AI/ML workloads

Inference, training, notebooks

Run AI workloads on our GPUs or yours. Deploy across AWS, GCP, Azure, Oracle, CoreWeave, or any Kubernetes cluster you bring.

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Same delightful DevEx

GPU workloads as first-class citizens

Select a GPU type, deploy your container, and start training or serving models. The same, consistent experience across AWS, GCP, Azure, Oracle, CoreWeave, and any neocloud.

So fast you might miss it

Northflank handles GPU attachment, CUDA driver setup, and cluster provisioning. Your container gets immediate GPU access without infrastructure setup.

Same delightful experience

GPUs are just another resource in Northflank. Use the same UI, CLI, and API for CPU and GPU workloads. Same Git integration, CI/CD, and monitoring.

Works with any Kubernetes cluster

Deploy on AWS EKS, GCP GKE, Azure AKS, Oracle OKE, CoreWeave, Civo, or any managed Kubernetes with GPU nodes. Northflank abstracts cloud differences so you get the same workflows across all providers.

Multi-cloud deployments

Run AI workloads across different hyperscalers & neo-clouds. Aggregate your GPUs easily, switch providers based on GPU availability, pricing, or latency. Without vendor lock-in, consistent experience everywhere.

Every AI workload supported

From training to inference to agents

If it runs in a container and needs a GPU, Northflank runs it. LLM training and fine-tuning, real-time inference APIs, batch processing, computer vision, video generation, STT, TTS, agents, and notebooks.

LLM training & fine-tuning

Train foundation models on multi-GPU clusters or fine-tune any open weight model on your data. Support for PyTorch, DeepSpeed, FSDP, and Ray. Checkpoint management and experiment tracking built-in.

Real-time inference APIs

Deploy production inference endpoints for text generation, embeddings, or chat completions. Auto-scaling from zero to hundreds of GPUs based on traffic. Sub-100ms latency with automatic batching and model caching.

Computer vision & image generation

Run object detection, image classification, segmentation models, or diffusion models for image generation. Deploy Stable Diffusion, DALL-E variants, or custom vision models with GPU acceleration.

Video processing & generation

Process video streams, generate synthetic video, or run video understanding models. Transcode at scale, apply ML filters in real-time, or generate video from text prompts with GPU-accelerated pipelines.

Code generation & autonomous agents

Run code-generation models, autonomous agents executing tasks, or multi-step reasoning systems. Deploy GitHub Copilot alternatives, coding assistants, or agent frameworks with dedicated GPU resources.

Interactive notebooks & experiments

Spin up Jupyter notebooks with GPU access for rapid experimentation. Perfect for data science teams iterating on models. Start and stop on-demand, paying only when actively using GPUs.

On-demand, no commit

Scale to 1,000s of GPUs

Choose from NVIDIA H100, H200, B200, A100, L4, L40S, TPUs, and more. Select the right GPU for your workload and budget. Provision in minutes, pay per-second, scale elastically. No waitlists, no long-term contracts, no infrastructure engineering.

Latest NVIDIA & AMD hardware

Deploy on GB300, B200, H100, H200, A100, or L4 for inference, fine-tuning, and training. 18+ GPU types across performance tiers and price points.

Per-second billing, instant provisioning

Pay only for GPU seconds used, no hourly minimums. Provision GPUs in minutes, not months. Perfect for bursty workloads, experiments, and production deployments. Scale from 1 to 1,000 GPUs on-demand.

GPU time-slicing for efficiency

Run multiple workloads on single GPUs with NVIDIA time-slicing. Share expensive H100s across development, testing, and small inference workloads. Improve utilization by 2-4x, reduce costs dramatically.

Testimonials from Sentry

From the front lines

Northflank is way easier than gluing a bunch of tools together to spin up apps and databases. It’s the ideal platform to deploy containers in our cloud account, avoiding the brain damage of big cloud and Kubernetes. It’s more powerful and flexible than traditional PaaS – all within our VPC. Northflank has become a go-to way to deploy workloads at Sentry.

David Cramer

Co-Founder and CPO @ Sentry

Read a testimonial from Sentry

Everything where you expect it to be

Optimized for developer happiness

We designed every part of Northflank with one goal: simplify complex infrastructure and give developers the controls they need. So they can focus on what they do best: building.

Built for scale

You’re in good company

Since 2019, teams have used Northflank to run everything from enterprise products to high-scale AI infrastructure. Whether it’s one container or one thousand, Northflank holds the line.

Millions

Containers

130B+

Requests processed

$24M+

Raised in funding

50k+

Developers in production

330+

Availability zones

Git-based workflows for ML models

CI/CD that understands GPU workloads

Push code to Git and deploy automatically to GPUs. We handle Docker builds, provisioning, and deployment. Preview environments for model experiments, gradual rollouts for inference APIs, rollbacks when needed.

Deploy ML models from Git

Use Git and CI/CD with Dockerfiles for container-based DevEx in model development, training, and production. Or use Northflank’s SSH and volumes for remote development and experiments. Version control for models, data pipelines, and deployment configs.

Integrated storage for datasets

Co-locate GPU compute with managed databases, S3-compatible object storage, and persistent volumes. Low-latency data access for training. Store model weights, checkpoints, and datasets alongside compute.

Auto-scaling

Inference endpoints scale from zero to hundreds of GPUs based on traffic. Handle viral traffic spikes automatically. Scale down during quiet periods to minimize costs. Intelligent queuing prevents request drops.

Observability

Real-time GPU utilization, memory usage, and temperature monitoring. Track inference latency, throughput, and costs per workload. Integrated logging for training runs and debugging inference issues.

Testimonials from Pebblebed