Development environments for AI teams in APAC.
Use Workspaces for fine-tuning, evaluation, and data preparation in-region, then ship to Brightnode Inference with one workflow.
Build in Workspaces, then deploy to inference.
Keep Workspaces focused on fine-tuning, evaluation, and APAC data preparation. Push production serving to Brightnode Router and dedicated endpoints.
Fine-tuning workspace
Llama and Mistral fine-tuning with preinstalled training toolchains.
PyTorch · Transformers · LoRA utilities · Brightnode SDK/CLI
Train checkpoint -> bn deploy --from-checkpoint -> serve on Brightnode Inference
Embedding pipeline workspace
Dataset cleanup, chunking, and embedding generation in-region.
Python · Vector tooling · Batch scripts · Brightnode SDK/CLI
Prepare and validate embeddings -> move directly to managed inference
Model evaluation suite
Run task-specific evals before promoting a model to production.
Evaluation harness · Prompt sets · A/B scripts · Brightnode SDK/CLI
Score model candidates -> deploy winning model to Brightnode endpoints
RAG development workspace
Build retrieval pipelines and test agent behavior against private data.
vLLM · Retrieval frameworks · Notebook tooling · Brightnode SDK/CLI
Prototype RAG -> productionize on Brightnode Router and dedicated endpoints
Workspace to inference, shown step-by-step.
Workspaces are the development stage. Inference is the production stage. The handoff should be obvious and fast.
Launch a GPU Workspace template in Singapore and run fine-tuning/evaluation jobs.
Use the Brightnode CLI to push your checkpoint from workspace to inference.
Expose a production endpoint through Router or dedicated capacity.
# from your workspace bn deploy --from-checkpoint ./output/checkpoint-final --name apac-model-prod
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Your Workload, Ready to Run
Pick a workload, we configure the environment. No GPU selection, no dependency hell, no waiting. Just deploy and build.
Log In & Select Workload
Sign up instantly with $100 credit, then choose from PyTorch, TensorFlow, ComfyUI, or vLLM templates.
Pick Region & GPU
Select Singapore or APAC region for low latency. Choose T4, L4, V100, A100 - all instantly available.
Deploy & Connect
One click deploy. In ~45 seconds your GPU is live with SSH, Jupyter, or web UI ready.
$100 FREE CREDIT INCLUDED • NO CREDIT CARD REQUIRED
Choose Your Workload. We Handle the Rest.
Pick what you want to run. We recommend the right GPU and pre-configure everything.
ComfyUI
AI image generation and creative workflows. Pre-configured with popular models and nodes.
Perfect for: Agencies, creators, marketing teams
Deploy ComfyUI
PyTorch
Model training, fine-tuning, and research workflows. Optimized for performance.
Perfect for: ML teams, researchers
Deploy PyTorch
Ubuntu CUDA
Custom workloads, full control
Perfect for: Advanced developers
Deploy Ubuntu CUDAAll workloads include pre-installed drivers, dependencies, and optimizations for APAC regions. Review all available GPU types
GPU tiers for every workload
We recommend the right GPU for your template. Pay per second, no hourly minimums, no long-term commit.
T4 / P4 / L4
16–24GB VRAM
From $0.50/hr
Available now
ComfyUI, small–medium LLMs, dev and light inference
A100 / V100
40–80GB VRAM
From ~$2–4/hr
Available now
vLLM, fine-tuning, 70B+ models, production inference
H100 / B200
80GB+ VRAM
On request
Capacity on request
Large training runs, maximum throughput, frontier models
Full pricing, pay per second, free egress within APAC.
From code to cloud
Deploy, scale, and run, without managing infrastructure. Everything you need in one workflow.
Launch in seconds
Pick a template (ComfyUI, vLLM, PyTorch, TensorFlow), we attach the right GPU and start the container. No provisioning tickets, no quota waits.
Persistent storage
Attach SSD volumes that survive restarts. Store models, datasets, and checkpoints without re-downloading. No egress fees within APAC.
APAC regions
Deploy in Singapore today; more regions coming. Low latency for you and your users in Southeast Asia and the wider APAC.
Bring your stack
Use our templates or bring your own Docker image. Full GPU access, SSH, Jupyter, or web UI, you choose the interface.
Workload FAQ
Common questions about GPU workloads, storage, and regions.
We support Python, Node, and any stack that runs in Docker. Our templates ship with PyTorch, TensorFlow, ComfyUI, vLLM, and Ubuntu CUDA. You can also deploy your own container with full GPU access.
More questions? Full FAQ or contact us.