> For the complete documentation index, see [llms.txt](https://docs.archera.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.archera.ai/help-center/faq/how-archera-underwrites.md). # How Does Archera Underwrite? ## What Does it Analyze? Archera uses its read-only access to cost- and usage-related data and metadata to build a comprehensive picture of your infrastructure usage and delineate between different use-cases being served. ## What Is the Core "Engine"? The core engine is based on a mix of learned heuristics and rules — for example: workload region, instance/VM type, engine, the types of commitments available to cover these workloads and their accompanying discount rates — as well as machine learning models trained on historical service usage & longevity rates. The way we calculate the "risk" of insuring a commitment is based on a few factors, one of which is the coverage ratio. The lower the historical coverage level a customer has before purchasing a commitment, the less risk posed to us when underwriting that risk. Conversely, the higher the historical coverage the more risky it is for Archera to underwrite. Once we understand the "risk" of commitment relative to the customer, the insured coverage allowance is determined on a per resource basis. At the extreme, where a customer is covering something that we would classify as extremely risky (e.g. going above 100% coverage on low survival rate services or resources), the platform actually won't even offer insured commitments. The risk is too great to justify, both from our perspective and from a customer savings perspective. If the customer wishes to cover lower risk resources, larger allowances are generally given. At an intermediate level of risk, they will have an allowance which lets them take on some insured commitments. ## Is There Flexibility Beyond What the Core Engine Provides? Archera, similar to an insurer, is always open to creating custom "policies" and insured commitments based on requests — and the underlying risk — on an ad hoc basis via a conversation with our customer success and underwriting teams that you can request via the App or email. ## Related Resources * [What are Guaranteed Commitments?](/help-center/guaranteed-commitments/what-are-guaranteed-commitments.md) * [How Does Guaranteed Commitment Pricing Work?](/help-center/guaranteed-commitments/pricing.md) * [What are Guaranteed Commitment allowances?](/help-center/guaranteed-commitments/allowances.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.archera.ai/help-center/faq/how-archera-underwrites.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.