Use Your Cloud Data to Predict What Happens Next
Cloud predictive analytics applies ML models to your operational and business data to forecast capacity needs, predict failures, identify cost trends before they become budget problems, and optimize resource allocation — turning historical data into forward-looking intelligence.
What RLM Delivers on Cloud Predictive Analytics
Reactive cloud operations — responding to problems after they occur — is expensive and disruptive. Predictive analytics shifts operations from reactive to proactive, enabling interventions before incidents and optimizations before waste accumulates.
How We Approach Cloud Predictive Analytics
Every Cloud Predictive Analytics engagement starts with what you have today and ends with something running in production — with independent evaluation in between.
Use Case Identification & Data Assessment
We identify the highest-value predictive analytics use cases for your cloud environment — capacity forecasting, cost prediction, performance trend analysis, failure prediction — and assess the data quality and availability required for each.
Analytics Platform Evaluation
We evaluate cloud-native and third-party predictive analytics platforms — AWS Forecast, Azure Machine Learning, GCP Vertex AI, Datadog forecasting, and specialized FinOps platforms — against your specific use cases and data architecture.
Forecasting Model Design
We design the forecasting models for your priority use cases — feature engineering, training data requirements, model architecture, and the accuracy validation methodology that confirms model quality.
Operational Integration
Predictive insights only create value when they're integrated into operational workflows. We design the integration between predictive analytics outputs and the operational processes — capacity planning, budget forecasting, incident prevention — that acts on them.
Cloud Predictive Analytics Evaluation Criteria
What follows is the Cloud Predictive Analytics evaluation checklist we actually use — the criteria that predict outcomes rather than demo well.
Forecast Accuracy & Horizon
Evaluate forecast accuracy (MAPE, RMSE) on your actual historical data for the specific time horizon relevant to your use case — capacity planning requires weekly/monthly forecasts; performance prediction may need sub-hour horizons.
Seasonality & Pattern Handling
Cloud workloads often have complex seasonal patterns — day-of-week, time-of-day, month-end, and event-driven spikes. Evaluate whether the forecasting approach captures these patterns accurately.
Data Volume Requirements
ML forecasting models require sufficient historical data to generalize reliably. Evaluate minimum data requirements against your data availability — particularly for newer services or recently migrated workloads.
Integration with FinOps & Budgeting
Cost forecasting should integrate with your FinOps platform and budget management processes. Evaluate how prediction outputs connect to reservation purchasing decisions and budget alerts.
Actionable Recommendations
Forecasts that require manual interpretation to produce action recommendations add analyst overhead. Evaluate whether the platform generates specific, actionable recommendations alongside forecasts.
Feedback & Model Improvement
Forecast accuracy improves with feedback — actual outcomes informing model updates. Evaluate the feedback loop mechanism and how quickly models incorporate corrections to improve future accuracy.
"Our migration was stalled for months. RLM came in, assessed the gaps, and helped us select a managed services partner that got us across the finish line in 60 days."
Independent means we will tell you when the answer is to keep what you have.
Where This Matters Most
Sector-specific considerations we see repeatedly in cloud and managed services engagements.
A Sample of the Cloud & Managed Services Providers We Evaluate
RLM is vendor neutral. These are among 600+ providers in our evaluation set — inclusion here is not an endorsement, and we are paid by the provider you choose, not by any provider in particular. How that works →
Ready to Get Cloud Predictive Analytics Right?
Start with a no-cost conversation with an RLM cloud advisor — vendor neutral, no agenda, just clarity on the right path forward.
Talk to a Cloud Advisor