Your Opsgenie migration is the best time to add DORA metrics and deploy risk prediction

By Andrew McCarron · March 15, 2026 · 10 min read

Right now, your engineering team is doing something rare: actively re-evaluating your incident tooling stack. That window — where a VP Eng has budget approval queued, political capital to make changes, and all stakeholders aligned on the need to act — almost never opens. Opsgenie's April 2027 shutdown has forced it open.

Most teams will use that window to make a like-for-like swap: Opsgenie → PagerDuty, or Opsgenie → incident.io. Same alerting, same on-call, different vendor. That's a missed opportunity.

Here's what we think you should do instead: use the migration as a forcing function to add the engineering intelligence layer you should have had years ago — DORA metrics, deploy risk prediction, and AI-powered incident investigation.

Why the migration window is the right moment

Adding new tooling to an engineering organization is politically hard. It requires a problem that everyone acknowledges, budget that's already been approved for something, and stakeholders who are primed to change. Opsgenie's shutdown creates all three conditions simultaneously.

You already have the conversation starter: "We're migrating off Opsgenie. While we're evaluating options, let's also look at what else we should add to our engineering intelligence stack."

Compare this to trying to introduce DORA metrics or deploy risk prediction in a normal quarter — where you're competing with roadmap priorities, budget scrutiny, and change fatigue. The Opsgenie migration is the rare opening.

What you're probably missing: the incident prevention layer

Every incident management platform — Opsgenie, PagerDuty, incident.io — occupies the same slice of the engineering reliability loop: detect and manage. An alert fires, someone acknowledges it, the incident gets resolved, a postmortem gets written.

None of them answer the question your VP Eng actually asks after every major outage: "Could we have seen this coming?"

In 70% of cases, the answer is yes — there were signals in the code changes that preceded the incident. High-risk PRs that touched critical paths with no test coverage. Changes to files no reviewer had expertise in. Deploys on Friday afternoons into services that had just had a P0. These signals are knowable before deployment.

DORA metrics: what Opsgenie was giving you (partially) and what you actually need

Opsgenie contributed to one DORA metric: MTTR (mean time to restore). Your Opsgenie incident timeline told you how long it took to resolve each alert. That's valuable, but it's one of four DORA metrics — and it's the reactive one.

The migration to a new incident platform resets your MTTR baseline if you're not careful. But it's also an opportunity: instead of just restoring MTTR, you can add the other three DORA metrics at the same time. Most teams never do this because it requires touching a second tool (usually a GitHub integration or a separate metrics platform). The Opsgenie migration is the moment to do it all in one change.

What deploy risk prediction looks like in practice

Deploy risk prediction is not a magic box. It's a model that scores each PR across research-validated signals before it merges:

• Coverage delta on changed lines (not global %) — uncovered changed code is higher risk
• CODEOWNERS compliance — PRs where required reviewers didn't approve
• Change entropy — many files changed across many directories signals tangled scope
• Author file-expertise — first-time changes to a file/service by this author
• AI authorship detection — AI-generated code has different risk profile than human-authored
• Tangled commit detection — multiple unrelated concerns in one PR
• Deploy timing — Friday afternoon + high-risk service = elevated risk
• Service incident history — services that had recent P0s are higher risk

The output is a 0–100 risk score on every PR, surfaced as a GitHub Check Run before the PR merges. Engineering managers see a portfolio view: which services have high-risk PRs queued this sprint, and what the top risk factors are.

This doesn't replace your incident management. It reduces how often your incident management gets triggered.

The argument for doing this now vs. later

"We'll add DORA metrics after we stabilize on the new platform." This is the most common answer — and it's how companies spend 3 years on their roadmap without ever getting to it.

The reason to do it during the migration:

• Budget is already open.

  You're already spending headcount evaluating incident management platforms. The marginal cost of evaluating Koalr alongside PagerDuty and incident.io is zero.

• Stakeholders are already re-evaluating.

  The VP Eng, CTO, and platform team are all in the same room talking about incident tooling. That meeting doesn't happen in normal quarters.

• Your MTTR baseline resets anyway.

  When you cut over to a new incident platform, your historical MTTR data needs to be imported or starts from zero. You're already touching this data — use that moment to build the full DORA picture, not just MTTR.

• Deploy risk pays off faster than you expect.

  Teams that add deploy risk scoring typically see their change failure rate drop within 8–12 weeks — before the model is even trained on their org's specific history. The research-validated default signals are effective from day one.

How to pitch this to your CTO in one slide