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Why a satisfaction score of 6 in Japan might be better than an 8 in China If you’ve ever looked at the same satisfaction question broken down by country and thought, “ Why are these numbers so wildly different? ” – you’re not alone. In global research, interpreting data responsibly is one of the hardest parts. At SlashData, we run developer studies across regions year after year (including our rolling Developer Program Benchmarking (DPB) , where we help vendors identify concrete improvement paths for their developer programs). One pattern shows up consistently: the way respondents use rating scales is deeply cultural. A satisfaction heatmap promises a unified view of performance. Executives can scan rows and columns for the green of success or the red of failure. Yet, once that data spans continents, it often conceals as much as it reveals. As we will see throughout this post, a growing body of research suggests that the standardised metric of customer satisfaction is often just a map of cultural biases. Without a cultural lens, your global heatmap isn't just a distorted mirror – it’s a dangerous map that can lead to strategic missteps, from the misallocation of resources to the unfair penalisation of high-performing regional teams. For simplicity, in this blog, we will assume a satisfaction scale from 0 to 10. Treat global satisfaction scores as directly comparable, and you can end up misallocating budget, fixing markets that aren’t broken, or missing early warning signals in markets that look healthy. The "Optimists" vs the "Sceptics" (and the Japan paradox) A quick glance at our DPB vendor satisfaction cuts often reveals a geographic divide. North American respondents are frequently at the high end. We also see a cluster of high-scoring Southeast and East Asian markets – especially the Philippines, Vietnam, Indonesia, and China. On the other hand, Japan consistently shows up as more conservative in its ratings, and we often see the same tougher grading in parts of Western Europe, including Germany and the Netherlands. This is not just a SlashData thing. Ipsos specifically flags that the Philippines, Indonesia, and Vietnam give high scores, while other Asian markets provide much lower scores, explicitly including Japan in the low-scoring set. And SurveyMonkey ’s cross-country NPS study shows just how dramatic this can be on a simple 0-10 “recommend” scale: Japan is the lowest of the markets they studied, while the United States and Canada are much higher, and the Netherlands sits well behind many other countries. If taken at face value, this data would suggest that developer programmes are thriving in North America but failing to impress in Western Europe. But does this align with actual retention rates? Often, the answer is no. This disconnect is likely driven by a few powerful cultural forces, like optimism bias and polite agreement on the one hand, and scepticism on the other.  High satisfaction scores can coexist with low developer retention, especially across regions. North America & Emerging Asia are “optimists” when it comes to surveys Research indicates that in cultures prioritising social harmony, such as some Asian markets (often correlated with high collectivism), respondents are predisposed to be agreeable. For high-scoring Asian markets (like the Philippines, Vietnam, China, Indonesia), one driver is often what researchers call agreement and harmony effects: in some cultures, direct negative feedback is less comfortable, and respondents can lean toward more socially acceptable, relationship-preserving answers. S ome markets systematically use the top end of the scale more than others, enough to reorder league tables without any real change in underlying experience. In high-agreement environments, a score of 8-9 can be closer to “fine” than “fanatically loyal”. The danger is overconfidence. In the US and Canada, high scores are often driven by Optimism Bias . Culturally, there is a tendency to view things in a positive light. “Good” is often rated as “Great”. As noted in global NPS studies by SurveyMonkey , American respondents consistently score higher than their European counterparts for the same service levels. Western Europe & Japan are the “sceptics” when it comes to surveys On the other side of the chart, we see Western Europe (e.g. Germany, the Netherlands) and Japan hovering at the bottom of the scale. Western Europe is home to the “ Dutch effect ” or sober grading. In these cultures, hyperbole is viewed with suspicion. A score of 10 is reserved for perfection – a standard almost no B2B service achieves. Japan is the ultimate outlier in our data. Despite being geographically close to the high-scoring Asian nations, it often produces the lowest satisfaction scores in the world. Multiple other studies reveal this pattern: in Japan, service expectations are famously high. A minor friction point that an American consumer might forgive is often punished harshly by Japanese consumers in surveys. Importantly, lower scores don’t automatically mean unhappiness. It often means that top ratings are reserved for rare perfection, and the cultural norm is to score more conservatively.  The cultural bias disconnect: confusing ratings with retention The biggest error a vendor can make is assuming a linear, universal relationship between these scores and churn. In harder-grading markets like Japan and some of Western Europe, a 6 or 7 can still behave like a loyal consumer. If you treat every sceptic as a problem, you risk wasting resources fixing relationships that aren’t broken – or worse, annoying stable customers with constant nudges to “rate us a 10.” In high-scoring markets, the risk flips. If the baseline is inflated, then a drop from 9 to 8 might not be just noise – it can be your early warning signal. And if the culture discourages direct complaints, you may not hear why until the customer leaves. If direct criticism is culturally discouraged, customers can look satisfied right up until they churn. Your first visible signal can be cancellation, not feedback. Adding a culturally intelligent framework to your research To navigate this landscape, vendors and clients must move beyond raw comparisons and adopt a relative, culturally intelligent framework. Benchmark intranationally, not internationally  Stop asking, " Why is our German market less satisfied than the Chinese market? " The correct question is, " How does our German score compare to the German benchmark? " If the regional average is a 6.5 and you score a 7.0, you are likely a market leader. Create country indices that normalise scores against the local average to reveal true performance. This is where tailored services like our Developer Program Benchmarking shine – we help you normalise scores against local averages to reveal true performance. Adjust your internal thresholds In “stoic” markets like parts of Western Europe and Japan, a 7 or 8 can be a genuine win. In high-scoring markets like some East Asian countries, treat anything below the local norm as a potential signal – especially if it’s trending down. Use ranking over rating  Where applicable, complement ratings with questions that force trade-offs. Ranking-style questions (“ Which programme is best? ”) are harder to game through polite scale use than “rate each one 0-10,” and they often reveal the competitive truth hiding beneath the friendliness. Conclusion A red Japan does not necessarily mean a failing program, nor does a green China guarantee a secure future.  By interpreting survey data through a cultural lens – acknowledging the sceptics, the polite promoters, and the scale's structural biases – you get closer to the true voice of the customer. The goal is not to standardise the customers, but to sophisticate the analysis so your data predicts what users will actually do next. Are you worried that your retention strategy is based on skewed heatmaps? We can audit your satisfaction data to separate real performance issues from cultural noise. Contact us today! About the author Bleona Bicaj - Principal Research Consultant Bleona is a research consultant, enthusiastic about product strategy and behavioural science. She holds a Master’s in Economic and Consumer Psychology. With more than 6 years of professional experience as an analyst, she has worked across quantitative and qualitative research studies, turning complex data into clear narratives that inform better products, smarter investments, and long-term growth.

Throughout the development world, we are seeing two competing approaches to DevOps maturity: developer empowerment or business focusing. Both models aim to ensure that their organisations are able to increase their developer velocity, ship securer code, and be able to respond to feedback and demands, but take diametrically opposed approaches to do so.  In this article, we explore both approaches: where each excels, what challenges they create, and how they manifest in real development teams. Drawing on data from SlashData's 30th Developer Nation Survey (which reached more than 10,000 developers globally in summer 2025), we'll show how these philosophical differences translate into concrete security practice adoption patterns, and why organisations should choose based on their specific context rather than industry trends.  Developer Empowerment, Autonomy and Visibility Those who follow the developer empowerment model focus on ensuring their developers are knowledgeable, informed, and have autonomy and visibility over their DevOps processes. Organisations adopting this approach typically value developer satisfaction and retention highly. This model hopes that recognition of experienced developers desire to control their toolchains and their resistance to imposed limitations will create happier developers who are willing to experiment freely. The organisations can provide guidance, approved vendor lists, or internal documentation, but ultimately they leave the decision to the ground-floor developers.  The challenge with this is consistency. Security practices may vary between teams, which can lead to blind spots. While individual developers, or teams, may have high levels of visibility into their processes and build up deep familiarity with security practices, the lack of consistency can lead to blind spots in the organisation-wide security posture. Adding to this challenge is that knowledge can become siloed within teams, with successful approaches not being shared with others. At its worst, developers who lack security experience can have their autonomy instead become a liability rather than an asset. However, while decentralised approaches to security risks gaps, it also allows developers to react very quickly to new vulnerabilities without having to wait on a central platform team.  In our current examination, this can include developers who are provided a curated list of tools for their  selection and configuration (34% of professional developers). This leads to a slightly higher adoption of IDE security checks (32%), pre-commit hooks (20%), and container-scanning(28% ) integrated into their CI/CD pipelines, as they are selecting the tools that they interact with during development.  Business Focus: Abstraction and Efficiency The other approach is business-focused, where the goal is to abstract away the concerns about security, infrastructure, deployment, and other DevOps processes behind an IDP or a controlled list of tooling configured for them (27% of professional developers). This aims to allow the developers to focus more on addressing business needs, and their core responsibilities, rather than having to consider wider aspects of the software development lifecycle. This approach emerges from different organisational priorities, including consistency at scale, meeting compliance requirements, or protecting specific business interests, even if it means constraining developers' choices. This can become especially true for companies with hundreds or thousands of developers, where complete heterogeneity of tooling can create maintenance headaches. In addition, organisations that want to prioritise their developer time on product differentiation, or need to onboard developers rapidly, a centralised process supports both of these. This aims to allow the developers to focus more on addressing business needs, and their core responsibilities, rather than having to consider wider aspects of the software development lifecycle. In practice, this can manifest as developers interacting with an IDP with abstracted interfaces. When a developer might deploy to staging, they may not be aware whether this instruction triggers Kubernetes, ECS, or Cloud Run behind the scenes. Within this approach, security checks happen automatically in the pipeline, where developers see the results but don’t necessarily configure these themselves. With these developers, we see higher rates of SCA (29%), DAST(26%), and IAST (27%) practices built into CI/CD pipelines because these happen behind-the-scenes for developers, which are benefitted by having highly centralised platforms.  However, despite the benefits to organisations, and developers, these systems risk creating ‘black box’ problems. If developers don’t understand what is happening behind the abstraction, they can become less effective at debugging, and have a shallower understanding of security practices. Additionally, platform teams can risk becoming bottlenecks, with every new tool or feature request platform team time. This can leave developers unable to work, or risk them engaging in shadow IT and compromising the goals of centralising security practices The False Choice Neither approach is inherently better or worse than the other. Every few years thought leaders emerge to declare that development teams should shift-left or shift-right  as the ‘correct’ way to do development, or to unlock previously unimaginable benefits. However, the reality is that simply shifting  doesn’t actually do anything, and it is instead the processes, practices, and culture within organisations and development teams that have the largest impact, and centralising or decentralising are just mechanisms to achieve this.  What matters instead is for organisations to consider other factors that will motivate them, and what capabilities it instead needs: faster feedback loops, comprehensive security coverage, developer satisfaction, or operational reliability. Some of these benefit from centralisation, and others from distribution, and organisations frequently blend aspects together to meet their specific needs.  What to consider when choosing a DevOps approach   Rather than asking 'which approach is better?', organisations should ask 'what does our context demand?'. Consider: Organisational size and growth trajectory: A 50-person startup might start with curated lists, knowing they'll need an IDP at 500 people Team security maturity: Less experienced teams may need more guardrails; senior teams may resent them Regulatory requirements: Financial services or healthcare often require centralised control and audit trails Cultural values: Does your organisation optimise for innovation speed or operational consistency? Platform team capacity: Building an IDP requires sustained investment—do you have the people and time? Your choice isn't permanent. Many organisations start with developer autonomy and gradually centralise as they scale. Others go the opposite direction, decentralising after realising their IDP became a bottleneck. The key is being intentional about the trade-offs you're making and regularly reassessing whether your approach still serves your needs. Our team of analysts can help you decide on the best option, using concrete data to help your decision-making. Let’s talk and find the solution that works for you.  About the author Liam Bollmann-Dodd Principal Market Research Consultant at SlashData Liam is a former experimental antimatter physicist, and he obtained a PhD in Physics while working at CERN. He is interested in the changing landscape of cloud development, cybersecurity, and the relationship between technological developments and their impact on society.

With AI taking centre stage these days, we thought we'd take a moment to step out from behind the algorithms. And say something simple: Thank you. Thank you for trusting the brains and hearts behind SlashData to help you make sense of the ever-expanding universe of AI and data. 🎇 From all of us, wishing you a joyful, curious, and very Happy New Year! 🥳 Happy New Year from  Alex ,  Álvaro ,  Andreas ,  Berkol ,  Bleona ,  David ,  Evgenia ,  Jed ,  Liam ,  Maria ,  Máté ,  Mina ,  Natasa ,   Nikita ,  Petro ,  Sarah  and  Stathis ! ❤️

AI Technology tools tracker compares AI coding tools based in developer adoption, performance, quality and trust, including: GitHub Copilot, Claude Code, OpenAI Codex, Cursor Windsurf, Gemini Code Assist, Amazon Q Developer, JetBrains AI, Replit, Firebase Studio, Sourcegraph Amp, Tabnine, Mistral Code, aider, GitLab Duo, Cline Get a detailed walkthrough: match these insights to your product. Schedule a detailed briefing with our analysts. BOOK A BRIEFING About More How developers feel about the AI coding tools they are using Meet the AI Technology tools benchmark Compare the 16 leading coding tools based on developer adoption, performance, quality and trust. Stop investing in hype. Power decisions on data, bring results. Build your product based on what developers using coding AI tools actually expect Respond to performance pressure with clear actions, backed by data Leave guesswork aside Foggy competitor landscape? Not anymore Capitalise on the areas where your competitors are lagging. The free version offers a comparison of Claude Code, Codex and Cursor . The full version benchmarks all 16 tools. ACCESS THE FREE REPORT ACCESS THE FULL REPORT Turn risky AI decisions into product roadmaps that drive results, adoption and revenue. Invest on what developers are looking and are willing to pay for, not hype Drive adoption, revenue and growth through the right product roadmap Find the right AI tool for the job YOU are doing Optimise performance, build a happy developer team The solution for AI technology vendors Benchmark product performance on awareness, adoption , engagement and satisfaction Discover what drives engagement Identify gaps in your competitors' products AI technology buyers Reduce risk by choosing the more trusted tool Choose the right tool for your stage in the lifecycle Choose the right tools for your team Set expectations on productivity and quality They trust us Decision clarity based on developer data and insights SCHEDULE A CALL Company About Culture & Careers Blog Methodology Contact us Resources Case studies Research Space Free Industry Reports Webinars Podcast Book Research Developer Ecosystem Insights Developer Program Benchmarking Competitive Technologies Landscape Data Dashboards Developer Population Sizing Free Industry Reports Research Space Methodologies Qualitative Research Quantitative Research Competitive Market Research NEWSLETTER Services All Services Product Development Brand Research Product Configuration & Optimisation Competitive Market Intelligence Audience Insights Customer Segmentation & Persona Insights SlashData © Copyright 2025 | All rights reserved | Cookie Policy | Privacy Policy AI Coding Tools Benchmark Stop investing in hype. Power AI decisions on real developer signals. Build your product based on what developers using coding AI tools actually expect Respond to performance pressure with clear actions, backed by data Leave guesswork aside Foggy competitor landscape? Not anymore Capitalise on the areas where your competitors are lagging. How are developers using... Replit Firebase Studio Sourcegraph Amp Tabnine Mistral Code aider GitLab Duo Cline GitHub Copilot Claude Code OpenAI Codex Cursor Windsurf Gemini Code Assist Amazon Q Developer JetBrains AI ...and how do they feel about it? Turn risky AI decisions into product roadmaps that drive results, adoption and revenue. Invest on what developers are looking and are willing to pay for, not hype Drive adoption, revenue and growth through the right product roadmap Find the right AI tool for the job YOU are doing Optimise performance, build a happy developer team

The aim of this report is to investigate the current state of the MLDS landscape through the exploration of key practices amongst MLDS developers. We begin by examining how developers engage with MLDS projects and where MLDS developers execute their code. Following this, we take a close look at the programming languages and algorithms that MLDS developers use. Finally, we discuss the types of data that developers use in their MLDS projects and where that data comes from. All Insights The state of machine learning and data science Examining code execution, algorithms, and data practices Access the Full Preview About this Report The aim of this report is to investigate the current state of the MLDS landscape through the exploration of key practices amongst MLDS developers. We begin by examining how developers engage with MLDS projects and where MLDS developers execute their code. Following this, we take a close look at the programming languages and algorithms that MLDS developers use. Finally, we discuss the types of data that developers use in their MLDS projects and where that data comes from. Key Questions Answered How are developers involved in data science, machine learning, and artificial intelligence (AI)? Where does the code of MLDS developers run? Which programming languages are used in MLDS projects? Which algorithms and approaches are used in MLDS projects? What types of data do MLDS developers work with? Where does the data used in MLDS projects come from? Click to expand ACCESS THE FULL PREVIEW Methodology The report is based on data collected from the 28th edition of the Developer Nation survey edition of the Developer Nation survey, a large-scale, online developer survey that was designed, hosted, and fielded by SlashData over a period of five weeks between September and October 2024. Questions? Let's talk! Fill the form. Natasa and Petro will help you drive developer adoption: Name Email I have read and agree to SlashData's Privacy Policy and I want to be contacted. GET IN TOUCH WITH ME Contact us First name* Last name* Work Email* Company * Role* Message I agree to SlashData's Privacy Policy and I want to be contacted * SUBMIT

This report investigates the state of integration of open and fully open-source artificial intelligence (AI) models by developers who add AI functionality to their applications. While focusing on the broader picture, we explore the types of functionalities developers use these models for, why they choose them over their proprietary alternatives, and the challenges they face. In addition to this, we also take a brief look at the reason why developers might avoid using open or open-source models in their applications. All Insights Developers’ experience with integrating AI functionality A focus on open and open-source AI models Access the Full Preview About this Report This report investigates the state of integration of open and fully open-source artificial intelligence (AI) models by developers who add AI functionality to their applications. While focusing on the broader picture, we explore the types of functionalities developers use these models for, why they choose them over their proprietary alternatives, and the challenges they face. In addition to this, we also take a brief look at the reason why developers might avoid using open or open-source models in their applications. Key Questions Answered Where are developers using network APIs located? What type of projects are network API users building? What types of network APIs are developers using? How do professional involvement and company size influence the implementation of network APIs? What challenges do developers face when using network APIs? How do developers prefer to pay for network APIs? How do pricing preferences differ between regions and companies of different sizes? Click to expand ACCESS THE FULL PREVIEW Questions? Let's talk! Fill the form. Natasa and Petro will help you drive developer adoption: Name Email I have read and agree to SlashData's Privacy Policy and I want to be contacted. GET IN TOUCH WITH ME Contact us First name* Last name* Work Email* Company * Role* Message I agree to SlashData's Privacy Policy and I want to be contacted * SUBMIT Methodology The report is based on data collected in the 27th edition of SlashData’s global Developer Nation survey, which was fielded between June and July 2024. This survey reached over 2,000 developers who add AI functionality to their applications and answered questions about their experiences with open and open-source models.