> For the complete documentation index, see [llms.txt](https://docs.amigo.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.amigo.ai/intelligence-and-analytics/cost-to-serve.md). # Cost to Serve Workspace-level cost-to-serve visibility covering LLM inference and infrastructure, sourced from the monthly cost-allocation close. The platform exposes workspace-level cost-to-serve data so Amigo staff can understand what it costs to operate each workspace. Two separate views cover distinct cost domains, each with its own accuracy characteristics. ## LLM Inference Cost LLM inference cost uses recorded workspace token usage and the rate-card tier effective on each usage date. It is a calculated cost, whose completeness depends on emitted usage, cache counters, and pricing coverage. It does not establish the amount invoiced by a provider or owed by a customer. Each model used by the workspace is reported individually with: * Input, output, and cached token counts. * Dollar cost, when the applicable rate-card entries and required usage counters are available. * A pricing status flag. Usage that cannot be priced remains visible with token counts and a null cost. The response-level total excludes unpriced usage and flags that the total may understate actual spend. Review unpriced usage before relying on the total for a cost comparison. ## Infrastructure Cost Infrastructure cost represents the workspace's usage-weighted share of pooled compute and storage. Components include pipelines, model serving, warehouse, storage, and related resources. Shared-resource figures depend on the allocation method and available usage weights. Review those assumptions separately from the token-metering and pricing coverage used for inference cost. Each component reports: * The infrastructure component category. * How the cost was determined: a usage-weighted share of a pooled resource, or spend tagged directly to the workspace. * The dollar amount for the month. Components with no spend in a given month are omitted rather than shown as zero. ## Data Source and Availability Both views are sourced from a monthly cost-allocation close that reconciles the full vendor bill to zero residual. Data is available after the close job completes, typically within a few days of month end. Only closed months can be queried. Account-level overhead that cannot be attributed to any single workspace is excluded at the data layer and never appears in workspace-level results. ## Access Restrictions Cost-to-serve data is restricted to Amigo staff. Workspace administrators - even those with full admin permissions - cannot access these endpoints. This is deliberate: the data represents Amigo's cost structure and margins, not the customer's bill or contracted price. Authentication requires an Amigo identity credential. API-key-authenticated callers are rejected because API keys do not carry the identity information needed for staff verification. ## Relationship to Other Analytics Cost-to-serve is separate from the workspace analytics and metric store surfaces. It is not available as an agent-reachable tool or through data access integrations. The data serves internal operational visibility, not customer-facing reporting. For the separately restricted staff interface, see [Cost to Serve](https://docs.amigo.ai/developer-guide/platform-api/platform-api/cost-to-serve) in the Developer Guide. For customer-facing operational reporting, see [Analytics and Dashboards](/intelligence-and-analytics/intelligence/analytics-dashboards.md). --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.amigo.ai/intelligence-and-analytics/cost-to-serve.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.