Match capacity to workload the right way, the first time.
Toggle between Cost vs CPU, Cost vs Memory, CPU vs Memory, and Table view to communicate data findings to instance planning stakeholders.
Set instance preferences based on instance series, generation, and size to be factored into proposal calculations.
Focus on the instances you’re ready to size with filters for element (resource), attribute, tags, and set utilization preferences.
Include/exclude specific candidates for sizing using tags, attributes, or element names
Set historical usage assumptions or enrich your measurements with our agent integrations
Frame your target values with varying risk tolerances by choosing from several data aggregation methods (95th percentile, 75th percentile, mean, and median)
Sort your sizing proposals and download them as a CSV file. You can also save each version of your report to keep track of different reservation initiatives.
Influence instance size and family type recommendations based on your input and added constraints
Compare current cost to projected cost and see savings percentages
Measure CPU and Memory utilization against current CPU and Memory resources
We go beyond EC2s and analyze the historic workload of some of your other AWS services.
Know the exact allocation of your reservation subscriptions to individual instances
Calculate memory utilization of AWS Lambda functions
Aggregate historic utilization of computing resources and correlate with costs
Filter, group, and sort by all of the meta-data provided by AWS including tags and attributes
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Metricly is rated 4.8 out of 5 stars on Capterra
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Most customers save 32%+ on their AWS bill