Randoli Observability | Product Demo

Hi there, I'm Kunal, devil engineer at Randoli. In this short video, I want to give you a quick look at how we are rethinking observability at Randoli. Not just as a tool, but as a model. Now, most observability stacks expect us to stream telemetry 24/7. This leads to high injection cost for you, tons of noise, and limited control over your own data. At Randoli, our approach to observability is a federated control plane. This means telemetry is processed closer to where it originates. That is inside your environment. We don't stream or tell you to ingest everything by default. We let you view telemetry on demand or only when an issue is detected. The result, low injection cost, full data privacy for you and clear correlated insights when you actually need it. Let me show you what this looks like. All right, so this is our infrastructure view. What you see here is a unified dashboard across multiple clouds and environments giving you full visibility across your entire infrastructure all in a single view. You get a quick view into system level performance such as CPU, memory, and disk usage. And you also have the ability to track key, system, and application level metrics using built-in monitors like CPU, memory, disk, IOPS, error rates, so on and so forth, all in a single place. Now, here is when things get interesting. Instead of continuously ingesting telemetry, Randoli lets you capture snapshots on demand. This means your telemetry stays within your environment and you only pull the data when something needs attention. That's a huge cost saver because this keeps the injection cost low and gives you full control over what gets collected and when. All while giving you the insights that you need to know to check the system level performance. Now let's take a look at workloads. This is where most teams spend time while debugging application performance or troubleshooting microservices. You get a good overview around your workload's performance such as distributed traces, key metric trends, a unified timeline of cluster and workload events, and a list of workload related events as well. If you want deeper insights, you can generate an ondemand telemetry snapshot that captures all the relevant telemetry signals on demand during a specific time window. This helps reduce the noise and focuses your attention while you're troubleshooting. Now, here's a part which engineers love. Our system supports automatic issue detection without requiring any static thresholds or custom alert rules to be set. The agent analyzes the workloads behavior and detects recurring patterns such as crash loops, network delays, part failures, so on and so forth, and creates a detailed incident report. Each issue report provides contextual telemetry, relevant metadata, and an attached runbook to guide resolution, reducing your time to identify and address root causes during an incident. Now, let's talk about logs for a second, which are typically the most expensive and often the hardest to manage part of observability. With log analyzers in Randoli, logs are scanned locally and no data ever leaves your environment. That means zero egress cost for you. You can continuously analyze your logs for specific patterns or messages in real time across workloads, name spaces or clusters while maintaining full data privacy. There's zero egress, zero vendor dependency for you and we automatically surface recurring issues before they escalate and affect your users. In the end for you, log analysis is fast, private, and incredibly cost efficient. So that's a quick look at Randoli's observability platform. It's built on federated ondemand telemetry model, giving you high context insights with low data volume, reduced injection cost, and complete control over your data. You don't have to compromise between depth and efficiency anymore. If you're looking for a modern approach to observability, give us a try and let us know what you think. You can get started for free or book a demo with our team to explore your particular use case. Thank you for watching and I'll see you