Networks that include F5 devices are getting more complex and the IT teams that manage them are often limited. Without automation, IT must manually identify and troubleshoot issues, taking them away from more strategic efforts. Indeni automates low level tasks such monitoring and inspecting device issues, so that you can focus on what matters most to your business.
Find Answers Quickly To Current And Future Issue
Collaborate with industry peers to rapidly respond to disruptions and preemptively identify issues before they occur.
- Install – Leverage the collective experience of thousands of IT professionals to improve their infrastructure health and deliver new projects on time.
- Maintain – Indeni is the only solution in the market that has an integrated, ever-expanding, crowdsourced knowledge base to compare device conﬁgurations, logs, and metrics to real-world best practices.
- Monitor – Custom scripts from device experts and the collaboration across peer environments is how Indeni learns ‘normal’ and ‘abnormal’ behavior and can proactively alert on impending F5 device issues.
- Diagnose – Indeni provides easy-to-follow remediation steps (tested by peers) allowing customers to proactively solve issues on their own before they cause network downtime.
Indeni + F5 = Better Together
F5 devices are superb with the amount of insight they provide in post-outage analysis with tools like BIG-IQ and iHealth. These tools are missing the ability to be proactive. This is what makes Indeni a perfect complement to F5 deployments. Indeni fills these gaps with the unique ability to alert proactively with increased accuracy.
- Become Proactive with Dynamic Knowledge – The combination of data plus context is what Indeni calls “Knowledge.” The collection, analysis, and correlation of scripts located in the Indeni knowledge database are written by F5 experts and customers. With this amount of knowledge, Indeni is able to provide deep-level proactive alerts.
- Increase Alert Accuracy – Indeni digests and analyzes thousands of parameters and compares settings in relation to each other and then alerts when limits are approaching abnormality. By having a larger sample set, Indeni’s alerts are tailored to customers’ specific requirements creating less white noise.