AI Deployment Without The Headaches

Deploy models on powerful A100s & A40s GPUs in minutes with usage-based billing that actually makes sense.

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856 developers already waiting
Live Demo
# Deploy an AI model in just one command
$ iris deploy --model gpt2 --gpu a100 --region nyc

✓ Model uploaded successfully
✓ Environment configured
✓ GPU instance provisioned (A100)
✓ Deployment complete in 37 seconds

Your API is live at: https://api.irisai.dev/gpt2-c7f9a
# That's it! Start making inference requests right away

The Old Way vs The Iris Way

The Old Way

Days of configuration wrangling with Kubernetes, YAML files, and complex infrastructure setup

Unexpected costs with hidden fees for data transfer, storage, and operations

DevOps nightmares dealing with security, scaling, and maintenance

Complex workflows requiring specialized knowledge and constant monitoring

Complex deployment diagram
"Just a few more configuration files to debug..."

The Iris Way

Minutes to deploy with simple UI or single command deployment

Predictable pricing with transparent per-hour GPU billing and no hidden fees

Developer joy focusing on your models instead of infrastructure

Simple integration with instant API endpoints and clear documentation

Simple Iris deployment flow
"That was surprisingly easy!"

Get Your Model Live in 3 Simple Steps

No DevOps expertise required. Deploy models as quickly as you'd deploy a website.

1

Upload or Connect

Securely upload your model files (ONNX, PyTorch, etc.) or link your Git repository.

Estimated time: 30 seconds
Supports all major ML frameworks
2

Configure & Launch

Select your desired Vultr GPU (A100, A40...), region, and basic settings via our UI or API.

Estimated time: 45 seconds
Simple slider controls for resource allocation
3

Integrate & Scale

Get your unique API endpoint instantly. Monitor performance and scale resources as needed.

Estimated time: 10 seconds
RESTful API with clear documentation

Deploy via API in one request

import iris

# Initialize client
client = iris.Client(api_key="YOUR_API_KEY")

# Deploy model
deployment = client.deploy(
    model_path="./models/my_model.pt",  # Local path or Git URL
    gpu_type="a100",                    # A100, A40, or others
    region="nyc",                       # Available regions
    scaling={                           # Optional scaling config
        "min_instances": 1,
        "max_instances": 5,
    }
)

# Get deployment info
print(f"Model deployed! API endpoint: {deployment.api_url}")
print(f"Estimated hourly cost: ${deployment.hourly_cost}")

Built for Developers, Powered by Performance

Everything you need for hassle-free AI deployments at your fingertips.

Ship Faster

Go from code/model to live API endpoint in minutes. UI, API, and Git-based deployments supported.

Compute Power When You Need It

Access high-performance NVIDIA GPUs on Vultr (A100, A40, and more) optimized for inference.

Built By Developers, For Developers

Clean APIs, clear documentation, and intuitive CLI designed for seamless integration into your workflow.

Simplified Model Management

Easily upload, version, and manage your models through our intuitive registry interface.

Integrated Monitoring

Real-time logs and performance metrics (GPU/CPU/RAM usage) out-of-the-box powered by Prometheus & Grafana.

Global & Reliable

Leverage Vultr's global infrastructure footprint for low-latency deployments closer to your users.

We Hated Surprise Bills Too

No complex calculations, no hidden fees. Just straightforward, usage-based billing you can actually understand.

Clear Per-Hour Rates

Pay only for the GPU instance time you consume, billed per second with clear hourly/monthly caps.

No Hidden Data Fees

Generous bandwidth included with all instances. Overage fees (if any) are clearly stated upfront.

Real-time Usage Monitoring

Track your GPU hours and estimated costs directly within your dashboard.

Simple Billing Integration

Securely managed via Stripe. Access invoices and manage payments easily.

No Setup or Cancellation Fees

Start and stop instances via API/UI anytime with no penalties.

Get Transparent Pricing

Cost Estimator

1h 8h 24h
Hourly cost: $1.50
Daily cost (8h): $12.00
Monthly estimate: $360.00

Most cloud providers would add $100+ in hidden bandwidth and operation fees. With Iris AI, what you see is what you pay.

How do we compare?

Iris AI Traditional Cloud AI PaaS
Deployment Time Minutes Days to Weeks Hours to Days
DevOps Required No Yes Sometimes
Hidden Fees None Many Some
API Control Full Limited Limited
Developer Focus High Low Medium

Iris AI Is Perfect For You If You Are...

Developer

You need to quickly deploy ML models without DevOps overhead. You want to focus on writing code, not managing infrastructure.

"I just want to deploy my model and get an API endpoint without becoming a Kubernetes expert."

Startup

You're building AI-powered features on a budget. You need cost predictability without compromising on performance.

"We got a shocking cloud bill last month. We need transparent pricing that scales with our usage."

ML Engineer

You're focused on model performance, not K8s administration. You need to iterate quickly and deploy often.

"I spend too much time configuring deployment pipelines instead of improving my models."

Team

You need a cost-effective and transparent GPU inference solution that your entire team can use without specialized training.

"We need to deploy multiple AI services without hiring a dedicated DevOps engineer."

What Early Users Are Saying

Developers like you are already experiencing the difference.

Sarah L.

ML Engineer @ AI Startup

"I deployed our recommendation model in under 5 minutes. The transparent pricing alone made it worth switching from our previous setup."

Michael R.

CTO @ Tech Startup

"Our cloud bills were unpredictable until we found Iris AI. Now we know exactly what we're paying for GPU inference without surprises."

Alex K.

Full-Stack Developer

"As someone who doesn't specialize in ML ops, Iris AI was a game-changer. I integrated our vision model into our app the same day."

TRUSTED BY INNOVATIVE TEAMS AT

Built on Powerful, Reliable Foundations

Enterprise-grade technology that powers your AI deployments.

Compute

  • Vultr High-Performance GPUs
  • Docker Containerization
  • Global CDN Distribution

Stack

  • Node.js & Go Backend
  • React/Next.js Frontend
  • PostgreSQL Database

Monitoring

  • Prometheus Metrics
  • Grafana Dashboards
  • Structured Logging

We handle the infrastructure complexity so you don't have to.

What's Next for Iris AI

We're just getting started! Here's a glimpse into our future plans.

Q2 2025

Initial Launch

  • • Core deployment platform
  • • Basic monitoring and logging
  • • RESTful API and documentation

Q3 2025

Advanced Features

  • • Support for Model Fine-Tuning & Training Workloads
  • • Advanced Scaling Options (e.g., Scale-to-Zero)
  • • CLI & Language-Specific SDKs

Q4 2025

Ecosystem Growth

  • • Integrated Model Hub/Marketplace
  • • GitOps Deployment Workflows
  • • Team Collaboration Features

2026 and Beyond

Enterprise Expansion

  • • Enterprise security features
  • • Multi-region deployment orchestration
  • • Advanced analytics and optimization

Frequently Asked Questions

What makes Iris AI different from other GPU cloud providers?

What ML frameworks are supported?

How does the pricing work?

When will Iris AI be available?

Is there a free tier or trial available?

Can't find the answer you're looking for?

Contact our team

Ready for Effortless AI Deployment?

Join the waitlist today and be first in line when we launch. Early adopters receive 100 free GPU hours and priority support.

Limited spots available for early access

Share on Twitter/LinkedIn when approved to get bumped to the front of the line.

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856

Developers waiting

37

Days until launch

100

Free GPU hours