating budget

For two developers, include distributing the shared configuration and verifying both machines, not only the inference invoice. If failures occur on one machine, standardize the relevant setup before expanding adoption. A successful common baseline reduces the chance that each additional user creates a fresh troubleshooting task, and it makes recurring support costs more measurable.

Diagnose increases systematically

When spending changes unexpectedly, review model changes, completion frequency, context sources and active users. Versioned configuration makes that investigation more reliable. Keep credentials out of committed settings and give billing and development owners a shared, non-secret usage record so they can explain an increase without exchanging private keys.

• Choosing Continue: model connections and editor workflows
• Cursor vs Continue: integrated workflow or configurable models