Best AI Infrastructure Platforms for Visual Studio Code

Compare the Top AI Infrastructure Platforms that integrate with Visual Studio Code as of November 2025

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Visual Studio Code AI Infrastructure Clear Filters

This a list of AI Infrastructure platforms that integrate with Visual Studio Code. Use the filters on the left to add additional filters for products that have integrations with Visual Studio Code. View the products that work with Visual Studio Code in the table below.

What are AI Infrastructure Platforms for Visual Studio Code?

An AI infrastructure platform is a system that provides infrastructure, compute, tools, and components for the development, training, testing, deployment, and maintenance of artificial intelligence models and applications. It usually features automated model building pipelines, support for large data sets, integration with popular software development environments, tools for distributed training stacks, and the ability to access cloud APIs. By leveraging such an infrastructure platform, developers can easily create end-to-end solutions where data can be collected efficiently and models can be quickly trained in parallel on distributed hardware. The use of such platforms enables a fast development cycle that helps companies get their products to market quickly. Compare and read user reviews of the best AI Infrastructure platforms for Visual Studio Code currently available using the table below. This list is updated regularly.

1

VESSL AI

VESSL AI

Build, train, and deploy models faster at scale with fully managed infrastructure, tools, and workflows. Deploy custom AI & LLMs on any infrastructure in seconds and scale inference with ease. Handle your most demanding tasks with batch job scheduling, only paying with per-second billing. Optimize costs with GPU usage, spot instances, and built-in automatic failover. Train with a single command with YAML, simplifying complex infrastructure setups. Automatically scale up workers during high traffic and scale down to zero during inactivity. Deploy cutting-edge models with persistent endpoints in a serverless environment, optimizing resource usage. Monitor system and inference metrics in real-time, including worker count, GPU utilization, latency, and throughput. Efficiently conduct A/B testing by splitting traffic among multiple models for evaluation.

Starting Price: $100 + compute/month

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2

Azure Machine Learning

Microsoft

Accelerate the end-to-end machine learning lifecycle. Empower developers and data scientists with a wide range of productive experiences for building, training, and deploying machine learning models faster. Accelerate time to market and foster team collaboration with industry-leading MLOps—DevOps for machine learning. Innovate on a secure, trusted platform, designed for responsible ML. Productivity for all skill levels, with code-first and drag-and-drop designer, and automated machine learning. Robust MLOps capabilities that integrate with existing DevOps processes and help manage the complete ML lifecycle. Responsible ML capabilities – understand models with interpretability and fairness, protect data with differential privacy and confidential computing, and control the ML lifecycle with audit trials and datasheets. Best-in-class support for open-source frameworks and languages including MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R.

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3

JarvisLabs.ai

JarvisLabs.ai

We have set up all the infrastructure, computing, and software (Cuda, Frameworks) required for you to train and deploy your favorite deep-learning models. You can spin up GPU/CPU-powered instances directly from your browser or automate it through our Python API.

Starting Price: $1,440 per month

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4

Thunder Compute

Thunder Compute

Thunder Compute is a cloud platform that virtualizes GPUs over TCP, allowing developers to scale from CPU-only machines to GPU clusters with a single command. By tricking computers into thinking they're directly attached to GPUs located elsewhere, Thunder Compute enables CPU-only machines to behave as if they have dedicated GPUs, while the physical GPUs are actually shared among several machines. This approach improves GPU utilization and reduces costs by allowing multiple workloads to run on a single GPU with dynamic memory sharing. Developers can start by building and debugging on a CPU-only machine and then scale to a massive GPU cluster with just one command, eliminating the need for extensive configuration and reducing the costs associated with paying for idle compute resources during development. Thunder Compute offers on-demand access to GPUs like NVIDIA T4, A100 40GB, and A100 80GB, with competitive rates and high-speed networking.

Starting Price: $0.27 per hour

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