Uncensored AI inference for adult platforms.

Most hosted model APIs apply blanket policy filters that reject lawful adult prompts, silently degrade outputs, or change behavior without warning. For an adult product, that is an unreliable dependency: the model you shipped is not the model you get, and your roadmap is gated by another company's policy team.

AdultInfra gives you inference infrastructure you control. Deploy supported open-source or custom models on GPU capacity, keep control of versions and routing, and decide product policy yourself within the law. Prohibited and illegal content stays prohibited; lawful adult workloads are not filtered out.

Interactive versus batch

Interactive companion chat, in-session personalization, and real-time generation need low-latency smart-routed inference. Batch tagging, embeddings, and video analysis fit asynchronous GPU pipelines better. Match the infrastructure to the task instead of forcing every model onto one path.

Serverless and dedicated

Use serverless GPU inference for bursty or unpredictable demand, and dedicated GPU capacity for steady high-volume workloads. Autoscaling, regional placement, and capacity separation by latency, data sensitivity, and utilization keep cost and performance predictable.

Accelerator classes matter

Not every workload wants the same silicon. Inference-optimized parts are the right default for high-throughput serving and interactive chat; larger flagship accelerators are for training, fine-tuning, and heavy generation. Virtual GPU fits shared or bursty serving, while bare-metal GPU and spot bare-metal GPU fit sustained, latency-sensitive, or cost-sensitive jobs. Match the class to the workload and keep the deployment portable so a model can move between them without a rewrite.

Serve inference close to the viewer

Interactive adult products (companion chat, in-session personalization, real-time generation) live or die on round-trip latency. Routing inference through a global edge with anycast and smart routing, and placing deployments regionally, keeps requests off long-haul paths and supports data-residency choices. For batch tagging, embeddings, and video analysis, the same platform can run asynchronous GPU pipelines where throughput, not latency, is the objective. Keep customer weights, prompts, and data private by default, with no shared-model leakage between tenants.

More than a model endpoint

Inference depends on storage, queues, networking, and delivery. Keep original media and datasets in object storage, process asynchronously through queues, and serve generated assets through the same global edge used for the rest of the platform.

OpenAI-compatible endpoints

Deployed models expose an HTTPS endpoint that implements the OpenAI API: POST /v1/chat/completions and GET /v1/models. Existing OpenAI SDKs work by pointing base_url at your endpoint. Supported parameters include model, messages, max_tokens, temperature (0–2), top_p, stream (server-sent events), and stop. Authentication is optional and uses an API key header when enabled.

Bring your own container

Deploy any Docker image that meets the registry and runtime requirements, from a public or private registry. First-class recipes cover vLLM (tensor parallelism, prefix caching, maximum context length, and served model names) and Diffusers for image generation. Each model is versioned and routed per deployment, so you can run multiple versions side by side.

Model catalogue

A managed catalogue covers text LLMs, text-and-image, text-and-audio, embeddings, rerankers, image generation, speech recognition, text-to-speech, video super-resolution, and safety classifiers. You can also serve your own fine-tunes or private models from the same control plane.

GPU classes

Accelerators include H200 (141 GB), H100 (80 GB), A100 (80 GB), L40S, B300, and GB300, in single-GPU through eight-GPU configurations with NVLink or InfiniBand interconnect. Virtual GPU flavours range from CPU-only instances to 1×, 2×, and 4× L40S, H100, or A100.

Serverless, virtual, and bare metal

Serverless inference autoscales on CPU and GPU utilization with configurable minimum and maximum replicas, cooldown, and pod lifetime, and routes across up to six regions. Virtual GPU fits bursty serving. Bare-metal GPU fits sustained, latency-sensitive workloads and scales to hundreds of nodes. Spot bare-metal GPU reduces cost by up to about 50% and is preemptable with 24 hours' notice. Health checks and automatic failover keep endpoints available through node or model failure.

Billing

Inference is billed per minute of compute used; there is no per-token pricing. GPU capacity is billed per GPU-hour. Indicative virtual-GPU rates start at about €1.08/hour for a single L40S and €1.53/hour for a single H100, with final pricing confirmed in your proposal. New accounts start with a zero quota that is raised on request, typically within minutes.

Availability

Inference runs across regions in the Americas, EMEA, and APAC, with regional placement for data-residency requirements. GPU and cloud services carry published availability commitments, and inference is covered by the platform service commitments confirmed in your proposal. See the service commitments for the current figures.

Adult-capable inference is infrastructure, not a licence. AdultInfra does not decide what is lawful for your market, does not verify age or identity, and does not assume your editorial or legal responsibility.

Customers remain responsible for age assurance, consent and rights records, likeness and model rights, provenance and labelling of generated media, content moderation, reporting, takedowns, and compliance with applicable law. AdultInfra can help integrate specialist providers for verification, consent, payments, and moderation workflows.

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