The 2025 Docker State of Application Development Report<\/em><\/strong> offers an ultra high-resolution view of today\u2019s fast-evolving dev landscape. Drawing insights from over 4,500 developers, engineers, and tech leaders \u2014 three times more users than last year \u2014 the survey explores tools, workflows, pain points, and industry trends. In this our third report, key themes emerge: AI is gaining ground but adoption remains uneven; security is now a shared responsibility across teams; and developers still face friction in the inner loop despite better tools and culture. With a broader, more diverse respondent base than our previous more IT-focused surveys, this year\u2019s report delivers a richer, more nuanced view of how modern software is built and orgs operate.<\/p>\n IT is among the leaders in AI <\/strong>\u2014 with 76% <\/strong>of <\/strong>IT\/SaaS pros using AI tools at work, compared with just 22%<\/strong> across industries. Overall, there\u2019s a huge spread across industries \u2014 from 1% to 84%.\u00a0<\/p>\n Security is no longer a siloed specialty<\/strong> \u2014 especially when vulnerabilities strike. Just 1 in 5 organizations outsource security<\/strong>, and it\u2019s top of mind at most others: only 1% of respondents say security is not a concern at their organization.<\/p>\n Container usage soared to 92% in the IT industry <\/strong>\u2014 up from 80% in our 2024 survey. But adoption is lower across other industries, at 30%<\/strong>. IT\u2019s greater reliance on microservice-based architectures \u2014 and the modularity and scalability that containers provide \u2014 could explain the disparity.<\/p>\n Non-local dev environments are now the norm \u2014 not the exception<\/strong>. In a major shift from last year, 64%<\/strong> of developers say they use non-local environments<\/strong> as their primary development setup<\/strong>, with local environments now accounting for only 36%<\/strong> of dev workflows.\u00a0<\/p>\n Data quality is the bottleneck<\/strong> when it comes to building AI\/ML-powered apps \u2014 and it affects everything downstream. 26% of AI builders<\/strong> say they\u2019re not confident in how to prep the right datasets \u2014 or don\u2019t trust the data they have.\u00a0<\/p>\n Like last year\u2019s survey, our 2025 report drills down into three main areas<\/strong>:<\/p>\n\n What\u2019s helping devs thrive \u2014 and what\u2019s still holding them back?<\/p>\n AI is changing software development \u2014 but not how you think<\/p>\n Security \u2014 it\u2019s a team sport<\/p>\n Great culture, better tools \u2014 but developers often still hit sticking points. From pull requests held up in review to tasks without clear estimates, the inner loop remains cluttered with surprisingly persistent friction points.<\/p>\n Self-guided learning is on the upswing<\/strong>. Across all industries, fully 85%<\/strong> of respondents turn to online courses or certifications, far outpacing traditional sources like school (33%<\/strong>), books (25%<\/strong>), or on-the-job training (25%<\/strong>).\u00a0<\/p>\n\n Among IT folks, the picture is more nuanced. School is still the top venue<\/strong> for learning to code (65%<\/strong>, up from 57% in our 2024 survey), but online resources are also trending upward. Some 63%<\/strong> of IT pros learned coding skills via online resources (up from 54% in our 2024 survey) and 57%<\/strong> favored online courses or certifications (up from 45% in 2024).<\/p>\n\n As for how devs prefer to learn, reading documentation tops the list<\/strong>, as in last year\u2019s report \u2014 that despite the rise in new and interactive forms of learning. Some 29%<\/strong> say they lean on documentation, edging out videos and side projects (28% each<\/strong>) and slightly ahead of structured online training (26%<\/strong>).\u00a0<\/p>\n\n AI tools play a relatively minor role in how respondents learn, with GitHub Copilot cited by just 13%<\/strong> overall \u2014 and only 9%<\/strong> among IT pros. It\u2019s also cited by 13%<\/strong> as a preferred learning method.<\/p>\n\n When learning to code via online resources<\/strong>, respondents overwhelmingly favored technical documentation (82%<\/strong>) ahead of written tutorials and how-to videos (66%<\/strong> each), and blogs (63%<\/strong>).<\/p>\n\n Favorite online courses or certifications<\/strong> included Coursera (28%<\/strong>), LinkedIn Learning (24%<\/strong>), and Pluralsight (23%<\/strong>).<\/p>\n\n When it comes to finding out about new tools<\/strong>, developers tend to trust the opinions and insights of other developers \u2014 as evidenced by the top four selected options. Across industries, the main ways are developer communities, social media, and blogs (tied at 23%<\/strong>), followed closely by friends\/colleagues (22%<\/strong>).<\/p>\n Within the IT industry only, the findings mirror last year\u2019s, though blogs have moved up from third place to first. The primary ways devs learn about new tools are blogs (54%<\/strong>), developer communities (52%<\/strong>), and social media (50%<\/strong>), followed by searching online (48%<\/strong>) and friends\/colleagues (46%<\/strong>).Conferences still play a significant role, with 34%<\/strong> of IT folks selecting this response, versus 17%<\/strong> across industries.<\/p>\n\n Unsurprisingly, IT industry workers are more active in the open source space<\/strong>:\u00a0<\/p>\n\n 48%<\/strong> contributed to open source in the past year, while 52%<\/strong> did not.\u00a0<\/p>\n That\u2019s a slight drop from 2024, when 59% reported contributing and 41% had not.<\/p>\n Falling open source contributions could be worth keeping an eye on \u2014 especially with growing developer reliance on AI coding copilots. Could AI be chipping away at the need for open source code itself? Future studies could reveal whether this is a blip or a trend.<\/p>\n\n Across industries, just 13%<\/strong> made open source contributions, while 87% <\/strong>did not. But the <\/strong>desire<\/em><\/strong> to contribute is widespread<\/strong> \u2014 spanning industries as diverse as energy and utilities (91%<\/strong>), media or digital and education (90% <\/strong>each), and IT and engineering or manufacturing (82%<\/strong> each).<\/p>\n\n Mirroring our 2024 study, the biggest barrier to contributing to open source is time<\/strong> \u2014 24%<\/strong>, compared with 40% in last year\u2019s IT-focused study. Other barriers include not knowing where to start (18%<\/strong>) and needing guidance from others on how to contribute (15%<\/strong>).<\/p>\n\n Employers are often supportive: 37%<\/strong> allow employees to contribute to open source, while just 29%<\/strong> do not.<\/p>\n\n This year, we dove deeper into\u00a0 the tech stack landscape to understand more about the application structures, languages, and frameworks devs are using \u2014 and how they have evolved since the previous survey.<\/p>\n\n Asked about the structure of the applications they work on<\/strong>, respondents\u2019 answers underscored the continued rise of microservices we saw in our 2024 report.<\/p>\n\n Thirty-five percent said they work on microservices-based applications <\/strong>\u2014 far more than those who work on monolithic or hybrid monolith\/microservices (24%<\/strong> each), but still behind the 38%<\/strong> who work on client-server apps.<\/p>\n\n The tides have officially turned. In 2025, 64%<\/strong> of developers say they use non-local environments<\/strong> as their primary development setup, up from just 36% last year. Local environments \u2014 laptops or desktops \u2014 now account for only 36%<\/strong> of dev workflows.<\/p>\n\n What\u2019s driving the shift? A mix of flexible, cloud-based tooling:<\/p>\n\n Ephemeral or preview environments<\/strong>: 10% (\u2193 from 12% in 2024)<\/p>\n Personal remote dev environments or clusters<\/strong>: 22% (\u2191 from 11%)<\/p>\n Other remote dev tools<\/strong> (e.g., Codespaces, Gitpod, JetBrains Space): 12% (\u2191 from 8%)<\/p>\n Compared to 2024, adoption of persistent, personal cloud environments has doubled, while broader usage of remote dev tools is also on the rise.<\/p>\n\n Bottom line:<\/strong> As we\u2019ve tracked since our first app dev report in 2022, the future of software development is remote, flexible, and increasingly cloud-native.<\/p>\n\n Internal Developer Portals (IDPs) may be buzzy, but adoption is still in early days. Only 7% of developers<\/strong> say their team currently uses an IDP, while 93%<\/strong> do not. When it comes to operating systems for app development, Linux continues to lead the pack<\/strong>, used by 53%<\/strong> of developers \u2014 the same share as last year. macOS<\/strong> usage has ticked up slightly to 51%<\/strong> (from 50%), while Windows<\/strong> trails just behind at 47%<\/strong> (up from 46%).<\/p>\n\n The gap among platforms remains narrow, suggesting that today\u2019s development workflows are increasingly OS-flexible \u2014 and often cross-platform.<\/p>\n\n Python is now the top language among developers, used by 64%<\/strong>, surpassing JavaScript<\/strong> at 57%<\/strong> and Java<\/strong> at 40%<\/strong>. That\u2019s a big jump from 2024, when JavaScript held the lead.<\/p>\n\n Framework usage is more evenly spread: Spring Boot<\/strong> leads at 19%<\/strong>, with Angular<\/strong>, Express.js<\/strong>, and Flask<\/strong> close behind at 18%<\/strong> each.<\/p>\n\n In 2025, MongoDB<\/strong> leads the pack at 21%<\/strong>, followed closely by MySQL\/MariaDB<\/strong> and Amazon RDS<\/strong> (both at 20%<\/strong>). That\u2019s a notable shift from 2024, when PostgreSQL<\/strong> (45%<\/strong>) topped the list.<\/p>\n\n GitHub<\/strong>, VS Code<\/strong>, and JetBrains editors<\/strong> remain top development tools, as they did in our previous survey. And there\u2019s little change across CI\/CD, provisioning, and monitoring tools:<\/p>\n\n CI\/CD<\/strong>: GitHub Actions (40%), GitLab (39%), Jenkins (36%)<\/p>\n Provisioning<\/strong>: Terraform (39%), Ansible (35%), GCP (32%)<\/p>\n Monitoring<\/strong>: Grafana (40%), Prometheus (38%), Elastic (34%)<\/p>\n Among IT pros, container usage soared to 92%<\/strong> \u2014 up from 80% in our 2024 survey. Zoom out to a broader view across industries, however, and adoption appears considerably lower. Just 30%<\/strong> of developers say they use containers in any part of their workflow.\u00a0<\/p>\n\n Why the gap? Differences in app structure may offer an explanation: IT industry respondents work with microservice-based architectures more often than those in other industries (68%<\/strong> versus 31%<\/strong>). So the higher container adoption may stem from IT pros\u2019 need for modularity and scalability \u2014 which containers provide in spades.<\/p>\n And among container users, needs are evolving. They want better tools for time estimation (31% <\/strong>compared to 23% of all respondents), task planning (18% for both container users and all respondents), and monitoring\/logging (16%) <\/strong>vs designing from scratch (18%) in the number 3 spot for all respondents \u2014 stubborn pain points across the software lifecycle.<\/p>\n\n No matter the role, estimating how long a task will take is the most consistent pain point<\/strong> across the board. Whether you\u2019re a front-end developer (28%<\/strong>), data scientist (31%<\/strong>), or a software decision-maker (49%<\/strong>), precision in time planning remains elusive.<\/p>\n Other top roadblocks? Task planning (26%)<\/strong> and pull-request review (25%)<\/strong> are slowing teams down. Interestingly, where people say they need better tools doesn\u2019t always match where they\u2019re getting stuck. Case in point, testing solutions and Continuous Delivery (CD)<\/strong> come up often when devs talk about tooling gaps \u2014 even though they\u2019re not always flagged as blockers.<\/p>\n\n When you break it down by role, some unique themes emerge:<\/p>\n\n Experienced developers<\/strong> struggle most with time estimation (42%<\/strong>).<\/p>\n Engineering managers<\/strong> face a three-way tie: planning, time estimation, and designing from scratch (28% each)<\/strong>.<\/p>\n Data scientists<\/strong> are especially challenged by CD (21%)<\/strong> \u2014 a task not traditionally in their wheelhouse.<\/p>\n Front-end devs<\/strong>, surprisingly, list writing code (28%)<\/strong> as a challenge, closely followed by CI (26%)<\/strong>.<\/p>\n Across roles, a common thread stands out: even seasoned professionals are grappling with foundational coordination tasks \u2014 not the \u201chard\u201d tech itself, but the orchestration around it.<\/p>\n\n On the tooling side, the biggest callouts for improvement across industries include:<\/p>\n\n Time estimation (23%)<\/strong><\/p>\n Task planning (18%)<\/strong><\/p>\n Designing solutions from scratch (18%)<\/strong><\/p>\n Within the IT industry specifically, the top priority is the same \u2014 but even more prevalent:<\/p>\n\n Time estimation (31%)<\/strong><\/p>\n Task planning (18%)<\/strong><\/p>\n PR review (18%)<\/strong><\/p>\n But productivity isn\u2019t just about tools \u2014 it\u2019s deeply cultural. When asked what\u2019s working well, developers pointed to work-life balance (39%), location flexibility such as work from home policies (38%)<\/strong>, and flexible hours (37%) <\/strong>as top cultural strengths.<\/p>\n The weak spots? Career development (38%), recognition (36%)<\/strong>, and meaningful work (33%)<\/strong>. In other words: developers like where, when, and how they work, but not always why.<\/p>\n\n While the dev world is full of moving parts, a few areas are surprisingly not<\/em> challenging:<\/p>\n\n Editing config files (8%)<\/strong><\/p>\n Debugging in dev (8%)<\/strong><\/p>\n Writing config files (7%)<\/strong><\/p>\n Contrast that with the most taxing areas:<\/p>\n\n Troubleshooting in production (9%)<\/strong><\/p>\n Debugging in production (9%)<\/strong><\/p>\n Security-related tasks (8%)<\/strong><\/p>\n It\u2019s a reminder that production is still where the stress \u2014 and the stakes \u2014 are highest.<\/p>\n\n Rumors of AI\u2019s pervasiveness in software development have been greatly exaggerated. A look under the hood shows adoption is far from uniform<\/strong>.\u00a0<\/p>\n One of the clearest splits we saw in the data is how people use AI at work. There are two groups:<\/p>\n\n Developers using AI tools <\/strong>like ChatGPT, Copilot, and Gemini to help with everyday tasks \u2014 writing, documentation, and research.<\/p>\n Teams<\/strong> building AI\/ML applications<\/strong> from the ground up.<\/p>\n Overall, only 22%<\/strong> of respondents said they use AI tools at work. But that number masks a huge spread across industries \u2014 from 1% to 84%<\/strong>. IT\/SaaS folks sit near the top of the range at 76%<\/strong>.\u00a0<\/p>\n IT\u2019s leadership in AI is even more marked when you look at how many are building AI\/ML into apps:\u00a0<\/p>\n\n 34% of IT\/SaaS respondents<\/strong> say they do.<\/p>\n Only 8% of non-tech industries<\/strong> can say the same.<\/p>\n And the strategy gap is just as big<\/strong>. While 73%<\/strong> of tech companies say they have a clear AI strategy, only 16%<\/strong> of non-tech companies do. Translation: AI is gaining traction, but it\u2019s still living mostly inside tech bubbles.<\/p>\n\n Here\u2019s the paradox: while 59%<\/strong> of respondents say AI tools are overhyped, 64%<\/strong> say they make their work easier.<\/p>\n\n Even more telling, 65% of users say they\u2019re using AI more now than they did a year ago<\/strong> \u2014 and that they use it daily.<\/p>\n\n The hype is real. But for many devs, the utility is even more real.<\/p>\n\n These numbers track with our 2024 findings, too \u2014 where nearly two-thirds of devs said AI made their job easier, even as 45% weren\u2019t fully sold on the buzz.<\/p>\n\n No big surprise here. ChatGPT is still the most-used AI tool by far<\/strong>. But the gap is growing.<\/p>\n Compared to last year\u2019s survey:<\/p>\n\n ChatGPT<\/strong> usage jumped from 46% \u2192 82%<\/p>\n GitHub Copilot<\/strong>: 30% \u2192 57%<\/p>\n Google Gemini<\/strong>: 19% \u2192 22%<\/p>\n Expect that trend to continue as more teams test drive (and trust) these tools in production workflows, moving from experimentation to greater integration.<\/p>\n\n While coding is the top AI use case overall, how devs actually lean on it varies by role:<\/p>\n\n Seasoned devs<\/strong> use AI for documentation and writing tests \u2014 but only lightly.<\/p>\n DevOps engineers<\/strong> use it to write docs and navigate CLI tools.<\/p>\n Software developers<\/strong> often turn to it for research and test automation.<\/p>\n2025 report key findings<\/h2>\n\n
Developer productivity, AI, and security are key themes<\/h2>\n
1. What\u2019s helping devs thrive \u2014 and what\u2019s still holding them back?<\/h2>\n\n
How devs learn \u2014 and what\u2019s changing<\/h3>\n\n
How devs like to learn<\/h3>\n\n
Online resources<\/h3>\n\n
Discovering new tools<\/h3>\n\n
Open source contributions<\/h3>\n\n
Tech stack<\/h3>\n\n
Application structure<\/h4>\n\n
Non-local dev environments are now the norm \u2014 not the exception<\/h4>\n\n
IDP adoption remains low \u2014 except at larger companies<\/h4>\n\n
That said, usage climbs with company size. Among orgs with 5,000+ employees, IDP adoption jumps to 36%<\/strong>. IDPs aren\u2019t mainstream yet \u2014 but at scale, they\u2019re proving their value.<\/p>\n\nOS preferences hold steady \u2014 with Linux still on top<\/h4>\n\n
Python surges past JavaScript in language popularity<\/h4>\n\n
Data store preferences are shifting<\/h4>\n\n
Tool favorites hold<\/h4>\n\n
Containers: the great divide?<\/h3>\n\n
An equal-opportunity headache: estimating time<\/h3>\n\n
Productivity by role: different hats, same struggles<\/h3>\n\n
Tools vs. culture: two sides of the experience equation<\/h3>\n\n
What\u2019s easy? What\u2019s not?<\/h3>\n\n
2. AI is changing software development \u2014 but not how you think<\/h2>\n\n
AI in dev workflows: two very different camps<\/h3>\n\n
IT is among the leaders in AI\u00a0<\/h3>\n\n
AI tools: overhyped and indispensable<\/h3>\n\n
AI tool usage is up \u2014 and ChatGPT leads the pack<\/h3>\n\n
Not all devs use AI the same way<\/h3>\n\n