Rezee vs GitHub, PagerDuty and LinearB - deploys, incidents and delivery metrics in one place

Many teams track deployments in GitHub, run incidents in PagerDuty and read delivery metrics in LinearB. An honest comparison with Rezee, where the deployment, the incident and the number are rows in one database - and a plain list of what each of those three tools does better.

Oct 8, 2026 · 7 min read · Kash Gohil · LinkedIn · X

Here is a common setup. Code and deploys live in GitHub. Incidents live in PagerDuty. Delivery metrics live in LinearB. Each tool is good, and each one sees one slice of the same event: a change went out, something broke, somebody fixed it.

Rezee puts those three slices in one workspace. A deployment, the incident it caused and the failure-rate figure that counts it are rows in one database. This post is about where that matters, and where it does not.

Short version: if you need paging and on-call, keep PagerDuty, because Rezee does not do it. If you need deep engineering analytics across many teams and git providers, LinearB goes much further than Rezee's four numbers. If your code stays on GitHub, Rezee's deploy record is not something you add beside it. The case for Rezee is narrower. It is for small and mid-sized teams who want the deploy, the incident and the follow-up work to point at each other, with nobody keeping them in sync.

What do GitHub, PagerDuty and LinearB do well?

GitHub is the default home for code, and its deployment story is good. Environments, deployment records and protection rules sit next to Actions, which has an answer for almost any automation. Most clouds and deploy tools support it first. If your deploys run from Actions, GitHub already knows a lot about them.

PagerDuty is built around one hard problem: getting the right person's attention at 3am. On-call schedules, escalation policies, phone, SMS and push alerts, and integrations with almost every monitoring tool. That problem is a lot harder than it looks, and PagerDuty has spent a long time on it.

LinearB reads your git provider and your tracker and turns them into engineering metrics. That means DORA-style numbers, cycle time broken into phases, benchmarks, and automation around pull requests. It works across providers, so an organisation with code in several places gets one view.

None of these is the problem. The problem is the space between them.

What goes wrong between three tools?

The deployment exists three times. GitHub has the deployment. PagerDuty has the incident that started ten minutes later. LinearB has a metric that may or may not know the two are related. Connecting them is a person reading timestamps, or an integration that has to be set up and kept working.

"What changed?" is a manual join. The first question in an incident is what shipped just before it. With three tools, the responder opens the incident in one tab and the deploy history in another and lines them up by eye. That is fine on a quiet day and slow at 3am.

The failure rate needs to be told what failed. A metrics tool can count deploys easily. Knowing which deploys failed in production, and which incident each one caused, needs data from the incident tool, mapped onto data from the code host. When that mapping drifts, the number drifts, and people stop trusting it.

The postmortem's work goes somewhere else again. The follow-ups become tickets in whichever tracker you use. Whether they shipped is rarely visible from the incident they came from.

What Rezee does differently

Rezee records deployments in the same database as the code, the issues and the chat. Three paths write one row. A pipeline job can carry a deploy: key, a script can call rezee deploy start and finish from the CLI, and anything else can call deploy_report over MCP or REST. Rezee does not deploy anything itself. It keeps the record, and your own job does the work. We wrote up how Ship is built separately.

Because the deployment is a row, the rest follows as joins:

The incident names the deployment. You can declare an incident from a failed deployment or just after a rollback, and it names that deployment. Every deploy into the incident's environment lands on its timeline as it starts and as it ends.
The figure names the incident. Ship's "changes rolled back or failed" figure opens to the list of changes behind it. Each change shows the incident it caused, or the incident naming the deployment that fixed it.
The postmortem files the work. A postmortem starts as a draft page with the incident's timeline already in it. Once it has a project and somebody other than the author approves it, each follow-up heading becomes an issue, linked back to the incident.
The issue says where it shipped. An issue reads "shipped in v1.4.2, production, 3h ago" with nobody linking anything.

Rezee also starts an incident where most incidents start, in chat. Declare one from a message, and that message's thread becomes the incident's discussion.

The delivery numbers are deliberately few. Ship shows deploys to production per week, merge to live, changes rolled back or failed with the median time to recover, and builds passing. Every one opens to the rows it came from. There are no grades.

Where GitHub wins

Ecosystem. The Actions marketplace, every third-party integration, and the fact that every developer already has an account.
Open source. If contributors need to find you, GitHub is where they look.
Staying put. Rezee's deploy record is about repositories Rezee hosts, and its merge-to-live figure asks Rezee's own git server. If your code stays on GitHub, you cannot point Ship at it. Moving is git remote add and git push, but your Actions workflows need rewriting in Rezee's pipeline YAML, and that is real work.

Where PagerDuty wins

This one is simple. Rezee does not page anyone. It has no on-call schedules, no escalation policies and no phone or SMS alerts. Rezee's own incident tools say so in their descriptions: they record, and nobody is paged.

If someone must be woken when production falls over, you need a paging tool, and PagerDuty is a strong one. You do not have to choose, though. Rezee takes alerts from a monitor at one endpoint, in its own shape or as an Alertmanager or Grafana webhook sends them. A unique index keeps one live incident per alert. A script can do the same with rezee incident report. So the monitor can page through PagerDuty and record in Rezee, and the record stays next to the deploys.

Where LinearB wins

Depth. Ship's delivery panel is four figures over twelve weeks. LinearB offers far more: phases of cycle time, pull request and review measures, trends across teams, and benchmarks.
Breadth of sources. LinearB reads several git providers and trackers. Rezee's numbers come only from Rezee.
Reporting up. If engineering leadership needs reports across a large organisation, that is LinearB's home ground, not ours.

Two of our own limits are worth knowing too. Merge to live starts at the merge, not the first commit, so it reads shorter than DORA's lead time for changes. And a bad change fixed forward, with a new commit and no rollback, does not count as a failure. Nothing in the data says the second deploy was a fix.

Who should not switch

Be honest about this one.

Your code is staying on GitHub. Ship cannot record deploys of repositories it does not host. You would be adding a fourth tool, not replacing three.
You run a real on-call rotation. Rezee does not replace that. Keep your paging tool, and decide whether recording incidents next to the code is worth a second integration.
Metrics are a management product for you. If you report delivery numbers across dozens of teams, four figures will not be enough.
You are large and your integrations work. If the joins between your tools are automated and trusted, the seam is not your biggest cost.

Who this is for

It is for teams of a handful to a few dozen people. On those teams, the person who merges a change often deploys it too, and gets the message when it breaks. For them the cost is not any one tool being weak. It is the first minutes of every incident, spent working out what shipped. It is also the follow-ups that never come back to the incident they came from.

It is also for teams that let agents near production. An agent can report a deploy, start a release train, and declare and update incidents. It uses the same scoped tokens and meets the same gates as everyone else. It can never answer a release gate. Those rules are easier to trust when the deploy, the gate and the incident share one permission model.

The record is on every plan, including Free: environments, the grid of what is live where, rollback, promote, incidents and the four numbers. Release trains with gates and bakes start at Startup. The details are on pricing.