Custom AI API hosting means we deploy and run your AI or machine learning model as a secure, scalable web API, handling the servers, GPUs, scaling, security and monitoring, so your applications can call the model without your team managing infrastructure.
Shipping AI features should not require running GPU servers. We host your custom or fine-tuned models behind secure, client-specific API endpoints that scale automatically with demand and come with monitoring and uptime commitments.
Deliver AI features as secure, on-demand services. At Prominent AI Services LLP, we deliver AI API hosting end to end, from discovery and data preparation to deployment, monitoring and support, so your teams get dependable results quickly and securely.
Deliver AI features as secure, on-demand services.
Ideal for: SaaS, Startups, Agencies. We start with a short onboarding to understand your data and goals, configure the service, agree service levels and then deliver continuous updates, reports and improvements under a transparent monthly plan.
Models are packaged in Docker containers and served with FastAPI, TorchServe, NVIDIA Triton or vLLM for large language models. We host on managed Kubernetes, serverless GPU services or virtual machines in Azure, AWS or Google Cloud, behind an API gateway with authentication, rate limiting and usage tracking. Monitoring covers latency, errors, cost and model quality (see MLOps).
Choose a managed hosting plan where we run the API for you, or a one-time deployment into your own cloud account that your team then operates. Related services include model as a service, cloud AI integration and model optimization to reduce running costs. You can also hire DevOps engineers.
Have a model that needs a home? Tell us about it and your expected traffic, and we will propose a hosting plan. Start your AI journey.
Common questions about our Custom AI API Hosting services.
Yes. Hosting plans include agreed service levels, monitoring and incident response.
Yes. Each endpoint uses its own keys, rate limits and network rules for isolation.
Yes. We host and optimise open-source models such as LLaMA and Mistral for private, cost-effective inference.
There are two parts: infrastructure (compute, GPUs, storage and bandwidth, which depend on traffic) and our management fee. We estimate both from your expected traffic before you commit.
Yes. We can deploy into your Azure, AWS or Google Cloud account so you keep ownership and billing, or host it in an environment we manage for you.
A model that is ready for production can usually be packaged and deployed within one to three weeks, depending on security reviews and integration testing.
Yes. Autoscaling adds capacity as requests increase. For GPU models we also use request batching and caching so the service stays responsive during peaks.
