gabdiax

I’m building EventSentinel.ai, a predictive AI platform that monitors hardware and network infrastructure to detect early signals of failures and connectivity issues before they cause downtime.

I’m looking for a few early-stage design partners (SRE / DevOps / IT / Network teams) who:

Manage on‑prem or hybrid infrastructure with critical uptime requirements

Are currently using tools like Datadog, PRTG, Zabbix, or similar, but still deal with “surprise” incidents?

Are open to trying an MVP and giving candid feedback in short feedback sessions?

What you’d get:

-Early access to our predictive failure and anomaly detection features

-Direct influence on the roadmap based on your needs

-Free usage during the MVP phase (and preferential terms later)

If this sounds relevant, drop a comment “interested” and I’ll follow up with details or email at [email protected]

I’m building EventSentinel.ai, a predictive AI platform that monitors hardware and network infrastructure to detect early signals of failures and connectivity issues before they cause downtime.

I’m looking for a few early-stage design partners (SRE / DevOps / IT / Network teams) who:

Manage on‑prem or hybrid infrastructure with critical uptime requirements

Are currently using tools like Datadog, PRTG, Zabbix, or similar, but still deal with “surprise” incidents?

Are open to trying an MVP and giving candid feedback in short feedback sessions?

What you’d get:

-Early access to our predictive failure and anomaly detection features

-Direct influence on the roadmap based on your needs

-Free usage during the MVP phase (and preferential terms later)

If this sounds relevant, drop a comment “interested” and I’ll follow up with details or email at [email protected]

That's really interesting. Using statistical process control for failure rates in HPC systems sounds like a very solid approach.

In your experience, were there usually early signals in metrics before job failures increased? For example patterns like latency changes, resource saturation or network anomalies.

I'm trying to understand whether those signals appear consistently enough to detect issues before incidents actually happen.

One thing I'm particularly curious about is whether teams see early signals in metrics or logs before incidents actually happen.

For example: - unusual latency patterns - slow resource saturation - network anomalies

Do people actively monitor these patterns or mostly rely on threshold alerts?

It feels like the traditional fixed SaaS pricing model is slowly shifting toward more consumption-based pricing.

Still very early stage, mostly research and prototypes for now.

I'm also curious about this.

In some cases I've seen teams rely on a mix of automated metrics and human review, especially for production systems where reliability matters a lot.

But evaluation pipelines for AI still seem much less standardized compared to traditional software monitoring.