Who actually uses these products,
and what made them stay.

The GitLab DevOps Engineer

A DevOps engineer, platform engineer, or senior developer at a company that chose GitLab — often for self-hosting, compliance, or all-in-one platform reasons. They maintain the GitLab instance or the pipeline configurations that all other engineers depend on. They think in pipelines, stages, and artifacts. They've written `.gitlab-ci.yml` files that are 300 lines long and know every YAML key by memory. They've debugged a pipeline failure on a Friday evening. They have strong opinions about GitHub Actions versus GitLab CI that they will share if asked.

“The shift was quiet.”

The GitLab DevOps Engineer

A DevOps engineer or platform engineer who chose GitLab because the promise of "one tool for the entire DevOps lifecycle" was too compelling to ignore. They manage the CI/CD pipelines, configure the runners, set up the security scanning, and maintain the deployment workflows. They appreciate that everything lives in one place — no integrating GitHub with CircleCI with Snyk with ArgoCD. But they've also learned that "one tool that does everything" sometimes means "one tool that does everything at 80%."

“Not a single dramatic moment — more like a Tuesday at 3pm when they realized they hadn't thought about cI pipeline configuration in YAML becomes deeply nested and hard to maintain as complexity grows in two weeks.”

The PagerDuty On-Call Engineer

A software engineer or site reliability engineer who is on a rotating on-call schedule and whose relationship with PagerDuty is defined by the moments it wakes them up. They've been paged at 3am. They've resolved incidents from their phone in bed. They've also been paged for something that wasn't an incident — a flaky alert, a threshold set too low, a monitoring rule that was never updated after the system changed. Every false positive erodes their trust in the alert and their willingness to respond with full urgency next time. They manage this tension carefully.

“The shift was quiet.”

The Fly.io Edge Deployer

A backend developer or DevOps engineer who deploys applications on Fly.io because they need their app running close to users globally — not just served from a CDN, but actually computing at the edge. They've outgrown Heroku's simplicity, don't want AWS's complexity, and find Vercel too opinionated for non-Next.js workloads. Fly.io hits the sweet spot: Docker containers deployed globally with a CLI that feels developer-first. They're comfortable with infrastructure but don't want it to be their full-time job.

“Not a single dramatic moment — more like a Tuesday at 3pm when they realized they hadn't thought about stateful workloads at the edge (databases, volumes) have limitations that aren't always clear until production in two weeks.”

The Datadog SRE

A site reliability engineer or DevOps engineer responsible for the uptime and performance of production systems. They chose Datadog because it combines metrics, traces, logs, and alerts in one place — but now they're paying for all of it and the bill is terrifying. They've built dashboards that are beautiful, alerts that are precise, and runbooks that nobody reads. They are the person who gets paged at 3 AM and needs to determine in 90 seconds whether this is a real incident or a flapping alert.

“The shift was quiet.”

The Zapier Power Automator

A RevOps lead, marketing ops specialist, or operations manager who has become their company's automation architect without the title. They've connected 15–30 apps through Zapier and built workflows that the entire company depends on but nobody else understands. They started with simple two-step Zaps and now build multi-step workflows with filters, paths, formatters, and webhooks. They are the person who gets called when "something stopped working" — which means a Zap failed and nobody noticed until the damage was done.

“A teammate asked how they managed build multi-step automations that handle edge cases without breaking.”

The Clay GTM Engineer

A GTM engineer, growth operations lead, or RevOps professional who uses Clay as their data enrichment and workflow engine. They build spreadsheet-like tables that pull from 50+ data providers — enriching companies with technographic data, finding decision-makers' emails, scoring leads based on signals, and triggering personalized outreach. They think in data transformations and API calls. They've replaced hours of manual prospect research with Clay workflows that run in minutes. They are the engineer of the sales pipeline's data layer.

“The shift was quiet.”

The Attio Revenue Operations Lead

A revenue operations lead or head of sales operations at a Series A–C startup who chose Attio because legacy CRMs either cost too much (Salesforce) or think too rigidly (HubSpot). They build custom objects, design pipeline views, and create automations that match how their team actually sells — not how a CRM template assumes they sell. They think in data models, not contact records. They've realized that a CRM is only as good as the data in it, and their primary job is making sure the data stays clean and the team actually uses the tool.

“It happened mid-workflow — the company is expanding from SMB to mid-market sales.”