How Canadian and global AI teams are using SScoreCompute to ship faster, spend less and scale their AI workloads.
A Toronto-based AI agent startup was running their LLM inference stack on a major US cloud provider, paying in USD with significant FX overhead. Migrating to SScoreCompute H200 instances cut their monthly spend by 77% while maintaining the same throughput for their multi-agent pipeline.
"Switching to SScoreCompute was the single best infrastructure decision we made this year. CAD billing alone saved us $18K in FX fees."
CTO · AI AGENT STARTUP · TORONTOA Vancouver health tech company needed to train a medical imaging classification model on 2 million radiology scans. Using SScoreCompute B300 GPUs on AWS ca-central-1, they completed training in 4 hours — down from an estimated 3 days on their previous CPU-based cloud setup. Data stayed in Canada throughout.
"We needed Canadian data residency for PIPEDA compliance AND fast GPU compute. SScoreCompute was the only provider that could give us both."
HEAD OF ML · HEALTH TECH · VANCOUVERA university AI research lab was burning through their annual compute budget on reserved cloud instances they didn't always need. Switching to SScoreCompute's pay-per-hour H100 model let them spin up large GPU clusters for experiments and shut them down immediately after — cutting their annual compute spend by 60%.
"We only pay for the GPU hours we actually use. For academic research with unpredictable compute needs, SScoreCompute's model is perfect."
RESEARCH DIRECTOR · UNIVERSITÉ DE MONTRÉAL