Proactive alerts to stop cost spikes
Stop cloud cost spikes in their tracks, using DoiT Anomaly Detection to autonomously surface cost anomalies and minimize their impact on your bill.
Available for AWS and Google Cloud.
“Every time we launch a new cloud product, we must closely monitor hosting costs on a daily basis, which can be painful because it is an incredibly manual task. Thanks to DoiT Anomaly Detection, we have more time to focus on other areas in our business, trusting that we’ll get alerted whenever there is any deviation in cloud usage from our normal behavior. You made my life easier!”
Raouf Ayoub
Head of FP&A
Anomaly Detection Benefits
Zero-effort monitoring
Uncover anomalies autonomously, without needing to dedicate resources towards identifying the source and scope of the problem.
No configuration required
Use ML to understand and define normal behavior for each service you use — per project and account — and avoid manual threshold setup.
Catch cost spikes early
Minimize impact to your bill with up-to-date analysis of your workload activity the second new billing files are made available.
How it works
DoiT Anomaly Detection uses ML to analyze your billing data and define spending patterns across the services you use, per project and account. It then alerts you when there’s a detected deviation from normal behavior so you can tackle the cause. By autonomously monitoring for anomalies, you minimize the impact of abnormal cost spikes and allow your engineers to focus on more important product initiatives.
Recent blogs
Design principles in a distributed digital world
An accessible website is easier to navigate, reinforcing customer loyalty. We discuss web design principles that help ensure visitors with and without disabilities have the best experience possible.
Helping customers harness the cloud to deliver customer value
In an interview with theCUBE at AWS re:Invent 2022, DoiT’s John Purcell and INFINOX Global’s Danislav Penev discuss managing cloud complexity and cloud costs to deliver customer value.
Avoiding eight common Big Query query mistakes
Optimizing BigQuery involves avoiding some common mistakes people make when writing BigQuery queries. We explain how issues such as using small inserts and overcomplicated views can slow down your query processing and increase costs.
