Snapt Nova alerts you to anomalies in application users, response codes, traffic, latency, WAF blocks, and more.
If Traffic Didn't Double On Monday, Would Your ADC Tell You?
Most application services only have static rules and thresholds for alerts. That's fine for traffic spikes and high response times. But what happens when response times are unusually low or traffic doesn't increase when you expect it to?
Put Your Application Data To Work
Snapt Nova connects to every node in your network. The central Nova Controller ingests application and traffic telemetry, feeding the Machine Learning Engine with real-time, real-world data. Nova learns what normal looks like and lets you know when you need to pay attention to something unusual.
As your network grows, so does your data. Every location, server, application, session, and user multiplies your data. You can keep hiring data analysts, system admins, and SecOps professionals to make sense of it. Or you could let Snapt do the hard work for you.
Snapt Nova ingests all your application telemetry via its dynamic app services providing load balancing and security in every node. Nova uses machine learning to profile your application behavior so it can alert you when something looks wrong.
Importantly, Nova keeps these alerts to a minimum, so you don't get tired of them and start to ignore them.
Snapt Nova reduces the need for human interaction with the day-to-day operation of your application infrastructure so you can focus your team's time on high-value interactions.
Less time spent discarding routine notifications, more time responding to business-critical events.
The normal way of monitoring application health, performance, and security is very basic. Static rules say "if this, then that". Thresholds say "Tell me when this number goes higher than X". Threat signatures mean attacks only get identified if they match a known signature.
But this method won't find a problem if the problem doesn't look like a problem. And that's a problem.
By contrast, machine learning and anomaly detection start with no pre-conception of what a problem looks like. Instead, it builds a pattern of what normal, healthy, and secure looks like and then looks for events that don't match the profile.
Less time spent trying to anticipate every possible scenario with custom configuration.
Context and scope make a big difference in data analysis. Something that occurs regularly on a global scale might be highly unusual in a particular location.
On the other hand, what looks normal in one location might be unexpected globally.
Snapt Nova gives you insight into anomalies whether they are global or local in nature.
Nova has centralized control and a real-time connection with every node in your network so its Machine Learning Engine can build patterns for each node individually as well as for your whole deployment.
Less time figuring out what's normal for the context, more time taking action (or relaxing if it's not a big deal).
Most application and traffic monitoring only tells you when something has gone wrong. Great, now you've got to hustle to fix it.
Snapt Nova uses machine learning to identify the telltale signs that something is probably going to go wrong – before it happens.
This lets your ops teams and SREs take action to avoid preventable outages, saving your business money (and boosting their reputation in the process).
Less time playing catchup with events, more time getting ahead and building cool stuff.
Full ADC Solution
Easy To Use
Fair, Scalable Pricing
Machine learning and automation is a big part of Snapt's DNA.
Discover more about how we use machine learning to save you time and boost application intelligence.