SaaS tokenminning
SaaS tokenminning applies this wiki’s cost engineering approach to the services around your workloads: identify what gets billed, keep the useful behavior, and change the expensive part of the architecture. Start with the service whose bill you want to reduce.
Platform credits and user licenses are different billing units from LLM tokens. Moving a workload off a managed service changes its platform bill; model API calls, storage, and replacement infrastructure still need their own budgets.
Reduce a specific bill
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How to reduce your Snowflake bill starts with warehouse usage, idle time, and repeated work, then evaluates self-hosted ClickHouse and DuckDB for suitable workloads.
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How to reduce your Salesforce bill covers purchased seats, editions, and add-ons, with a migration and cost comparison for SuiteCRM and EspoCRM.
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Why Dagster gets expensive explains the May 2026 pricing change, the credit meter, and why frequent small jobs can produce a large platform bill.
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Self-hosting Dagster: benefits and break-even costs compares managed hosting with running Dagster OSS, including the engineering work that belongs in the calculation.
Make the bill explainable
Take a representative billing period and separate the subscription, metered events, execution resources, and downstream services. Match each charge to useful work: a completed refresh, a delivered dataset, or a successful customer workflow. Then compare the cost of retaining the managed service with operating the equivalent workload yourself.
Self-hosting is particularly worth investigating when the platform meter grows faster than the resources the workload needs. Include capacity headroom and maintenance time before deciding that a smaller infrastructure bill means a cheaper system.
For inference costs, continue with the optimization sequence and self-hosted model serving.