MLOps Pipeline Cost Calculator
Estimate monthly MLOps costs including training, serving, storage, and monitoring infrastructure.
About this calculator
This calculator adds up the four recurring cost centers of running a machine learning system in production: training, serving, storage, and monitoring. Training cost scales with how often models are retrained -- each run is estimated at a flat illustrative rate representative of a short GPU training job plus overhead, since actual training cost varies enormously with model size, GPU type, and training duration. Serving cost assumes inference endpoints run continuously (24/7) at an illustrative hourly rate per instance, which is the dominant real-world MLOps cost for most teams because inference capacity typically can't be scaled to zero the way batch training jobs can.
Storage cost applies a flat rate per terabyte per month, representative of standard object storage pricing for training data, model artifacts, and logs. Monitoring, if enabled, adds a base platform fee plus a per-instance charge, reflecting that drift detection and performance tracking typically scale with the number of things being monitored. All of these figures are illustrative planning numbers, not quotes from any specific cloud provider -- actual pricing varies by vendor, region, instance type, and volume discounts, sometimes substantially.
Inputs
Results
Total Monthly Cost
$1,535.00
Annual Cost
$18,420.00
How to Use This Calculator
- Enter Training Runs/Month, Serving Instances, and Data Storage (TB).
- Set Monitoring & Observability, Disabled, and Enabled.
- Review Total Monthly Cost ($) and Annual Cost ($).
- Use Training Cost ($) and Serving Cost ($) to inform your decision.
- Use the chart to visualize the results and explore different scenarios by adjusting inputs.
How the result changes with Serving Instances
| Serving Instances | Total Monthly Cost | Annual Cost |
|---|---|---|
| 1 | $1,125.00 | $13,500.00 |
| 1.5 | $1,330.00 | $15,960.00 |
| 3 | $1,945.00 | $23,340.00 |
| 5 | $2,765.00 | $33,180.00 |
What each input means
- Training Runs/Month
- Number of model training or retraining runs per month
- Serving Instances
- Number of inference endpoint instances running 24/7
- Data Storage (TB)
- Total data storage for training data, model artifacts, and logs
- Monitoring & Observability
- Model monitoring for drift detection, performance tracking, and alerting
How this is calculated
Worked example, using the default values
- Identify Input Parameters4 parametersTraining Runs/Month = 8, Serving Instances = 2, Data Storage (TB) = 5, Monitoring & Observability = 1 = 4 input(s) provided
- Calculate Total Monthly CostTotal Monthly Cost1535 = $1,535
- Calculate Training CostTraining Cost400 = $400
- Calculate Serving CostServing Cost720 = $720
Engine last updated . Checked against 2 independently-derived tests — how we verify calculators. Built by Paul Gunder, a software engineer, not a licensed financial, medical, or legal professional.
Frequently Asked Questions
Why does serving cost usually dominate MLOps spending?
Unlike training jobs, which run for a bounded period and then stop, inference endpoints for a production model typically need to be available around the clock to serve requests. This calculator reflects that by charging serving cost continuously (24 hours a day, every day of the month) per instance, while training cost only accrues for the runs actually scheduled -- which is why adding serving instances tends to move the total more than adding training runs.
What does the monitoring cost represent?
It's a placeholder for drift detection, performance tracking, and alerting infrastructure that watches a deployed model's inputs and outputs for degradation over time. This calculator models it as a base platform fee plus a per-serving- instance charge, since monitoring effort typically scales with how many endpoints need to be watched; toggle it off if your team doesn't run dedicated ML monitoring tooling.
How can I reduce monthly ML infrastructure costs?
The levers that move this estimate most are cutting serving instance count (for example, by batching inference requests or using a smaller, faster model), reducing training frequency if models don't need frequent retraining, and pruning stored data and artifacts that aren't actively needed -- since serving typically represents the largest recurring cost, right-sizing it usually has the biggest impact.
Does this account for data engineering or labeling costs?
No -- this calculator covers only the infrastructure costs of training, serving, storage, and monitoring a model that's already built. It does not include data collection, annotation, feature engineering, or the engineering time spent building and maintaining the pipeline itself, all of which are real costs in a production ML system.
Related Calculators
The questions that sit next to this one — chosen by subject, including calculators filed under a different category.
Inference Latency Calculator
Estimate model inference latency, throughput, and memory requirements across CPU, GPU, and edge hardware.
MLOps & AI CostingGPU Training Cost Calculator
Estimate cloud GPU training time, cost, and CO2 emissions for machine learning model training.
Oil & GasPipeline Flow Calculator
Calculate gas flow rate from pipe diameter and pressure differential.
MLOps & AI CostingData Annotation Cost Calculator
Estimate labeling costs for ML datasets by task type, dataset size, and annotator rates.
More in Technology & Computing.