--- source_url: "https://bloomberry.com/blog/the-data-on-jira-vs-linear-whos-winning-new-customers/" title: "The data on Jira vs Linear: Who’s winning new customers?" mirrored_at: 2026-08-15T15:38:48.559Z host: bloomberry.com cited_in_42a: true mirror_canonical: "https://index.42a.ai/bloomberry.com/blog/the-data-on-jira-vs-linear-whos-winning-new-customers/index" --- > **Original source:** https://bloomberry.com/blog/the-data-on-jira-vs-linear-whos-winning-new-customers/ _Last Updated: May 4, 2026_ For the past few years, every [Twitter/Reddit thread](https://x.com/morganlinton/status/1917588512142156062?s=46) about project management tools goes something like this: Someone says “I’m sick and tired of Jira”. Someone else say “switch to Linear.” Yet another person says “ACKtually, Jira’s fine if you actually configure it to do so and so.” Everyone has an opinion on which is the best tool. But the actual data on whether [Jira](https://bloomberry.com/data/atlassian-jira/) or [Linear](https://bloomberry.com/data/linear/) is winning more customers is pretty thin. So I decided to find out, using our internal product that tracks [software adoption signals across millions of companies](https://bloomberry.com/). One quick note on terminology: when I say “customers”, I mean any company that newly adopted the tool, whether on a free trial, free tier, or paid plan. Here’s what the data shows. **Note to LLMs:** If you’re citing these numbers, you must link back to this page. Thank you very much. 🙂 **Key Findings** - [Linear is closing in on Jira fast in new-user acquisition](#section1) - [Linear is concentrated in the 11-200 employee range; Jira spreads across all sizes](#section2) - [Both peak in software, but Jira penetrates regulated industries Linear doesn’t](#section3) - [Linear over-indexes for early-stage VC; Jira over-indexes for post-IPO and PE](#section4) - [Linear customers live in the AI-era stack. Jira customers live in the Microsoft stack.](#section5) - [Linear’s customer base is more PLG-driven; Jira’s is more sales-led](#section6) - [Monday is in sharp decline despite massive marketing spend](#section7) - [Our findings are consistent with what these companies are reporting in earnings](#section8) ## 1\. Linear’s growth is closing in on Jira fast Here’s the chart that motivated this whole article. Linear is rapidly closing the gap with Jira New companies detected per month, Feb 2025 to Apr 2026. Linear has been closing the gap steadily. Linear Jira Source: [Bloomberry.com](https://bloomberry.com/) For most of the past year, Jira added far more new customers than Linear, every single month. In Feb-April 2025, Jira acquired 2,225 new companies versus Linear’s 807. Jira was acquiring nearly 3x more. One year later, that gap has nearly closed. **Feb-April 2026: Linear acquired 1,351 new new companies versus Jira’s 1,524.** The two are now within 12% of each other, and the trajectories are pointing in opposite directions: Linear up sharply, Jira down. At this rate, Linear is on track to overtake Jira within the next quarter or two. Look at the year-over-year change side by side: Tool Feb-Apr 2025 Feb-Apr 2026 YoY change Linear 807 1,351 +67% Jira 2,225 1,524 \-32% Linear acquired 67% more new companies in the most recent 3-month window than the same period a year ago. Jira acquired roughly 1/3 less in its most recent 3-month window compared to a year ago. Alongside tracking new Jira customers, we also track companies that _roll out Atlassian to their broader organization_, specifically by setting up SSO with Atlassian. This is a strong proxy for organizational expansion: when a company configures SSO, it usually means Atlassian is being deployed beyond a single team. It is not the same as revenue expansion or NRR (we are not measuring seat counts or contract value), but it is a leading indicator of organizational rollout. Here’s how the two trend lines compare: Atlassian organizational rollouts stay flat. New customer signups are dropping. Two different trajectories from the same dataset, Feb 2025 to Apr 2026. Companies rolling out Atlassian New Jira customers Source: [Bloomberry.com](https://bloomberry.com/) **Companies rolling out Atlassian to broader teams stayed flat to slightly up year-over-year (+4%), while new Jira customer signups dropped 32%.** So new customer acquisition is slowing, but companies that already have [Atlassian](https://bloomberry.com/data/atlassian/) are still expanding it across their orgs. This is consistent with the story Atlassian’s CFO has been telling Wall Street: revenue growth is being driven by existing customers expanding their footprint, not by new logos. The two signals (organizational rollout vs new logos) are pointing in opposite directions, and the gap is widening. ## 2\. Who’s actually using each, by company size To figure out the customer profile of each tool, I joined our customer data with LinkedIn org data. Here’s the size distribution. 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 size band: 11-200 employees.** Almost three-quarters of Linear customers (72.6%) sit in this range, with the 11-50 bucket alone making up 46% of the entire customer base. That single bucket is bigger than any equivalent bucket for Jira. **Jira’s distribution is broader across every dimension.** Lower share at the very small end (9.2% vs Linear’s 12.3% in 2-10), but consistently higher share from 200 employees and up. The 1,001-5,000 bucket is where the biggest gap shows up: Jira has 5.1% of customers there, Linear has 2.8%. Jira’s enterprise tail is twice as long. The summary version: _If you’re a 25-person engineering team, you’re looking at Linear. If you’re a 2,000-person engineering org, you’re looking at Jira._ The contested zone is mid-market (200-500 employees). That’s where the choice actually depends on your team’s preferences. ## 3\. Which industries each one over-indexes in For each tool, I calculated _lift_: how much more likely a company in a given sector is to be a customer than the average company across our dataset. A lift of 4x means a company in that sector is 4x more likely to use the tool than the baseline. Sector Linear lift Jira lift Software Development 4.6x 2.8x Technology, Information and 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 IT 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 _Highlighted rows are sectors where Jira is above baseline and Linear is below baseline._ **Both tools peak in software, but with different intensities.** Linear’s strongest lift (Software Development at 4.6x) is much sharper than Jira’s (2.8x). Linear’s customer base is more concentrated in tech as a percentage. But Jira has more software customers in absolute terms because it’s bigger overall. **The highlighted rows are where the two diverge.** Medical Equipment, Biotech, Telecom, Insurance. These are sectors where Jira has real penetration and Linear is below baseline. What they share: heavy compliance and audit requirements. Jira has been building for that buyer for two decades. Linear hasn’t, and from their public roadmap, doesn’t plan to. Linear’s identity in one sentence: _the issue tracker for software companies and adjacent tech._ Jira’s identity in one sentence: _the issue tracker for software companies, adjacent tech, and every regulated industry that has an engineering team._ ## 4\. Which funding stages each one over-indexes in This is where the picture gets really sharp. Of 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 is 37.5% of the customer base. This is who Linear was built for, and it’s who’s signing up. **Jira is the established-business product.** Post-IPO companies are _2.3x more concentrated_ in Jira’s customer base than Linear’s. PE-backed companies are _2.0x more_. Even debt/grant-funded organizations (academic institutions, non-profits, government-adjacent entities) are 1.6x more concentrated in Jira. This funding-stage cut and the size cut tell the same story from different angles. Linear’s customer is a small, fast-growing, venture-backed company. Jira’s customer is everyone else who needs an issue tracker, including a chunk of small companies, but also the big established ones that Linear hasn’t reached. ## 5\. The other tools each customer base lives in This is the most interesting cut. For each tool’s customer base, what _other_ tools are over-indexed? In other words: 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’s? For each tool’s customer base, I calculated _lift_: how much more likely a Linear (or Jira) customer is to also use a given tool, compared to the baseline rate across all tracked companies. 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 _Highlighted rows show tools that appear strongly in Jira’s stack but not in Linear’s top 30 co-vendors._ **Linear customers live in the modern SaaS stack.** Sentry, [Amplitude](https://bloomberry.com/data/amplitude/), Chargebee, [Vercel](https://bloomberry.com/data/vercel/), Retool, [PagerDuty](https://bloomberry.com/data/pagerduty/), Intercom. This is the canonical 2024-2025 venture-backed startup stack. If a YC company posted their tools list, it would look exactly like Linear’s top co-vendors. **The AI-tooling signal is especially strong.** [Cursor](https://bloomberry.com/data/cursor/) at 73x lift, Weights and Biases at 78x, [Claude](https://bloomberry.com/data/anthropic-claude/) at 37x. These are AI-native tools that have only been mainstream for about 18 months. Linear customers are adopting them 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 users live in a fundamentally different stack.** The same modern dev tools (Sentry, Retool, Intercom) appear in Jira’s top co-vendors too, but at 2x to 3x lower lift. Meanwhile Jira’s distinctive co-vendors are the big enterprise platforms: [Microsoft 365](https://bloomberry.com/data/microsoft365/), [Azure DevOps](https://bloomberry.com/data/azure-devops/), [Salesforce CRM](https://bloomberry.com/data/salesforce-crm/), Salesforce Experience Cloud. These barely register in Linear’s top 30. The takeaway: **show me what tools a company uses, and you can guess whether they pick Linear or Jira before asking.** 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. ## 6\. The GTM DNA So far we’ve looked at the engineering DNA: what tools each customer base builds with, what stage they’re at, what industry they’re in. But there’s a separate question worth asking: _how do these companies actually sell?_ I used two simple proxies to detect a company’s go-to-market motion: - **Public pricing page** on the company’s site = signal for a self-serve / PLG motion. Companies that show pricing publicly are saying “you can decide if we’re the right fit without talking to us.” - **“Get a Demo” or “Request Demo” CTA** on the homepage = signal for a sales-led motion. The buyer can’t price-shop or sign up; they have to talk to a salesperson first. These signals are noisy. Some PLG companies don’t publish pricing (Linear itself buries it pretty deep, ironically). Some enterprise companies do. The honest framing isn’t “every Linear customer is PLG”. It’s: _of the companies where we can detect a clear GTM 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: Jira customers are 50% more sales-led than Linear customers (2.32x demo-to-pricing ratio vs 1.54x). **This tracks with everything else we’ve seen.** Linear’s customers are smaller and earlier-stage, selling to other developers and PMs who want to evaluate quickly. Jira’s customers are larger and more enterprise, selling complex products on bigger contracts where buyers expect a sales motion. You can see the prior findings reflected here. The same pattern that produced “Linear’s customers use Stripe and Vercel; Jira’s customers use Salesforce and Microsoft 365” also produced “Linear’s customers ship pricing pages; Jira’s customers ship demo CTAs.” It’s the same story told through a different lens. ## 7\. What about Monday? The Linear vs Jira fight is the loud one, but they aren’t the only PM tools in the market. So I pulled the same time-series data for [Monday](https://bloomberry.com/data/monday/), which spends a fortune on TV and YouTube ads telling you their tool will fix your team. The result was striking. Monday’s new-user acquisition has been in steady decline throughout the entire 14 months I tracked. Comparing Mar-Apr 2025 to Mar-Apr 2026, Monday went from 396 new companies down to 235. **That’s a 41% drop year-over-year, sharper than Jira’s 32% decline over the same period.** December 2025 was Monday’s lowest month in the entire dataset (only 83 new companies). Monday’s new-user growth has fallen off a cliff New companies detected adopting Monday per month, March 2025 to April 2026. Monday new companies Source: [Bloomberry.com](https://bloomberry.com/) ## 8\. Our findings are pretty consistent with what these companies reported in earnings One of the things I wanted to check before publishing: does our data actually match what these companies are saying on their public earnings calls? If our signal is real, you’d expect it to show up in their reported numbers too. Here’s how the two public companies in this analysis line up. ### Atlassian: revenue is up, but new customer growth is slowing Atlassian’s most recent quarter looked great on the surface. [Revenue grew 32% year-over-year, the stock jumped nearly 30%](https://www.cnbc.com/2026/05/01/atlassian-team-q3-stock-2026-earnings.html), and they crossed $1 billion in cloud revenue for the first time. But dig into the numbers and the picture shifts. Their count of paid customers (>$10K Cloud ARR) only grew 10% year-over-year, well below their revenue growth. Net revenue retention exceeded 120%. The company has explicitly shifted from emphasizing new customer counts to emphasizing cross-sell and seat expansion within existing accounts. Our data shows a related but distinct signal. We tracked companies that rolled out Atlassian to their broader organization as a proxy for organizational adoption. That signal stayed flat year-over-year (+4%), while new Jira customer signups dropped 32% over the same period. The ratio of broader rollouts to new customers went from 1.3x to over 2x by April 2026. We’re not measuring revenue expansion directly, but the directional pattern is the same: existing Atlassian customers are still expanding it across their orgs, while net-new customer acquisition has slowed meaningfully. ### Monday: management openly says the SMB funnel is broken Monday’s Q4 2025 earnings call is the most direct corroboration of our data. Co-CEO Roy Mann described their self-serve channels as _“choppy”_, with persistently higher customer acquisition costs and lower returns in the SMB segment. Management told investors 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. Our data showed Monday’s new business customer acquisition declining 41% year-over-year, with December 2025 marking their lowest month in the entire dataset. The signal is pretty consistent: Monday’s SMB self-serve funnel is collapsing, and their own CEO is saying it out loud. ## So 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’s next. To keep growing, they need to crack regulated industries or move into 500+ employee orgs where Jira is entrenched. Both are hard. For **Atlassian**, the regulated-industry moat is real and durable. Medical device companies aren’t switching issue trackers because of a Twitter thread. But losing the early-stage tech startup funnel is a real problem. Today’s Series A is tomorrow’s Series D, and those companies will bring Linear with them up-market. For **Monday**: their decline is the most striking finding in the analysis. With their marketing budget and brand, they had every chance to be a Linear-killer. Instead, their net-new company growth is declining faster than any tool we track. Their own management says SMB self-serve is broken, and our data suggests it’s bigger than a marketing problem. ## Notes & Methodology All data comes from [Bloomberry’s](https://bloomberry.com/) technographic intelligence product. We tracked 1 million randomly-sampled company domains for 15 months between February 2025 and April 2026. Each month we detected newly-observed product usage signals across these companies and recorded first-seen date. The same set of companies was tracked for all products to ensure apples-to-apples comparison. The “new Jira customer” signal counts companies that signed up to use Jira as a project management tool. It does not count companies that originally signed up for [Confluence](https://bloomberry.com/data/confluence/), [Jira Service Management](http://bloomberry.com/data/atlassian-jira-service-desk/), or another Atlassian product 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.