If you spend any time on tech Twitter or developer Reddit, you already know the script. Someone complains about Jira. Someone tells them to switch to Linear. A third person jumps in to defend Jira if you just configure it properly. Repeat weekly.
Plenty of opinions, very little data. So I went looking for the actual numbers, using adoption-signal data from Bloomberry, which tracks new customers through clickstream data - meaning when a new company-specific URL shows up (a new Atlassian Jira workspace URL, a new Linear team URL, and so on), it's logged as a new customer for that tool.
The dataset covers roughly a million company domains tracked between February 2025 and April 2026.
A quick definition before we get into it: when I say "customer," I mean any company that newly adopted the tool, whether on a free trial, free tier, or paid plan.
Here's what the numbers say.
Linear is catching Jira fast
Twelve months ago, this race was not close. Between February and April 2025, Jira picked up 2,225 new companies. Linear picked up 807. Jira was acquiring almost three times as many new customers each month, every month.
A year later, the gap has nearly disappeared. In February through April 2026, Linear added 1,351 new companies. Jira added 1,524. The two are now within 12% of each other, and the lines are heading in opposite directions:
| Tool | Feb–Apr 2025 | Feb–Apr 2026 | YoY change |
|---|---|---|---|
| Linear | 807 | 1,351 | +67% |
| Jira | 2,225 | 1,524 | -32% |
Linear added two-thirds more new customers in the most recent three-month window than the same window a year ago. Jira added roughly a third fewer. If both trends hold, Linear overtakes Jira on new customer acquisition within a quarter or two.
There is one important wrinkle on the Atlassian side. New Jira signups are dropping, but the number of existing Atlassian customers expanding the product across their organizations has held steady. The way to detect organizational expansion in clickstream data is by looking for SSO setup with Atlassian - when a company configures SSO, it usually means the tool is being deployed beyond a single team. That signal stayed roughly flat year-over-year, up about 4%, while new Jira customer signups dropped 32%.
This isn't the same as revenue expansion or net revenue retention - we're not measuring seat counts or contract value. But it is a useful leading indicator of organizational rollout. And the two trend lines, when you look at them together, are pointing in opposite directions: existing customers keep expanding their footprint while new customers are signing up at a slower rate.
That's consistent with what Atlassian's CFO has been telling Wall Street for a while now. Revenue growth is being driven by existing customers expanding seats, not by net-new logos. The Atlassian story isn't "the product is dying." It's "the funnel for new customers is slowing, and we're making it up by selling more to the people we already have."
Different customers, different sizes
The two products serve very different parts of the market, which becomes obvious as soon as you cut the data by company size. I joined the customer data with LinkedIn organization data to see how each customer base breaks down by headcount:
| Company size | Linear % | Jira % |
|---|---|---|
| 2–10 | 12.3% | 9.2% |
| 11–50 | 45.6% | 38.1% |
| 51–200 | 27.0% | 28.4% |
| 201–500 | 7.9% | 11.7% |
| 501–1,000 | 3.0% | 5.1% |
| 1,001–5,000 | 2.8% | 5.1% |
| 5,001–10,000 | 0.6% | 1.0% |
| 10,001+ | 0.9% | 1.2% |
Linear is concentrated in one specific band: companies with 11 to 200 employees. Almost three-quarters of Linear customers - 72.6% - sit in that range, and the 11-to-50 bucket alone accounts for 46% of the entire customer base. That single bucket is bigger than any equivalent bucket for Jira.
Jira's distribution is wider on every dimension. It has slightly fewer customers in the smallest segments, but consistently more share from 200 employees up. The clearest gap shows up in the 1,001-to-5,000 bucket, where Jira has 5.1% of customers and Linear has 2.8%. Jira's enterprise tail is roughly twice as long.
The short version: a 25-person engineering team is probably picking Linear. A 2,000-person engineering org is probably staying on Jira. The interesting fight is in the 200-to-500 zone, where the choice really does come down to the team's preferences, the existing tooling stack, and how much process the company has built up around the issue tracker.
The industry split
The size split tells one story; the industry split tells a related but distinct one. For each tool, I calculated the lift over baseline: how much more likely a company in a given sector is to use the tool than the average company across the dataset. A lift of 4x means a company in that sector is four times more likely to use the tool than the typical company.
| Sector | Linear lift | Jira lift |
|---|---|---|
| Software development | 4.6x | 2.8x |
| Technology, information, internet | 2.8x | 1.3x |
| Computer and network security | 2.6x | 2.2x |
| Computer games | 2.2x | 2.8x |
| E-learning providers | 2.0x | 1.5x |
| IT services and consulting | 1.5x | 1.7x |
| Medical equipment manufacturing | below baseline | 1.6x |
| Biotechnology research | below baseline | 1.3x |
| Telecommunications | below baseline | 1.2x |
| Insurance | below baseline | 1.2x |
| Financial services | 1.4x | 1.3x |
Both tools peak in software, but with very different intensities. Linear's lift in software development is 4.6x - much sharper than Jira's 2.8x. Linear's customer base is more concentrated in tech as a percentage. But because Jira is the bigger product overall, it actually has more software customers in absolute terms.
The more revealing rows are the ones near the bottom of the table. Medical equipment, biotech, telecom, insurance - these are sectors where Jira has real penetration and Linear is below baseline. The common thread across all of them is heavy compliance and audit requirements: regulated workflows, change-management documentation, audit trails, role-based permissions, the kind of features that take years to build properly. Atlassian has been building for that buyer for two decades. Linear, based on its public roadmap, has not been and isn't planning to.
You can describe each product's identity in one line. Linear is the issue tracker for software companies and adjacent tech. Jira is the issue tracker for software companies, adjacent tech, and every regulated industry that ships software.
Funding stage tells the same story from a different angle
The size and industry data line up neatly with funding stage. Among customers where I have funding-stage data, here's how each tool's customer base breaks down:
| Funding stage | Linear % | Jira % | Linear / Jira |
|---|---|---|---|
| Early stage (seed, pre-seed, angel) | 37.5% | 27.7% | 1.35x |
| Series A / B / C | 25.6% | 19.2% | 1.33x |
| Series D+ | 2.5% | 2.1% | 1.19x |
| Post-IPO | 2.4% | 5.4% | 0.44x |
| Private equity | 4.1% | 8.0% | 0.51x |
| Debt / grant / non-equity | 8.6% | 14.2% | 0.61x |
Linear is the venture-backed-startup product. Two-thirds of Linear customers with funding data are at Series C or earlier. The early-stage cohort alone - seed, pre-seed, and angel-funded companies - makes up 37.5% of the customer base. This is exactly who Linear was built for, and it's exactly who is signing up.
Jira is the established-business product. Post-IPO companies are 2.3 times more concentrated in Jira's customer base than Linear's. PE-backed companies are 2x more concentrated. Even debt and grant-funded organizations - universities, nonprofits, public-sector adjacent groups - skew toward Jira by 1.6x.
This is the size cut and the industry cut viewed from a third angle, and they all paint the same picture. Linear's customer is a small, fast-growing, venture-backed company. Jira's customer is everyone else who needs an issue tracker - including a meaningful share of small companies, but also the big established ones that Linear hasn't reached.
Tell me your stack and I'll guess your tracker
This was the single most interesting cut in the analysis. If I tell you a company uses Linear, what's the rest of their stack likely to look like? And how does that compare to Jira?
To answer that, I calculated lift again, but this time within each tool's customer base: how much more likely a Linear (or Jira) customer is to also use a given tool, compared to the baseline rate across all companies in the dataset.
| Tool | Linear lift | Jira lift |
|---|---|---|
| Intercom (customer messaging) | 122x | 50x |
| Sentry (error monitoring) | 109x | 51x |
| Amplitude (product analytics) | 109x | 45x |
| Chargebee (subscription billing) | 103x | 48x |
| Cloudflare Workers (edge compute) | 94x | not in top 30 |
| Retool (internal tools) | 90x | 41x |
| Vercel Pro (front-end deploy) | 88x | 32x |
| PagerDuty (incident response) | 88x | not in top 30 |
| Weights and Biases (ML experiments) | 78x | not in top 30 |
| Cursor (AI code editor) | 73x | not in top 30 |
| Microsoft 365 | not in top 30 | 3.8x |
| Azure DevOps | not in top 30 | 19x |
| Salesforce CRM | not in top 30 | 21x |
| Salesforce Experience Cloud | not in top 30 | 37x |
Linear customers live in the modern startup stack. Sentry, Amplitude, Chargebee, Vercel, Retool, PagerDuty, Intercom - these are the names you'd expect to see on a Y Combinator company's tools list. The lift values are striking. Intercom shows up at 122x baseline, Sentry at 109x, Chargebee at 103x. The same patterns are present in Jira's customer base, but at two to three times lower lift.
The AI-tooling signal is even sharper. Cursor at 73x, Weights and Biases at 78x, Claude at 37x. These are tools that have only been in widespread use for about 18 months, and Linear customers are picking them up at extraordinary rates relative to the rest of the market. Linear customers aren't just modern - they're early on the AI-coding curve.
Jira customers live in a different world. The same modern dev tools appear in Jira's top co-vendors too, but at notably lower lift. Jira's distinctive companions are the big enterprise platforms: Microsoft 365, Azure DevOps, Salesforce CRM, Salesforce Experience Cloud. None of those crack Linear's top 30.
The takeaway is simple. Show me what tools a company uses, and I can guess which issue tracker they picked. A company on Vercel + Cursor + Sentry + Amplitude is overwhelmingly going to be on Linear. A company on Microsoft 365 + Azure DevOps + Salesforce is overwhelmingly going to be on Jira. The two stacks barely overlap, even though both products in theory solve the same problem.
How they sell mirrors who they sell to
There's another dimension worth pulling apart: how the customers of each tool actually go to market themselves. Two simple proxies are useful here:
- A public pricing page on a company's website is a signal of self-serve, product-led-growth motion. The company is saying you can decide whether to buy without talking to anyone first.
- A "Get a Demo" or "Request Demo" CTA on the homepage is a signal of sales-led motion. The buyer can't price-shop or sign up; they have to talk to a salesperson before anything happens.
These signals are noisy. Some PLG companies don't publish pricing - Linear itself buries it pretty deep, ironically - and some enterprise companies do. The honest framing isn't "every Linear customer is PLG." It's: among the companies where we can detect a clear go-to-market signal, what's the ratio?
| Customer base | Pricing page (PLG) | Demo CTA (sales-led) | Demo : Pricing ratio |
|---|---|---|---|
| Linear | 2,589 (39%) | 3,981 (61%) | 1.54x |
| Jira | 2,634 (30%) | 6,106 (70%) | 2.32x |
Linear's customer base is meaningfully more PLG-leaning. 39% of Linear customers with a detected GTM signal have a public pricing page, versus 30% for Jira. Phrased the other way around, Jira's customer base is about 50% more sales-led than Linear's - a 2.32x demo-to-pricing ratio, against Linear's 1.54x.
This tracks with everything else. Linear's customers are smaller and earlier stage, often selling to other developers and product managers who want to evaluate quickly without having to sit through a discovery call. Jira's customers are larger and more enterprise, often selling complex products on bigger contracts where buyers expect a sales motion. The same forces that produced "Linear customers use Stripe and Vercel; Jira customers use Salesforce and Microsoft 365" also produced "Linear customers ship pricing pages; Jira customers ship demo CTAs." It's the same underlying fact viewed through a different lens.
And then there's Monday
The Linear vs. Jira fight is the loud one in the project management space, but it's not the only race in the market. So I ran the same time-series analysis on Monday, which spends a fortune on TV and YouTube ads telling teams their tool will fix everything. The result was striking.
Monday's new customer acquisition has been in steady decline for the entire 14 months I tracked. Comparing March-April 2025 to March-April 2026, Monday went from 396 new companies to 235. That's a 41% drop year-over-year - sharper than Jira's 32% decline over the same window. December 2025 was Monday's worst month in the entire dataset, at just 83 new companies.
This isn't only what the third-party data shows. It's also what Monday's own management is telling investors. On the Q4 2025 earnings call, co-CEO Roy Mann described Monday's self-serve channels as "choppy," with persistently higher customer acquisition costs and lower returns in the SMB segment. The company told Wall Street not to expect any improvement in no-touch performance marketing through 2026. They withdrew their previously communicated 2027 targets. The stock dropped 13% on the report.
So when the data shows Monday's new business customer acquisition declining 41% year-over-year, with December 2025 marking their lowest month in the dataset, it's not a noisy signal. The company's own CEO is saying out loud, on an earnings call, that the SMB self-serve funnel is broken.
What does it all mean
For Linear, the momentum is real and broad-based. They own the modern, AI-era startup cohort, which happens to be the fastest-growing one. The harder question is what comes next. To keep growing at this pace, Linear needs to crack regulated industries or move up-market into 500-plus-employee organizations where Jira is entrenched. Both are hard. The regulated-industry play takes years of compliance work that doesn't show up in feature velocity. The up-market play means selling to procurement and IT departments that have been on Jira for a decade and have built every internal process around it.
For Atlassian, the regulated-industry moat is real and durable. Medical device companies do not switch issue trackers because of a viral Twitter thread. But losing the early-stage tech-startup funnel is a real long-term problem. Today's Series A is tomorrow's Series D, and those companies will carry Linear with them as they scale. The size-and-funding data isn't just a snapshot; it's a leading indicator of who the next decade's enterprise customers will be using.
For Monday, the decline is the most striking finding in the analysis. With their marketing budget and brand recognition, they had every opportunity to be the company that beat Linear in the SMB segment. Instead, their net-new customer growth is falling faster than any tool I tracked, and management is saying the funnel itself is broken. That's a different kind of problem from a tool that's losing a feature war. It suggests something has changed in how SMBs discover and choose project management software, and Monday's strategy was built for the old way.
The tools we pick early in a company's life tend to come with us as we grow. That's what makes the current moment interesting. The next decade of issue tracking is being decided right now, in the choices being made by 30-person startups.
Methodology: data comes from Bloomberry's technographic intelligence product, which detects new customers via clickstream signals - when a new Atlassian Jira workspace URL, Linear team URL, or equivalent product-specific URL appears, it's logged as a new customer for that tool. The same set of one million randomly sampled company domains was tracked for all products across 15 months (February 2025 to April 2026), so the comparisons are apples-to-apples. The "new Jira customer" count specifically captures companies that signed up for Jira as a project management tool - it does not include companies that originally signed up for Confluence or Jira Service Management and later expanded into Jira. Atlassian's true new-user flow is therefore larger than what's shown, but the methodology is identical across all 15 months, so the directional trends are valid.
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