Radiant

A specialized AI infrastructure provider that combines high-end NVIDIA GPU availability with managed Kubernetes, filling the gap between raw compute providers and high-level model APIs.

Best for engineering teams scaling LLM inference and fine-tuning who need Kubernetes-level control without the overhead of managing bare-metal GPU clusters.

Gpu Mlops Inference Fine-Tuning Cloud Platform Kubernetes Ai Development Developer Tools

Analysis based on product data, pricing structure, traffic signals, and public user sentiment.

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Who Should Use Radiant?

Typical users

MLOps engineers, backend developers at AI startups, and enterprise data science teams moving from research to production.

Maturity fit

scaling to advanced

Choose this if…

You need immediate access to NVIDIA H100 or A100 GPUs without long-term cloud contracts.
Your workflow is already containerized and you want a managed Kubernetes environment optimized for AI.
You want to pay only for active compute via serverless GPU inference rather than idle reserved instances.

Skip this if…

You are a beginner looking for a simple 'no-code' AI wrapper.
Your application requires deep integration with a vast ecosystem of non-AI managed services like those in AWS or GCP.
You have no experience with Docker or Kubernetes orchestration.

About Radiant

Radiant is an AI-native cloud platform designed to simplify the deployment and scaling of machine learning models. It provides a performance-optimized environment that bridges the gap between low-level hardware management and high-level model hosting.

What it actually does

The platform offers on-demand access to NVIDIA-accelerated compute through two primary paths: serverless GPU inference for variable workloads and dedicated Kubernetes clusters for persistent, high-throughput tasks. It handles the underlying infrastructure orchestration, allowing developers to deploy models via API or containerized environments.

What makes it different

Unlike general-purpose clouds, Radiant focuses exclusively on the AI stack, offering 'Serverless Kubernetes' which allows for auto-scaling GPU workloads without managing the control plane. It prioritizes high-interconnect speeds and GPU availability, which are often bottlenecks on larger, legacy cloud providers.

Serverless GPU inference Managed Radiant Kubernetes Service (RKS) On-demand NVIDIA H100/A100/L40S access Automated model fine-tuning workflows Global edge deployment for low latency Private networking and VPC peering Integrated resource monitoring and logging

Key Features

Serverless GPU Scaling

Automatically scales compute resources to zero when not in use to minimize costs.

Radiant Kubernetes Service (RKS)

A managed environment that removes the complexity of setting up K8s for ML workloads.

High-Performance Interconnects

Optimized networking between GPU nodes to accelerate distributed training and inference.

Fine-tuning API

A streamlined interface to adapt open-source models like Llama or Mistral to proprietary datasets.

Instant Provisioning

Rapid spin-up times for GPU instances compared to traditional cloud providers.

Enterprise Security

Includes features like SOC2 compliance and dedicated private infrastructure options.

Unified Dashboard

Single interface to manage compute, storage, and model deployments across regions.

Pricing

Serverless Inference

Pay-as-you-go per second/token

Scale to zero
Shared GPU pools
Standard support
API-based access

Popular

On-Demand GPUs

Variable hourly

Dedicated NVIDIA instances
Full Kubernetes control
Customizable storage
No long-term commitment

Reserved Instances

Custom monthly/yearly

Guaranteed capacity
Significant hourly discounts
Dedicated account manager
Custom networking configurations

Pricing checked 4 months ago

Pricing guidance

Best plan for most users: The On-Demand GPU plan is the best starting point for most scaling startups, providing a balance of dedicated performance and cost flexibility.

Free plan enough? No — Radiant typically does not offer a permanent free tier, though they often provide trial credits for new developers to test the infrastructure.

Upgrade when:

When production traffic becomes consistent enough that reserved instances are cheaper than on-demand.
When you need private networking or VPC peering for security compliance.
When moving from single-model testing to multi-node distributed training.

Watch out for:

Minimum spend requirements may apply for certain high-end GPU reservations.
Egress data costs can vary significantly depending on the volume of model weights being moved.
Availability of specific GPU types (like H100s) can still be subject to short-term 'spot' fluctuations.

Competitive pricing that undercuts major clouds on high-end GPU hourly rates, positioned as a premium performance-first provider.

Pros & Cons

Strengths

Superior GPU Availability
Often has stock of high-demand NVIDIA chips (H100s) when major providers like AWS or Azure are sold out or require enterprise agreements.
Kubernetes-Native Workflow
Allows DevOps teams to use familiar tools like Helm and kubectl, making it easier to integrate into existing CI/CD pipelines.
Cost-Effective Serverless Options
The ability to scale to zero is a significant advantage for startups with unpredictable traffic, preventing massive bills for idle hardware.
Optimized for LLMs
The infrastructure is specifically tuned for large language model requirements, including high memory bandwidth and low-latency serving.

Weaknesses

Limited Managed Services
Does not offer the breadth of auxiliary services (managed databases, auth, pub/sub) found in general clouds, requiring users to stitch these in from elsewhere.

Affects: Full-stack developers wanting a single-vendor solution
Technical Barrier to Entry
Requires a solid understanding of containerization and orchestration; it is not a 'one-click' deployment tool for non-technical users.

Affects: Small teams without dedicated DevOps or MLOps resources
Regional Footprint
While growing, their data center locations are fewer than the 'Big Three' clouds, which may impact latency for specific geographic user bases.

Affects: Global applications requiring ultra-low latency in niche regions

Real User Sentiment

Users generally view Radiant as a reliable, high-performance alternative for GPU compute with a developer-friendly Kubernetes approach.

Users tend to like

Fast provisioning of high-end GPUs
Clean Kubernetes integration
Responsive technical support
Transparent pricing compared to legacy clouds

Users commonly complain about

Documentation can be sparse for advanced networking setups
Dashboard UI is functional but basic
Occasional capacity constraints in popular regions

Recurring tradeoffs

You trade the convenience of a fully managed ecosystem (like AWS) for better GPU availability and lower specialized compute costs.

Happiest users

ML engineers who want to manage their own clusters via Kubernetes without the headache of hardware maintenance.

Often frustrated

Developers looking for a simple 'Heroku for AI' experience who find the Kubernetes requirements too complex.

Use Cases

LLM Inference

Scaling production-grade inference for models like Llama 3 with auto-scaling to manage costs.

Model Fine-tuning

Running periodic jobs to adapt open-source models to specific domain data using dedicated A100 clusters.

Batch Processing

Utilizing serverless GPUs to process large datasets through computer vision or NLP models without maintaining idle servers.

AI Research

Quickly spinning up high-performance H100 nodes for short-term experimental training runs.

Multi-tenant AI Apps

Using Kubernetes namespaces to isolate compute resources for different end-customers in a SaaS environment.

Frequently Asked Questions

How does Radiant pricing compare to AWS or GCP?

Radiant typically offers lower hourly rates for high-end GPUs like the A100 and H100, often by 20-30%, because they lack the massive overhead of general-purpose clouds. Additionally, their serverless GPU option can be significantly cheaper for intermittent workloads that would otherwise require a 24/7 reserved instance on AWS.

Do I need to know Kubernetes to use Radiant?

While Radiant offers a Fine-tuning API and serverless inference that abstract some complexity, their core power lies in the Radiant Kubernetes Service (RKS). To get the most out of the platform, especially for custom deployments, a working knowledge of Docker and Kubernetes is highly recommended.

Can I reserve GPUs for long-term projects?

Yes, Radiant offers reserved instance pricing for 1-year or 3-year commitments. This is the most cost-effective way to secure H100 or A100 capacity for consistent, high-volume production workloads, providing a significant discount over on-demand rates.

What GPUs are currently available on Radiant?

Radiant focuses on modern NVIDIA hardware, typically offering H100, A100 (80GB), and L40S GPUs. Availability can vary by region, but they are known for having better stock of these high-demand chips than larger, more congested cloud providers.

Does Radiant provide managed databases or storage?

Radiant provides high-performance block storage and object storage optimized for model weights and datasets. However, they do not offer managed relational databases (like RDS) or NoSQL services. Most users pair Radiant's compute with external databases from providers like MongoDB Atlas or Pinecone.

Is there a free trial or free tier?

Radiant does not have a permanent free tier. They typically operate on a credit-based system where new users can apply for trial credits to test the infrastructure before committing to spend. You must usually provide a valid business email and use case to qualify for these credits.

Why trust this page?

This evaluation combines product positioning, pricing analysis, traffic and market signals, and public user sentiment into a single decision-support page. Content is generated editorially — not copied from the vendor's website.

Funding & Company

Founded

2017

Stage

Late stage

Total Raised

$297.5M

Latest Round

Series B (May 2024)

Notable Investors

Brookfield Asset Management Tailwind Ventures Molten Ventures Episode 1 Ventures

Radiant has raised nearly $300 million, positioning it as a financially robust player in the AI infrastructure market. The company's funding is distinguished by a significant strategic investment from Brookfield Asset Management, a major global infrastructure firm, ensuring substantial long-term stability and a utility-focused approach to growth.

Full funding report medium confidence

Market Signals & Traffic

Estimated visits, global rank, geography, traffic sources, monthly visit trends, and organic search keywords (Similarweb)—on a dedicated page built for depth and search.

Estimated visits: 0
Global rank: —
Snapshot: Apr 2026
Traffic trend: —