Usage Forecasting
“What will this account's compute usage be next month?”
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A real-world example
What will this account's compute usage be next month?
Usage-based SaaS companies must forecast compute consumption for both infrastructure planning and revenue recognition. A cloud platform serving 2,000 accounts where usage estimates are off by 25% either over-provisions $8M in infrastructure or faces capacity crunches that degrade service for top accounts. Billing surprises from unpredictable usage also drive 18% of customer complaints. The usage trajectory depends on feature adoption, team growth, seasonal patterns, and the account's own business cycles.
How KumoRFM solves this
Graph-learned product intelligence across your entire account base
Kumo connects accounts, daily usage, feature events, billing history, and alert patterns into a temporal graph. It learns that accounts that recently adopted the batch-processing feature show a 3x usage spike in weeks 2-4 before stabilizing. The model captures seasonality (e-commerce accounts spike in Q4), growth trajectories (accounts adding 5+ users per month accelerate usage), and cross-account infrastructure patterns (when a shared cluster's accounts all grow simultaneously, capacity planning must account for contention).
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
ACCOUNTS
| account_id | plan | industry | active_users | contract_type |
|---|---|---|---|---|
| ACC401 | Usage-based | E-commerce | 85 | Pay-as-you-go |
| ACC402 | Usage-based | Finance | 30 | Committed |
| ACC403 | Usage-based | Technology | 120 | Pay-as-you-go |
USAGE_DAILY
| usage_id | account_id | date | compute_hours | storage_gb | api_calls |
|---|---|---|---|---|---|
| UD01 | ACC401 | 2025-03-01 | 450 | 2800 | 125,000 |
| UD02 | ACC401 | 2025-03-02 | 480 | 2850 | 132,000 |
| UD03 | ACC402 | 2025-03-01 | 120 | 500 | 28,000 |
FEATURES
| feature_id | account_id | feature | enabled_date | usage_30d |
|---|---|---|---|---|
| FT01 | ACC401 | Batch processing | 2025-02-15 | High |
| FT02 | ACC401 | Real-time API | 2024-12-01 | High |
| FT03 | ACC402 | Batch processing | 2025-03-01 | New |
BILLING
| billing_id | account_id | month | amount | vs_estimate |
|---|---|---|---|---|
| BL401 | ACC401 | 2025-02 | $12,400 | +18% |
| BL402 | ACC401 | 2025-01 | $10,200 | +8% |
| BL403 | ACC402 | 2025-02 | $3,100 | -5% |
ALERTS
| alert_id | account_id | type | timestamp | threshold_pct |
|---|---|---|---|---|
| AL01 | ACC401 | Usage spike | 2025-02-20 | 150% |
| AL02 | ACC403 | Quota warning | 2025-03-01 | 90% |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT SUM(USAGE_DAILY.COMPUTE_HOURS, 0, 30, days) FOR EACH ACCOUNTS.ACCOUNT_ID
Prediction output
Every entity gets a score, updated continuously
| ACCOUNT_ID | INDUSTRY | LAST_MONTH_HOURS | PREDICTED_NEXT_MONTH |
|---|---|---|---|
| ACC401 | E-commerce | 12,800 | 16,200 |
| ACC402 | Finance | 3,400 | 5,800 |
| ACC403 | Technology | 8,900 | 9,100 |
Understand why
Every prediction includes feature attributions — no black boxes
Account ACC402 -- Finance, 30 active users
Predicted: 5,800 compute hours predicted next month (+71%)
Top contributing features
New batch processing adoption
Enabled 2 days ago
32% attribution
Similar-account batch adoption curve
3x spike, weeks 2-4
24% attribution
User growth rate (30d)
+5 new users
18% attribution
Historical monthly growth
+12% MoM avg
14% attribution
Committed tier buffer
80% of committed used
12% 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: A usage-based SaaS platform serving 2,000 accounts that improves usage forecasting accuracy from 75% to 92% saves $8M in infrastructure over-provisioning and eliminates billing surprises that drive 18% of churn-related complaints. Kumo captures feature-adoption usage curves and cross-account seasonality that time-series models on individual accounts cannot learn.
Related use cases
Explore more B2B SaaS 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.
