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Quick answer: CloudK connects to your AWS, GCP, or Azure account in 5 minutes and immediately identifies where you're burning runway — idle GPU instances, oversized EC2s, forgotten S3 buckets. AI startups save an average of 38% on their cloud bill in the first month. No DevOps engineer, no AWS expertise required. Free to start.
It's not negligence — it's velocity. Moving fast creates cloud waste. The problem is you don't find out until the AWS invoice arrives.
You spun up a p3.8xlarge for a training run on Tuesday. It's still running Saturday. That's ~$2,400 gone.
A misconfigured distributed training job runs 10× longer than expected. You find out when the invoice arrives.
Every engineer has an EC2 dev box. Half of them haven't been touched in 3 weeks but they're all still billing.
AWS Cost Explorer requires expertise to interpret. By the time you understand what happened, the damage is done.
Link your AWS, GCP, or Azure account with a read-only IAM role. CloudK never touches your infrastructure without approval.
CloudK surfaces idle GPU instances, oversized compute, forgotten storage, and underutilized databases — ranked by cost impact.
Each recommendation is one click to implement. Automatic backup created first. 24-hour rollback if anything looks wrong.
Budget alerts, anomaly detection, and weekly cost digest emails mean you're never surprised by your cloud bill again.
Not generic FinOps tooling — features that match AI workloads.
Real-time alerts when GPU instances are running with < 5% utilization. Catch forgotten training jobs before they drain runway.
CloudK identifies which of your training and batch workloads are safe to run on Spot — saving 60–90% vs on-demand.
Instant Slack or email alert if daily spend increases more than your threshold. Know about runaway jobs in minutes, not weeks.
See every development EC2, notebook instance, and RDS database that hasn't been used in the past N days. One-click stop.
Training datasets, model checkpoints, and experiment artifacts accumulate fast. CloudK surfaces old buckets and unused storage.
AWS for inference, GCP for TPU training, Azure for enterprise clients? Monitor all three from one dashboard.
Yes. CloudK supports AWS, Google Cloud (GCP), and Microsoft Azure. Many AI startups use GCP for TPU training and AWS for inference — CloudK shows both in one unified dashboard with consolidated cost reporting.
Especially at pre-revenue stage. Every dollar saved on cloud extends your runway. CloudK is free to start and typically identifies more in savings in the first week than its monthly subscription cost.
Yes. CloudK monitors SageMaker training jobs, notebook instances, and endpoints. It alerts you when notebook instances are idle, identifies oversized training configurations, and tracks cumulative training costs per job type.
AWS Cost Explorer shows you historical data after the fact. CloudK gives you real-time anomaly detection, plain-English recommendations, one-click optimizations, and 24-hour rollback safety — plus Azure and GCP support. Most AI founders find AWS Cost Explorer too technical to act on without weeks of learning.