The Complete Canadian AI Stack: Inference API + GPU Instances + Object Storage on Nebula Block
If you're building an AI product in Canada, you've probably hit this problem: your inference API lives in the US, your GPU rental is on a US hyperscaler, and your storage bucket sits wherever egress pricing was cheapest that month.
Each piece works fine on its own. Then a compliance officer, a client contract, or a Quebec regulator asks the one question that actually matters: where does the data go, and who can access it?
That's the gap Canadian AI infrastructure providers like Nebula Block are built to close. Inference, compute, and storage all live under one Canadian roof — so your architecture diagram and your compliance diagram finally match.
Why Data Residency Matters for AI Teams
Cross-border data flows aren't just a legal footnote. For healthcare, finance, and legal teams, they're often the difference between shipping a product and stalling in a privacy review for weeks.
When your stack spans three vendors in three jurisdictions, "where's our data" becomes a multi-vendor flowchart. When it's one provider, one account, and one data residency story, it's a single sentence.
Learn more about data residency requirements in Canada
Inference API: Serverless Power Without the Infra Overhead
For teams that don't want to manage base operating systems or complex network configurations, Nebula Block offers a fully managed, OpenAI-compatible Inference API. You can route your production traffic to frontier open-source models (such as Llama 3.1 or Stable Diffusion XL) using standard API calls—with the guarantee that every query and token is processed entirely on Canadian soil.
It strikes the perfect balance: the plug-and-play speed of serverless endpoints, combined with strict sovereign data governance. Because it is fully OpenAI-compatible, you can drop our API into your existing codebase using standard SDKs, getting you into production instantly without giving up control over your data residency.
GPU Infrastructure for AI Workloads
Nebula Block provides Canadian-hosted GPU infrastructure for organizations building and deploying AI applications. Rather than managing large on-premise clusters, teams can lease dedicated GPU capacity on flexible hourly or monthly terms and run their own inference, training, or fine-tuning workloads.
The platform is designed for organizations that require data residency and infrastructure within Canada. By keeping compute and data inside Canadian jurisdiction, enterprises can simplify compliance with privacy frameworks such as PIPEDA and Quebec's Law 25 while maintaining greater control over sensitive AI workloads.
Whether you're serving production LLMs, running retrieval-augmented generation (RAG) pipelines, fine-tuning open-source models, or supporting internal AI applications, dedicated GPU infrastructure provides the flexibility to deploy the frameworks and models that best fit your workload—without being tied to a managed inference platform.
This approach is particularly well suited for Canadian enterprises, public sector organizations, regulated industries, and AI teams that require sovereign infrastructure alongside high-performance GPU compute.
GPU Instances: When You Need the Hardware Yourself
Not every workload fits a managed endpoint. Fine-tuning a custom model, running an embedding model with an odd configuration, or hosting something outside the standard catalogue — these need dedicated hardware.
Nebula Block's GPU Instances give you virtual machines, containers, and bare-metal servers built on NVIDIA H100, H200, A100, L40S, RTX A6000, and RTX 4090 cards. You pick the card, OS image, and region at deploy time.
Instances support SSH key or password authentication, and they sit behind firewalls and security groups — so the access model feels like any other cloud VM, not something exotic. Billing is pay-as-you-go, based on hardware and duration.
If you're running a private RAG pipeline, a fine-tuning job, or a model that has to stay inside your own network perimeter, this is the layer where that happens. You get root access. You decide what runs on the box.
Why Your Privacy Impact Assessment Needs a GPU Section Now
Object Storage: The Connective Tissue
Object Storage ties the whole stack together, and it's fully S3-compatible. Boto3, the AWS CLI, and tools like Cyberduck all work against it without modification — which matters, because nearly every ML tool you already use (training frameworks, dataset libraries, model registries) speaks S3 natively.
In practice, this is where training datasets, model checkpoints, and pipeline assets live between jobs. A GPU instance mounts data from a bucket at the start of a training run, then pushes checkpoints back at the end. No copying files across providers or regions.
Buckets can be public or private, depending on the use case:
- Public: for assets served directly, like model files behind a CDN-style URL
- Private: for restricted data, with signed URLs for temporary access
Combined with GPU instances and the inference API, this closes the loop. Data lands in storage, gets processed on a GPU instance, and optionally gets served back out through the inference API — while the model weights sit in the same storage layer the entire time.
Why the Full Stack Beats Individual Pieces
Plenty of providers sell GPU instances. Plenty sell S3-compatible storage. What's harder to find is both under one jurisdiction, one account, and one support relationship with a data residency story that actually holds up under a strict privacy impact assessment.
That's the real value of a Canadian AI infrastructure stack like this one. Scale your heavy lifting onto secure GPU Instances, and keep your data gravity in Object Storage the whole time. It's not a more complicated stack than the alternative. It's the same modern stack — minus the part where you have to explain to a regulator why your enterprise data crossed a border to get processed.
Learn more at
- Email: contact@nebulablock.com
- Website: nebulablock.com
- Docs: docs.nebulablock.com
- Book a call: nebulablock.com/contact