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Continuous Optimization is an optional monthly subscription for a purchased package. RunInfra keeps re-optimizing and re-benchmarking the package as serving engines improve, then gives your workspace the newest verified version. Your original package purchase remains separate. The package is yours forever, stays downloadable, and never expires whether or not you subscribe.

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What the subscription includes

A new version is verified against measured results. Refer to that version’s receipt for its speedup, throughput, accuracy recovery, and VRAM figures.

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Billing

Continuous Optimization costs about 20 credits per month for each subscribed package, or about 20because1credit=20 because 1 credit = 1. The subscription draws from the same workspace credit balance as the package purchase and other RunInfra usage. The subscription applies per package. Subscribing to one package does not add update coverage to another package in the workspace.

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Cancel the subscription

You can cancel at any time. The current paid period continues until its end, and future monthly renewals stop after that period. Canceling does not remove the package you bought. Your one-time purchase remains downloadable and does not expire. Version-update access continues through the end of the paid subscription period.

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Common questions

No. It is optional. The purchased package remains available to download and use without a subscription.
RunInfra sends a version-update notice by email. Each updated version includes its measured receipt.
The monthly charge comes from your existing workspace credit balance. Expect about 20 credits per package each month, where 1 credit = $1.
The paid period runs to its end. Future renewals stop, while the package you purchased remains yours and stays downloadable.

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Next steps

Optimized Models Catalog

Browse, buy, download, and deploy verified packages.

Optimization

Run pipeline-specific optimization against your own priorities and constraints.

Plans and pricing

Review workspace credits and paid plans.

Models

Explore supported model types and selection guidance.