The Problem
Distributed workloads often waste resources and money.
Spark, Ray, Kubernetes, Airflow, Dataproc, and ML workloads often waste cost due to idle clusters, inefficient job configs, resource overprovisioning, poor parallelism, and lack of architecture-level optimization.
The Solution
AI-powered optimization that understands your workloads.
OptiFlow analyzes workload metadata, logs, execution graphs, infrastructure configs, and cost signals to recommend performance and cost optimizations.
Architecture
How OptiFlow analyzes and optimizes your workloads.
Data Ingestion
Collect workload metadata, logs, and execution graphs.
Workload Analyzer
Parse and understand job configurations and resource usage.
AI Reasoning Module
LLM-powered analysis of optimization opportunities.
Cost Estimation Engine
Calculate cost impact of recommended changes.
Recommendation Engine
Generate actionable optimization recommendations.
Continuous Learning
Learn from applied optimizations and outcomes.
Reporting Dashboard
Visualize savings, trends, and optimization history.
Use Cases
Where OptiFlow delivers value.
Demo
See OptiFlow in action.
OptiFlow Architecture Walkthrough
Coming Soon
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