Infrastructure Capacity Planning
“Which servers will exceed 90% CPU utilization in the next 7 days?”
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A real-world example
Which servers will exceed 90% CPU utilization in the next 7 days?
A single capacity-related outage costs $100K–$500K per hour in lost revenue and SLA penalties. Most teams provision based on peak historical usage plus a 30% buffer, wasting $2–5M annually in over-provisioned infrastructure. If you could predict which specific servers will breach capacity limits next week, you could auto-scale proactively — eliminating both outages and waste.
How KumoRFM solves this
Relational intelligence for every forecast
Kumo models the full infrastructure graph — servers connected to clusters, request patterns, deployment schedules, and dependent services. A traditional threshold alert fires when CPU is already at 85%. Kumo predicts which servers will breach 90% seven days from now by learning from cross-cluster traffic patterns, deployment cadences, and correlated workload spikes. Server SRV-401 may look fine today, but Kumo sees that its cluster is absorbing traffic from a scaling neighbor and a new deployment is scheduled for Thursday.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
SERVERS
| server_id | cluster | instance_type | region |
|---|---|---|---|
| SRV-401 | prod-api-east | m5.2xlarge | us-east-1 |
| SRV-402 | prod-api-east | m5.2xlarge | us-east-1 |
| SRV-510 | prod-ml-west | p3.8xlarge | us-west-2 |
USAGE_METRICS
| metric_id | server_id | cpu_percent | memory_percent | timestamp |
|---|---|---|---|---|
| M-80001 | SRV-401 | 72.4 | 61.2 | 2025-09-15 14:00 |
| M-80002 | SRV-402 | 45.1 | 38.7 | 2025-09-15 14:00 |
| M-80003 | SRV-510 | 81.9 | 74.3 | 2025-09-15 14:00 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT MAX(USAGE_METRICS.CPU_PERCENT, 0, 7, days) > 90 FOR EACH SERVERS.SERVER_ID
Prediction output
Every entity gets a score, updated continuously
| SERVER_ID | TIMESTAMP | TARGET_PRED | True_PROB |
|---|---|---|---|
| SRV-401 | 2025-09-22 | True | 0.92 |
| SRV-402 | 2025-09-22 | False | 0.15 |
| SRV-510 | 2025-09-22 | True | 0.87 |
Understand why
Every prediction includes feature attributions — no black boxes
Server SRV-401 (prod-api-east)
Predicted: 92% probability of exceeding 90% CPU in 7 days
Top contributing features
CPU trend (7d slope)
+4.2%/day
32% attribution
Memory-CPU correlation
0.89
23% attribution
Cluster load (peer servers)
78% avg
20% attribution
Request growth rate
+18%/week
15% attribution
Scheduled deployment
Thursday
10% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: Prevent capacity outages 7 days before they happen and reclaim $2–5M annually in over-provisioned infrastructure.
Related use cases
Explore more forecasting use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
