Build AI on infrastructure you control, in the jurisdiction you choose.
AI labs, SaaS platforms, and enterprise teams run AI agents, retrieval pipelines, and vector databases on ZCP today, keeping training data and models in the region they select under a single accountable operator. GPU is available now for private cloud and bare metal; GPU on public cloud is on the roadmap.
GPU instances, coming soon
CPU and high-RAM VM instances are available today for AI agents, RAG pipelines, embeddings, and CPU-optimized inference. GPU-accelerated compute for large model training and high-throughput inference is on the roadmap. Join the waitlist.
Ship agent workloads today
High-memory instances run agent frameworks, embedding pipelines, and retrieval orchestration right now, with no GPU required for most agent workloads. You can move from idea to production without waiting on scarce accelerator capacity.
Your jurisdiction, your control
Training data and model weights stay in the region you select, under one named operator. That matters when export controls, customer contracts, or internal policy dictate exactly where sensitive AI development is allowed to run.
A foundation for retrieval and RAG
Fast persistent storage and high-memory instances run the leading vector databases at scale, with object storage for training data and model checkpoints, so your retrieval stack performs without exposing it to infrastructure you do not control.
GPU available now for serious training
GPU-optimized bare metal and private cloud are available today for large model training, fine-tuning, and high-throughput inference, with GPU on public cloud on the roadmap. Talk to us to scope a dedicated GPU build-out.
Common workloads
Where organizations like yours put ZCP to work.
AI agent hosting
Deploy multi-agent frameworks, LangChain, AutoGen, CrewAI, LlamaIndex, on high-RAM VM instances (up to 96 GB). Run agent orchestration, tool-calling loops, and memory systems on dedicated CPU compute. Available now.
Ollama on YUL Intel VMs
Run the Ollama Marketplace image on YUL-1 Intel compute. The ci2.4xl plan provides 16 vCPU, 64 GB RAM, and a 320 GB root disk, sized for CPU inference with large quantized models such as Llama 3.3 70B. Response speed depends on model size, context, and concurrency. GPU public-cloud VM inference is not currently available.
RAG pipeline infrastructure
Vector databases, document chunking, embedding pipelines, and retrieval APIs on dedicated, region-controlled compute. No shared-tenant resource contention. NVMe block storage for index persistence.
LLM inference, CPU-optimized
Run llama.cpp, Ollama, or vLLM with CPU-optimized models (Phi-3, Mistral 7B quantized, Gemma 2B) on high-core-count VM instances. Practical for agent sub-tasks, classification, and summarization at lower cost.
Embedding generation
Bulk embedding jobs on large-vCPU instances for indexing pipelines, semantic search, and document retrieval. Predictable CAD compute cost, no per-token billing, no shared quota limits.
MLOps and experiment tracking
Host MLflow, DVC, or custom experiment tracking on dedicated VMs with persistent NVMe storage. Keep training runs, metrics, and model artifacts under your control.
GPU training and large-model inference
GPU-optimized bare metal and private cloud are available now for large model training, LoRA fine-tuning, and high-throughput inference. GPU on public cloud VM instances is on the roadmap. Contact us to scope a GPU deployment.
Get started
Start building on
sovereign cloud today.
Eligible new accounts get CA$100 in promotional credit for 30 days. Spend CA$200 on eligible compute plans, then request another CA$200 in promotional credit, for up to CA$300 total. For prepaid accounts, a CA$1.00 verification payment is added to account credit and remains available. No long-term contract. Or talk to our team about a private cloud build-out.
Eligible new accounts only · Offer ends Dec 31, 2026 · Terms apply · ZSoftly Technologies Inc., Ottawa ON