Obkio

05/08/2026

The challenge with private networks isn't just monitoring them - it's monitoring them end-to-end, across multiple locations, service providers, and technologies simultaneously.

We put together a full guide on exactly that. How to monitor and troubleshoot MPLS, SD-WAN, and VPN networks with precision: what to measure, where to look, and how to catch issues before they impact your users.

Free download. 👇

🔗 https://hubs.la/Q04fwy6y0

05/07/2026

500 sites. One global MPLS to SD-WAN migration. Zero room for error.

That's the project StableLogic brought to us, and what made it work wasn't just the SD-WAN rollout. It was having independent, vendor-neutral monitoring in place before the first site cut over.

Here's how they did it - and what the data looked like when it was done. 👇

🔗 Full case study in comments

05/06/2026

The average P1 takes 4–8 hours to resolve.

Most of that time isn't spent fixing the problem. It's spent finding it.

Complete WAN to LAN visibility changes that math.

Obkio monitors every segment of your continuously: from your ISP handoff through your WAN, all the way to your internal LAN and end devices. When something goes wrong, the data is already there.

- No manual correlation.
- No guesswork.
- No starting from scratch.

The result: issues pinpointed and resolved up to 4x faster than traditional methods.

- Same team.
- Same skills.
- Just the right data, at the right time.

05/05/2026

Every IT team tracks downtime. Not enough track how long it takes to recover from it.

MTTR (Mean Time to Resolve) is the metric that tells you exactly how efficient your team is at getting from "something's broken" to "everything's back to normal." And most teams that measure it don't love what they find.

⚠️ The industry average for a P1 incident is 4–8 hours. Best-in-class is under 1 hour.

The gap between those two numbers isn't usually a skills problem. It's a visibility problem.

Most of that time isn't spent fixing the issue. It's spent finding it:

- Manually correlating data across tools
- Chasing intermittent symptoms
- Trying to reproduce something that already resolved itself

Continuous monitoring compresses that gap significantly. When the data is already being collected before the incident is reported, detection is faster, diagnosis is faster, and the time you spend actually fixing things goes up while the time you spend hunting goes down.

Full breakdown in the comments. 👇

05/01/2026

We're heading to GITEX Kenya x AI Everything Kenya in Nairobi! 🇰🇪

After an incredible experience in Marrakech, we're thrilled to continue in the GITEX universe, this time on the other side of the continent.

We'll be at booth H6-054 alongside our partner SATLX.

Come find us, say hello, and see what Obkio can do for your network.

📍 Booth H6-054
🗓 May 19–21, 2026

See you there! 👋

04/30/2026

Most aren't slow at fixing . They're slow at finding them because their tools show a snapshot, not the full picture.

That's what was built for. Continuous monitoring across every network path (errors, quality metrics, device health, ) all correlated on a single timeline.

So when hits, you're not starting from scratch. You're looking at data that was already being collected before anyone noticed something was wrong.

The time between "something is wrong" and "here's exactly what it is" gets very short, very fast.

04/29/2026

This is what network instability looks like in the data.

Firewall CPU hitting 100%. Network response time degrading at the same moment.

Performance fluctuating with no clear pattern: fine one hour, degraded the next.

That's exactly what Station22 was dealing with. Intermittent issues that users could feel but nobody could consistently reproduce. No obvious outage. No single smoking gun. Just instability that kept coming back.

The problem turned out to be the firewall, but proving it required correlating , , interface data, and performance metrics across the same timeline simultaneously. Do that manually across separate tools and you'll be chasing ghosts for weeks.

With everything on a single correlated dashboard, the pattern became impossible to miss.

That's the real value of continuous monitoring, not just knowing something is wrong, but having the data to prove exactly what caused it.

Full case study here. 👇

🔗 https://hubs.la/Q04dL5pB0

04/28/2026

Network instability is harder to troubleshoot than an outage.

When the network goes down, everyone knows. You get alerts, users call, the problem is obvious. You fix it and move on.

Instability is different. The is up (technically). But performance fluctuates. Calls drop randomly. Apps lag for 30 seconds then recover. Users complain but can't reproduce it on demand.

And because nothing is "officially" down, it's easy to dismiss. Until it isn't.

The frustrating part: rarely has one cause. It's usually a combination of on a degraded link, from an overloaded device, routing changes introducing inconsistent paths, or ISP issues that come and go without ever showing up on a status page.

You can't fix what you can't consistently see.

That's why continuous monitoring matters more for instability than for . By the time you're investigating, the symptom is usually gone. The data is all you have.

04/24/2026

Yesterday, we attended Comitti (ex-Infotélécom) 2026, and yes, we talked about your laggy Teams calls. 📞

Our CEO, Pierre-Luc Charbonneau, took the stage again this year at Sommet Comitti, hosted by our friends at Kinessor. It's become an annual tradition for us, and every year the conversations get better.

His talk this time 🗓️ : "Diagnosing Internet Quality Issues: When Teams Lags and Nobody's Responsible."

You know how it goes. MS Teams is lagging. The meeting is a disaster. And when it's over, everyone just shrugs. Is it the ISP? The internal network? The user's laptop? Microsoft's servers? Nobody owns it. Nobody fixes it.

That's the paradox Pierre-Luc broke down on stage, and it resonates every single time, because almost every IT team has lived it.

He showed how Obkio's new MS Teams Monitoring & Insight: Automatic Diagnostics features correlate network performance data with your critical application metrics to produce a clear, automated, and actionable diagnosis.

One thing he made sure to say out loud: Teams-specific tests aren't always necessary. Because the truth is, most Teams problems are network problems, and Obkio is already very good at diagnosing those.

That said, the appetite in the room for native Teams monitoring was very real. So yes, that's coming.

And after Obkio Insight and MS Teams monitoring? Let's just say we're not slowing down. 👀

Thanks to Kinessor for another great event. See you next year.

04/23/2026

When a user says an app is slow, you have two options.

1. Guess.
2. Or measure.

The APM tab in Obkio gives you a full transaction breakdown for any HTTP endpoint:

- DNS resolution
- TCP connection
- SSL handshake
- Server response time, download

Every step, measured continuously, from your network locations. You open the dashboard. You see exactly where the delay is. You fix the right thing.

Start monitoring your apps alongside your network with a free 14-day trial.

Start monitoring your apps alongside your network with free 14-day trial. 👇

🔗 https://hubs.la/Q04cXsm00

04/22/2026

"The network is fine. Must be the app."
"The app is fine. Must be the network."

Sound familiar?

This is the most common dead end in IT troubleshooting: two teams, two dashboards, no shared answer. Everyone's data shows green on their end, and the user is still sitting there with a slow app.

HTTP monitoring closes that gap. It gives you a single transaction view that shows exactly where the delay is happening (network side or application side) so the conversation stops being about blame and starts being about fixing.

Read the full breakdown on how it works and how to use it. 👇

🔗 https://hubs.la/Q04cTpjc0

Learn how HTTP monitoring works, which metrics matter, and how to set it up to isolate network vs. application issues fast.