--- title: "Custom and open-source models" description: "Deploy open-source and custom-container models for adult platforms: versioning, routing, capacity, usage-based metering, and customer-controlled content policy." url: "https://adultinfra.com/docs/ai/custom-models/" dateModified: "2026-09-12" --- [Home](https://adultinfra.com/) / [Docs](https://adultinfra.com/docs/) / AI and GPU # Custom and open-source models Deploy open-source and custom-container models for adult platforms: versioning, routing, capacity, usage-based metering, and customer-controlled content policy. LAST REVIEWED 2026-09-13 ON THIS PAGE - [Open-source and custom containers](https://adultinfra.com/docs/ai/custom-models/#open-source-and-custom-containers) - [Versioning and routing](https://adultinfra.com/docs/ai/custom-models/#versioning-and-routing) - [Capacity](https://adultinfra.com/docs/ai/custom-models/#capacity) - [Usage-based metering](https://adultinfra.com/docs/ai/custom-models/#usage-based-metering) - [Customer-controlled policy](https://adultinfra.com/docs/ai/custom-models/#customer-controlled-policy) - [Where AdultInfra fits](https://adultinfra.com/docs/ai/custom-models/#where-adultinfra-fits) A **custom model** is any model you supply rather than one drawn from a fixed catalogue: an open-source checkpoint you fine-tuned, a container that wraps your own preprocessing and inference code, or a model you built in-house. Deploying your own model is how an adult platform gets behaviour that general-purpose endpoints will not provide, and how it keeps its content policy under its own control. ## Open-source and custom containers Two deployment shapes cover most needs: - **Open-source checkpoints:** you supply weights and a runtime configuration. Good for standard architectures and for models the platform can serve from a known runtime. - **Custom containers:** you supply a container image that starts a server and answers inference requests. Use this when you need custom preprocessing, a non-standard runtime, or glue logic bundled with the model. A custom container should read its configuration from the environment, expose a health endpoint, and shut down cleanly on signal. Keep the image immutable and pinned by digest so a redeploy does not silently change the code behind a version. ## Versioning and routing Treat every model as a versioned artifact. A version is a tuple of weights and runtime configuration; changing either produces a new version, never an in-place mutation of a live endpoint. This lets you: - Roll forward with a **weighted canary** and compare output or latency before full promotion. - Roll back instantly by routing back to the previous version. - Pin a tenant to a specific version when reproducibility matters. Route by model name and version, and keep old versions addressable until you are certain they are unused. ## Capacity Model size drives memory and concurrency. Plan capacity from the largest model that must be resident at once, not the average, and account for warm-up: a cold container pays a load cost before it can serve. Practical controls: - **Concurrency limits** per endpoint to prevent a traffic spike from thrashing memory. - **Warm pools** so latency-sensitive endpoints are not cold on the first request. - **Queueing with timeouts** so overload degrades predictably instead of failing randomly. - **Separate pools** for interactive and batch work, so bulk jobs cannot starve live traffic. Scale on a signal that reflects load (queue depth or concurrent requests, not raw request count) and set a ceiling so autoscaling cannot run away. ## Usage-based metering Inference is commonly metered by usage rather than by a flat reservation, because consumption is bursty and model-dependent. Metering typically tracks compute time, tokens, or requests per model and version, so a customer can see which models drive consumption and attribute it to a workload. Keep metering per tenant and per endpoint so chargeback and capacity planning use the same numbers. This documentation describes the model; it does not cover account or billing mechanics. ## Customer-controlled policy The platform runs the model; the customer defines the policy. Concretely, that means: - No platform-wide refusal of lawful adult prompts for custom models. - Input and output filtering, if any, is configured by the customer, not imposed. - Safety behaviour lives in the model, the prompt, or a customer-supplied guardrail container, all of which the customer controls and can audit. This is a responsibility boundary, not an abdication: age, consent, likeness, rights, and applicable law remain the customer’s obligations. The platform guarantees only that it will not override lawful use with an undisclosed blanket filter. ## Where AdultInfra fits AdultInfra deploys open-source and custom-container models with private weights, versioned routing, and metered usage, so adult platforms can run the behaviour they need without fighting a default policy. To move a workload off a refusing provider, start with [adult AI inference](https://adultinfra.com/adult-ai-inference/) and bring one model. **Need this configured for your platform?** [Get a test plan](https://adultinfra.com/contact/) and an engineer will map the resource, cache, and delivery design to your workload. ```json {"@context":"https://schema.org","@graph":[{"@type":"TechArticle","headline":"Custom and open-source models","description":"Deploy open-source and custom-container models for adult platforms: versioning, routing, capacity, usage-based metering, and customer-controlled content policy.","url":"https://adultinfra.com/docs/ai/custom-models/","dateModified":"2026-09-13","keywords":"open source model hosting, custom container inference, uncensored LLM hosting, model versioning, usage-based inference","author":{"@id":"https://adultinfra.com/#organization"},"publisher":{"@id":"https://adultinfra.com/#organization"},"inLanguage":"en"},{"@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://adultinfra.com/"},{"@type":"ListItem","position":2,"name":"Documentation","item":"https://adultinfra.com/docs/"},{"@type":"ListItem","position":3,"name":"Custom and open-source models","item":"https://adultinfra.com/docs/ai/custom-models/"}]}]} {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://adultinfra.com/#organization","name":"AdultInfra","url":"https://adultinfra.com","logo":"https://adultinfra.com/favicon.svg","description":"Luxembourg-based specialist infrastructure for adult platforms.","slogan":"Already behind the adult internet.","areaServed":"Worldwide","knowsAbout":["Adult video CDN","Porn tube site infrastructure","Adult live-cam streaming","Creator subscription platforms","Byte-range video caching","Origin shielding","Player quality of experience","DDoS protection","GPU infrastructure","Adult-capable AI inference"],"location":{"@type":"Place","name":"Luxembourg","address":{"@type":"PostalAddress","addressCountry":"LU"}},"contactPoint":{"@type":"ContactPoint","contactType":"sales","url":"https://adultinfra.com/contact/"}},{"@type":"WebPage","@id":"https://adultinfra.com/docs/ai/custom-models/#webpage","url":"https://adultinfra.com/docs/ai/custom-models/","name":"Custom and open-source models","description":"Deploy open-source and custom-container models for adult platforms: versioning, routing, capacity, usage-based metering, and customer-controlled content policy.","isPartOf":{"@id":"https://adultinfra.com/#website"},"about":{"@id":"https://adultinfra.com/#organization"},"primaryImageOfPage":{"@type":"ImageObject","url":"https://adultinfra.com/og-default.png"},"inLanguage":"en","mainEntity":{"@id":"https://adultinfra.com/docs/ai/custom-models/#article"},"dateModified":"2026-09-13"},{"@type":"WebSite","@id":"https://adultinfra.com/#website","url":"https://adultinfra.com","name":"AdultInfra","description":"Luxembourg-based specialist infrastructure for adult platforms.","publisher":{"@id":"https://adultinfra.com/#organization"},"inLanguage":"en","potentialAction":{"@type":"ContactAction","target":"https://adultinfra.com/contact/"}}]} ```