Technology / Machine Learning Models
A model's cost doesn't stop when training finishes. Data pipelines, experiment tracking, feature stores, and serving infrastructure all keep accumulating spend long after the training run that got the headlines. Zolix tracks cost across the entire ML lifecycle - not just the GPU hours everyone already watches.
Data & storage
Feature stores, versioning
Experiments
MLflow, W&B artifacts
Training & serving
Batch vs. real-time inference
Cost tracked across the entire lifecycle, not just the GPU hours everyone already watches.
Read-only IAM roles
Cost-per-performance data
Spark & Databricks-aware
Most teams have a rough sense of what a training run costs. Far fewer can account for everything around it - and that's usually where the recoverable spend is hiding.
Training datasets routinely run into terabytes, and the cost of storing, versioning, and moving that data between environments compounds quietly. Feature stores in particular tend to retain far more historical data than any active model actually queries.
Every hyperparameter sweep, every abandoned architecture, every “just testing something” run leaves behind compute, storage, and logged artifacts in tools like MLflow or Weights & Biases. Individually small, collectively significant, and almost never cleaned up.
Not every training run is equally efficient per dollar. A model trained at high cost for a small accuracy gain is a different economic proposition than one that reaches similar performance for a fraction of the spend - but most teams don't have the cost-per-performance-point data to make that comparison.
Real-time inference infrastructure has to stay available continuously, while batch inference can run on a schedule against cheaper, non-guaranteed capacity. Plenty of production workloads are running as real-time when their actual latency requirements would tolerate batch processing at a fraction of the cost.
Models get retrained on a schedule that's often set once and never revisited - weekly, monthly, whatever seemed reasonable at the time - regardless of whether that cadence still matches how much the underlying data is actually drifting.
How Zolix Tracks the Full ML Lifecycle
Identifies datasets, feature store entries, and training artifacts that haven't been accessed in a defined window and flags them for a lower-cost storage tier or cleanup.
Every training run, sweep, and experiment gets tracked back to the team and project driving it, so experiment sprawl becomes visible spend.
Connects training cost to model performance metrics, so a team can see cost per accuracy point, not just total spend.
Analyzes latency requirements against actual serving patterns and flags workloads that could shift to batch processing without missing any real requirement.
Tracks whether current cadence still matches actual model drift, flagging schedules that may be running more - or less - often than the data requires.
Profiles training runs against infrastructure metrics and model-specific signals, surfacing bottlenecks a simple cost dashboard would miss.
Recommends coverage per training workload based on how fault-tolerant a given job actually is, rather than one blanket commitment strategy.
Flags the migration opportunity directly when a newer, more cost-efficient instance family would handle the same workload just as well.
Detects idle executor time specifically for teams running on Spark or Databricks, rather than treating a cluster as a single opaque compute cost.
Simulates hyperparameter and hardware combinations against a defined budget ceiling before committing to a full training run.
Full ML Stack
Zolix connects across the tools already in most ML workflows - cloud storage, feature stores, experiment tracking platforms, and both batch and real-time serving infrastructure - so cost visibility follows the model's entire lifecycle. This includes visibility into workloads running on Spark, Databricks, Kubeflow, and standard PyTorch or TensorFlow training pipelines, across AWS, Azure, and GCP.
What teams see after connecting ML workloads to Zolix
Full Lifecycle
Visibility - cost tracked across data, training, experimentation, and serving, not just the training run.
24 Hours
to a first cost visibility report across every connected ML environment.
Zero Agents
Read-only access - Zolix connects through read-only IAM roles, with no agents installed inside training or serving infrastructure.
Yes. Data pipelines, feature stores, experiment tracking, and serving infrastructure are all tracked as part of the full ML lifecycle, not just training compute.
Yes. Datasets, feature store entries, and logged experiments that haven't been accessed in a defined window are flagged for cleanup or a lower-cost storage tier.
Yes. Cost is connected to performance metrics, so teams can evaluate cost per accuracy point rather than looking at spend in isolation.
Yes. Latency requirements are compared against actual serving patterns, and workloads that could shift to cheaper batch processing without missing real requirements are flagged.
Yes. Retraining cadence is compared against actual data drift, flagging schedules that may no longer match how often retraining is genuinely needed.
Yes. Coverage is recommended per training workload based on its actual fault tolerance, rather than applying one blanket commitment strategy across every job.
Yes. Idle executor time is tracked specifically, rather than treating a Spark or Databricks cluster as a single opaque compute cost.
No. Zolix connects through read-only IAM roles, with no agents installed inside training, storage, or serving environments.
Connect your ML infrastructure through read-only access and get visibility into data pipelines, experiments, and serving costs - the spend that builds up quietly around every training run.