The GPU Cloud for AI Developers

Develop, train, and deploy AI models faster than ever. No code changes needed.

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Reproducible Dev Environments

Provision a cloud GPU machine with your environment, code, and SSH keys ready to go.
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Connect to it through SSH or your favorite IDE, or open it inside a Jupyter notebook.

~/project/dev.yaml
resources:
  accelerators: A100:1
workdir: ~/my_project
setup:
  pip install -r requirements.txt
>_
komo machine launch ~/project/dev.yaml --name dev
~/project/train.yaml
resources:
  accelerators: A100:8
workdir: ~/my_project
setup:
  pip install -r requirements.txt
run:
  python train.py --ngpus 8
>_
komo job launch ~/project/train.yaml

Serverless Jobs

Launch batch jobs for tasks such as training, fine-tuning, and data processing. Easily scale to multiple nodes for distributed jobs.
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Once your job completes, the cloud instances will automatically be terminated. Never pay for idle instances ever again.

Infinitely Scalable Models

Deploy AI models behind a safe and secure endpoint. With built-in load balancing and autoscaling, your models scale with traffic, ensuring you only pay for the compute you need.
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Use the serving framework of your choice (vLLM, Triton, etc.) for maximum flexibility.

~/project/serve.yaml
resources:
  accelerators: A100:1
  ports: 8000
workdir: ~/my_project
envs:
  HF_TOKEN: MY_TOKEN
setup:
  pip install -r requirements.txt
run:
  python -u -m vllm.entrypoints.openai.api_server \
    --port 8000 \
    --model meta-llama/Meta-Llama-3-8B-Instruct \
    ---trust-remote-code \
    --gpu-memory-utilization 0.95 \
    --max-num-seqs 64
service:
  replica_policy:
    min_replicas: 1
    max_replicas: 3
    target_qps_per_replica: 5
  
  # An actual request for readiness probe.
  readiness_probe:
    initial_delay_seconds: 1800
    path: /v1/chat/completions
    post_data:
      model: meta-llama/Meta-Llama-3-8B-Instruct
      messages:
        - role: user
          content: Hello! What is your name?
      max_tokens: 1
>_
komo service launch ~/project/serve.yaml --name llama3
AWS
GCP
Azure
Kubernetes

Multi-Cloud Execution

Use the Komodo Cloud or bring your own cloud account to consume your cloud credits and keep your data within your own private VPC.
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With built-in Kubernetes support, you can seamlessly overflow from your on-prem cluster to the cloud.