Deploy models on powerful A100s & A40s GPUs in minutes with usage-based billing that actually makes sense.
# 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
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
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
No DevOps expertise required. Deploy models as quickly as you'd deploy a website.
Securely upload your model files (ONNX, PyTorch, etc.) or link your Git repository.
Select your desired Vultr GPU (A100, A40...), region, and basic settings via our UI or API.
Get your unique API endpoint instantly. Monitor performance and scale resources as needed.
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}")
Everything you need for hassle-free AI deployments at your fingertips.
Go from code/model to live API endpoint in minutes. UI, API, and Git-based deployments supported.
Access high-performance NVIDIA GPUs on Vultr (A100, A40, and more) optimized for inference.
Clean APIs, clear documentation, and intuitive CLI designed for seamless integration into your workflow.
Easily upload, version, and manage your models through our intuitive registry interface.
Real-time logs and performance metrics (GPU/CPU/RAM usage) out-of-the-box powered by Prometheus & Grafana.
Leverage Vultr's global infrastructure footprint for low-latency deployments closer to your users.
No complex calculations, no hidden fees. Just straightforward, usage-based billing you can actually understand.
Pay only for the GPU instance time you consume, billed per second with clear hourly/monthly caps.
Generous bandwidth included with all instances. Overage fees (if any) are clearly stated upfront.
Track your GPU hours and estimated costs directly within your dashboard.
Securely managed via Stripe. Access invoices and manage payments easily.
Start and stop instances via API/UI anytime with no penalties.
Most cloud providers would add $100+ in hidden bandwidth and operation fees. With Iris AI, what you see is what you pay.
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 |
You need to quickly deploy ML models without DevOps overhead. You want to focus on writing code, not managing infrastructure.
You're building AI-powered features on a budget. You need cost predictability without compromising on performance.
You're focused on model performance, not K8s administration. You need to iterate quickly and deploy often.
You need a cost-effective and transparent GPU inference solution that your entire team can use without specialized training.
Enterprise-grade technology that powers your AI deployments.
We handle the infrastructure complexity so you don't have to.
We're just getting started! Here's a glimpse into our future plans.
Can't find the answer you're looking for?
Contact our teamJoin the waitlist today and be first in line when we launch. Early adopters receive 100 free GPU hours and priority support.
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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."
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