Operations | Monitoring | ITSM | DevOps | Cloud

For Independent Software Vendors (ISVs), delivering containerized applications to enterprise clients often means navigating a difficult trade-off between minimal image size and accurate security visibility. Traditional approaches can leave development teams battling severe CVE noise or, conversely, missing critical vulnerabilities entirely due to scanner blind spots.

IBM completed its $6.4 billion acquisition of HashiCorp on February 27, 2025. The HCP Terraform free tier ended March 31, 2026. And Terraform's CDKTF framework was quietly deprecated in December 2025. None of these events broke your Terraform infrastructure. But together they raised a question that's harder to dismiss: what are you actually paying for, and what happens if the roadmap shifts again?

Related reading: Still deciding which platform to migrate to? Terraform Alternatives: A 2026 Buyer's Guide compares env zero, Spacelift, Scalr, and the IaC tool alternatives side by side. Migrating from Terraform Cloud (TFC), now known as HCP Terraform, or Terraform Enterprise (TFE) can quickly become complex. Workspaces, variables, remote state, and project structure all need to be recreated carefully to avoid breaking infrastructure workflows.

Most teams wire up PagerDuty Slack workflows in the shallowest possible way: an incident fires, a message appears in a channel, and a human reads it and then goes somewhere else to do the actual work. That is a notification, not a workflow, and it leaves most of the value on the table. The useful version automates the steps between the alert arriving and someone competent looking at it. Who gets assigned. Where the conversation happens. Who else needs pulling in.

Incident severity levels exist for one reason: so that a responder who was asleep ninety seconds ago can decide, without debate, how many people to wake up. Everything else (the reporting, the SLA math, the quarterly review slides) is downstream of that one decision. If your scale cannot be applied in under thirty seconds by someone with partial information and no context, it is not a severity scale. It is documentation.

Every major security information and event management (SIEM) platform prices on the volume of data you send it. Microsoft Sentinel meters per gigabyte across two tiers. Splunk charges per gigabyte indexed or per compute unit. Google SecOps draws down a prepaid gigabyte credit balance. Elastic Security bills ingest plus retention, or the resources your cluster consumes. Three of the four keep their real rates quote-only. Budgeting starts with the meter.

Every so often we sit down with someone from our support team and turn their week into a blog post. This time, Roberto walks us through four things generating tickets right now: an upcoming Microsoft authentication change, how AI credit pools actually work, multi-account support in Kepler, and a one-line fix for laggy GitKraken on Linux or WSL. Here’s what’s changing and what to do about it.

In order for users to get the most out of the data being sent to Sentry, it’s important that we make it easy to find that data. Our team works on features to help users browse their data to find a particular event using search queries and filters. The search bar enables users to find their data by specifying search terms. Searching uses the Sentry Search Syntax, which can be barrier for users.

A request to upgrade a saturated circuit is easy to raise and hard to defend. The interface graph proves the link is full. It says nothing about which application, host or conversation filled it, so the spend gets approved on assumption instead of evidence. The same missing detail turns up everywhere else. Incidents run long because the cause is guessed at, capacity planning rests on estimates, and security questions arrive weeks after the traffic record expired.

ChatGPT Work has become a common starting point for data and product teams. Analysts open it to compare launch adoption across segments, diagnose a metric that moved overnight, or turn a week of scattered numbers into a readout that a leader can act on. But the moment teams ask whether their experiment actually caused an effect they’ve observed, the conversation stalls.