Shipping an AI feature shouldn't start with provisioning GPUs. Explore the NevTan AI Model Catalog and you can take a ready-to-deploy model — an open-source LLM, an embedding model, a speech model — and launch it on managed GPU infrastructure in a few clicks.
No servers to configure, no inference stack to babysit. Just production-ready endpoints that scale with your traffic, so you spend your time building the product instead of the plumbing.
Table of Contents
What Is the NevTan AI Model Catalog?
Why Use the NevTan AI Model Catalog?
AI Models You Can Explore
Key Features
Benefits for Developers
How to Deploy an AI Model
Common AI Use Cases
Why Developers Choose NevTan Cloud
Pricing & Scalability
Frequently Asked Questions
Final Thoughts
What Is the NevTan AI Model Catalog?
The NevTan AI Model Catalog is a centralized library of AI models you can deploy directly on NevTan Cloud, without building or managing the underlying infrastructure yourself. Think of it as a curated starting point for AI development: instead of hunting down model weights, writing serving code, and wrestling with GPU drivers, you browse the catalog, pick a model, and deploy it as a live inference endpoint.
Behind the scenes, NevTan Cloud handles the parts that usually eat weeks of engineering time. The platform provisions GPU resources, packages the model for serving, exposes it through an API endpoint, and keeps it running. That is what "managed AI infrastructure" actually means in practice — the operational burden of hosted AI models shifts from your team to the platform.
This matters because AI inference is deceptively hard to run well. A model that works perfectly in a notebook can fall over the moment real traffic hits it. Memory management, batching, cold starts, GPU utilization, failover — these are full-time problems. An AI model catalog paired with a managed deployment platform removes most of them from your plate.
Whether you are a solo developer prototyping a chatbot, a GenAI startup racing toward launch, or an enterprise team standardizing how models get shipped internally, the catalog gives you the same thing: a shorter path from "we should use AI for this" to a working endpoint in production.
If you already run applications on the NevTan Cloud App Platform, the model catalog fits naturally alongside it — your app and your AI inference layer live on the same cloud, which simplifies networking, billing, and operations.
Why Use the NevTan AI Model Catalog?
There are two ways to get an AI model into production. The first is the do-it-yourself route: rent GPU instances, install CUDA and an inference server, download model weights, write serving logic, configure load balancing, set up monitoring, and then maintain all of it forever. The second is to use a managed AI deployment platform. The catalog exists to make the second path genuinely fast.
Here is what that translates to day-to-day:
Deployment in clicks, not sprints. Selecting a model from the catalog and launching it takes minutes. The traditional route — sourcing hardware, configuring the serving stack, hardening it for production — routinely takes weeks.
No infrastructure management. You never SSH into a GPU box. NevTan Cloud manages the machines, the drivers, and the serving layer.
Managed GPU resources. GPU capacity is provisioned and optimized by the platform. You do not need to become an expert in GPU memory profiles to run an LLM.
Faster iteration. Want to swap Llama for Mistral and compare outputs? On a managed platform that is an afternoon experiment, not a re-architecture.
Scaling that follows your traffic. As demand grows, the platform scales inference capacity with it, instead of you scrambling to add nodes during a spike.
Less DevOps, more product. Every hour your team does not spend on inference plumbing is an hour spent on the features your users actually see.
The short version: the catalog turns AI deployment from an infrastructure project into a product decision.
AI Models You Can Explore
Model catalogs on managed AI platforms typically span several categories, each suited to different workloads. The table below shows the common categories and widely used open-source AI models you would expect to work with. Check the live model list on NevTan Cloud for the current, exact set of supported models — availability evolves as new models are released.
Model category | Common example models | Best for |
|---|---|---|
Large Language Models | Llama, Mistral, DeepSeek | Chatbots, assistants, text generation, reasoning |
Code Models | Code Llama, DeepSeek Coder | Coding assistants, code review, autocomplete |
Embedding Models | BGE, E5 | Semantic search, RAG, recommendations |
Vision Models | CLIP, LLaVA | Image understanding, classification, captioning |
Speech Models | Whisper | Transcription, voice interfaces, subtitling |
LLMs are the workhorses of generative AI. Open-source families like Llama, Mistral, and DeepSeek have closed much of the gap with proprietary models, and deploying open-source LLMs on your own cloud infrastructure gives you control over data flow that third-party APIs cannot match.
Embedding models rarely get the spotlight, but they power almost every serious RAG application. They convert text into vectors so you can search by meaning rather than keywords.
Code, vision, and speech models round out the catalog for teams building beyond pure text — developer tooling, image-aware applications, and voice products all start here.
If none of the off-the-shelf options fit your domain closely enough, the next step is usually fine-tuning rather than reaching for a bigger model — and fine-tuned models deploy through the same endpoint workflow as everything else in the catalog.
The practical advantage of a catalog is that all of these live in one place with one deployment workflow. You are not stitching together five different vendors to cover five modalities.
Key Features
One-Click Deployment
The headline feature. Pick a model, confirm your configuration, and deploy. The platform handles containerization, GPU allocation, and endpoint creation. What used to be a DevOps ticket becomes a button.
Managed Infrastructure
Every layer beneath your model — hardware, operating system, drivers, serving runtime — is operated by NevTan Cloud. Patches, updates, and hardware failures are the platform's problem, not yours.
GPU Acceleration
Modern AI models need GPUs to serve responses at usable speeds. The catalog runs models on GPU-backed model servers by default, so inference latency stays low without you tuning anything.
Automatic Scaling
Traffic to AI features is rarely flat. Auto scaling adjusts serving capacity as request volume rises and falls, which keeps latency stable during spikes and avoids paying for idle capacity during quiet periods.
API Endpoints
Every deployed model is exposed through an API endpoint. Your application talks to the model over standard HTTP, which means any language, any framework, any stack can integrate it. This is what makes the catalog useful for deploying AI APIs that other teams or products consume.
Monitoring
Deployments come with visibility into how your model is behaving — request volume, latency, and health — so you can spot problems before your users do.
Security
Models run inside NevTan Cloud's managed environment rather than on ad-hoc servers someone configured at 2 a.m. Access to endpoints is controlled, and your inference traffic stays within infrastructure you govern.
Version Management
Models improve constantly. A managed platform makes it practical to run a newer version alongside the old one, compare results, and switch over deliberately rather than crossing your fingers on upgrade day.
Reliability and High Availability
Production AI features cannot disappear when a single machine dies. Managed deployments are built to stay available, with the platform handling failover so an infrastructure hiccup does not become a customer-facing outage.
Benefits for Developers
Features describe the platform; benefits describe your week. Here is what actually changes when you move from self-hosted inference to the catalog:
You ship sooner. The gap between "idea" and "working demo" collapses from weeks to hours. For startups, that speed is often the whole ballgame.
You stop being an accidental SRE. ML engineers routinely lose half their time to infrastructure. A managed AI platform gives that time back.
You experiment more. When trying a new model costs minutes instead of days, you actually try them. Better experimentation leads to better products.
Your costs become predictable. GPU infrastructure managed by a platform, scaled to actual usage, is easier to budget than idle self-hosted hardware.
Juniors can do senior work. A student or early-career developer can deploy the same production-grade LLM endpoint a specialist would — the platform absorbs the expertise gap.
Production is not a rewrite. Because deployments are production-ready from the start, the prototype you demoed is architecturally the same thing you launch.
How to Deploy an AI Model
Here is the general workflow for going from zero to a live inference endpoint on NevTan Cloud. If the steps in the console differ slightly, follow the on-screen flow — this is the standard shape of it.
Create a NevTan Cloud account. Sign up and log in to the console. This is also where your existing NevTan Cloud services live if you are already a user.
Open the AI Model Catalog. Browse the available models by category — LLMs, embeddings, vision, speech — and review the details of any model you are considering.
Choose your model. Match the model to your use case. Building a chatbot? Start with an instruction-tuned LLM. Building search? You want an embedding model.
Configure the deployment. Set your deployment options, such as the resources allocated to the model. Sensible defaults mean you can accept the standard configuration and refine later.
Launch. Deploy the model. The platform provisions GPU capacity and spins up your endpoint.
Grab your API endpoint. Once the deployment is live, you get an endpoint URL. Point your application at it and start sending requests.
Scale as you grow. As usage increases, scale the deployment to match. Because scaling is managed, this is a configuration change rather than an infrastructure project.
That is the whole loop. No CUDA installs, no serving framework debates, no capacity planning spreadsheets.
→ Start Building on NevTan Cloud
Common AI Use Cases
What do teams actually build once deploying a model takes minutes? These are the patterns we see most:
AI chatbots. Deploy an LLM, wrap it in your product's context, and you have a conversational interface for support, onboarding, or sales.
Customer support automation. Route, draft, and resolve support tickets with an LLM that understands your documentation.
RAG applications. Pair an embedding model with an LLM to build assistants that answer from your data — the single most requested GenAI architecture in enterprises right now.
AI agents. Multi-step workflows where a model plans, calls tools, and executes tasks. Reliable, low-latency inference endpoints are the foundation.
Code generation. Internal coding assistants built on code models, tuned to your team's standards and repositories.
Content creation. Drafting product descriptions, marketing copy, and documentation at scale.
Semantic search. Replace brittle keyword search with meaning-based retrieval across your knowledge base or product catalog.
Enterprise AI and internal tools. HR assistants, report summarizers, meeting-notes processors — the unglamorous internal tools that quietly save thousands of hours.
Knowledge base assistants. Let employees or customers ask questions in plain language and get answers grounded in your docs.
The common thread: every one of these needs a model running somewhere reliable. The catalog is the "somewhere."
Why Developers Choose NevTan Cloud
The honest comparison is not NevTan Cloud versus other managed platforms — it is managed deployment versus doing it all yourself. Here is how that stacks up:
Feature | NevTan Cloud | Traditional self-hosting |
|---|---|---|
Deployment time | Minutes via the catalog | Days to weeks of setup |
Infrastructure management | Handled by the platform | Your team's responsibility |
GPU management | Provisioned and managed for you | Manual procurement, drivers, tuning |
Maintenance | Platform-managed updates and patches | Ongoing engineering overhead |
Scaling | Managed, adjusts with demand | Manual capacity planning |
Model updates | Deploy new versions from the catalog | Manual download, test, redeploy |
Cost efficiency | Resources scaled to actual usage | Idle hardware costs during low traffic |
Ease of use | Console-driven, beginner-friendly | Requires deep MLOps expertise |
Production readiness | Built in from day one | Must be engineered and hardened |
Self-hosting still makes sense in a few situations — highly specialized hardware requirements, or regulatory setups that mandate on-premises everything. For most teams building most AI products, though, the math favors managed deployment decisively. The engineering hours saved compound every single month.
There is also a consolidation benefit. If NevTan Cloud already runs your applications through the App Platform, adding AI inference on the same cloud means one vendor, one console, one bill, and no cross-cloud networking headaches between your app and your model.
Pricing & Scalability
AI infrastructure costs come down to one question: are you paying for capacity or for usage? Self-hosted GPU servers charge you around the clock whether requests are flowing or not. Managed deployment on NevTan Cloud is designed to align cost with actual workload — resources are provisioned for what you run, and scaling adjusts capacity as your traffic changes rather than forcing you to over-provision for the worst case.
For current plans and rates, visit the NevTan Cloud pricing page. Inference pricing and GPU instance pricing are broken out separately in the docs, and usage is metered through AI credits so you can attribute spend per workload.
As a rule of thumb when you are estimating: factor in not just the raw compute, but the engineering time you will not spend on infrastructure — and the GPU capacity you are not paying for while idle. For most teams, those second and third numbers are the bigger ones.
On scalability: the platform is built so that the same deployment workflow covers a weekend prototype and a production service. You do not graduate off the platform as you grow — you scale the deployment up.
What Makes NevTan Cloud Different
Managed AI infrastructure, end to end. From GPU provisioning to API endpoints, the operational layer is the platform's job.
Faster deployments. The catalog compresses the model-to-endpoint timeline from weeks to minutes.
Simplified AI operations. Monitoring, scaling, and availability are built into the deployment, not bolted on afterward.
Reduced DevOps complexity. No inference servers to run, no GPU fleet to maintain, no 3 a.m. pages about a crashed serving process.
Developer-friendly workflows. Console-driven deployment plus standard API endpoints means any developer can ship AI features, not just MLOps specialists.
One cloud for apps and AI. Deploy your application and your models on the same platform and skip the multi-vendor integration tax.
Built for developers, startups, and enterprises looking for scalable AI deployment — from a student shipping a first RAG project to an engineering org standardizing how models reach production. If you are evaluating where your AI workloads should live, the fastest way to decide is to deploy one model and see how the workflow feels.
Frequently Asked Questions
What is the NevTan AI Model Catalog?
It is a centralized library of AI models available on NevTan Cloud that you can deploy as managed, GPU-backed inference endpoints — without setting up or maintaining any infrastructure yourself.
Which AI models can I deploy?
The catalog covers the major model categories used in modern AI applications: large language models, code models, embedding models, vision models, and speech models. Check the live model list in the NevTan Cloud console for the current set of supported models.
Can I deploy open-source LLMs?
Yes — deploying open-source LLMs is the catalog's core use case. Running open models on managed infrastructure gives you the flexibility of open source without the operational burden of self-hosting.
Do I need GPU expertise?
No. GPU provisioning, drivers, and optimization are handled by the platform. You choose a model and a configuration; NevTan Cloud handles the hardware.
Is it suitable for production workloads?
Yes. Deployments are designed to be production-ready from launch, with managed scaling, monitoring, and high availability built in — so your prototype and your production service share the same architecture.
How does scaling work?
Capacity adjusts with your traffic. As request volume grows, the platform scales your deployment to maintain performance; when traffic drops, you are not stuck paying for a fleet of idle GPUs.
Can I deploy AI APIs for my applications?
Every deployed model is exposed as an API endpoint over standard HTTP. Any application, in any language, can integrate it — which also makes the catalog a practical backend for internal AI APIs consumed by multiple teams.
Is the infrastructure fully managed by NevTan Cloud?
Yes. Servers, GPUs, the serving runtime, updates, and availability are all managed by the platform. Your responsibility starts and ends at the API endpoint.
Final Thoughts
The hardest part of AI development was never choosing a model — it was everything required to run one reliably. That is the problem the catalog solves. Explore the available models, pick the one that fits your use case, and you can have a production-grade inference endpoint serving requests before your coffee goes cold.
Open-source models keep getting better. The teams that win with them will be the ones who can deploy, test, and iterate fastest — and speed is exactly what managed AI deployment buys you.
