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- Support for VPC, IAM policies, monitoring, and testing at production-grade levels - The emergence of serverless DevOps infrastructures, fully managed through IaC ... - Background tasks (ETL, image/video processing) - Incident management and automatic rollback - ML inference and scheduled report generation

- 45% of DevOps leaders encounter cultural resistance as a major impediment. - 31% of DevOps leaders said a lack of skilled resources is their biggest challenge. - Legacy systems and infrastructure are a problem for 29% of DevOps leaders - Other issues include a lack of tool integration, the difficulty of selecting the appropriate metrics to measure success, and the challenge of security and compliance.

## Problem 4: Too much DevOps time is wasted on repetitive tasks Many DevOps tasks can be quite repetitive: writing Terraform templates, piecing together long cloud CLI commands, parsing JSON files, or combing through endless pod logs. These tasks aren’t complex, but they are time-consuming, tedious, and take attention away from solving real problems. … - Generating infrastructure code such as Terraform snippets or YAML manifests instead of writing boilerplate by hand. - Creating cloud CLI commands and scripts on demand, e.g. finding AWS autoscaling groups and ECS services across regions, scaling them up, and triggering new deployments with a single prompt. - Analyzing logs automatically, highlighting suspicious entries, frequent errors, or summaries. … ## Problem 5: Managing infrastructure as code across different tools Working with infrastructure as code means bouncing between different formats and technologies constantly – Terraform for provisioning, Helm charts for packaging and templating, and Kubernetes YAML for deployment. While you can add plugins in other editors, support is often inconsistent. Autocompletion may work for one tool but not another, validation can be inconsistent, and switching between terminal and editor tabs quickly gets messy.

By contrast, new adopters usually see instability because their feedback and validation systems lag behind the speed of automation. Because of that, roughly **30%** of developers still show little or no trust in AI output. This tells me maturity depends less on how much you adopt. ... … Teams with mature pipelines and automated testing achieve measurable gains in both speed and stability. Meanwhile, others see an uptick in rework, incident response delays, and cognitive overload. The data also reveals a clear trade-off. AI improves throughput, yet it also raises the change failure rate where feedback loops can’t keep pace. **And there’s a human cost as well.** Burnout, friction, and context switching rise when adoption is rushed. In the end, sustainable improvement happens only when AI deployment follows strong platform engineering foundations. Source ### Customization and Developer Experience One insight that stands out is how **adapting AI to existing developer environments** drives a better developer experience. The report notes that AI must meet developers where they already work (inside IDEs, repositories, and workflows). **Teams that tailor AI to their existing internal platforms experience less friction and higher satisfaction.** So, simple configuration changes can lower your team’s cognitive load and improve focus. This includes prompt tuning or repository filtering. When AI setups are rigid or uniform across teams, they only accelerate old bottlenecks. … - **Burnout** – the human cost of faster cycles and continuous AI interaction. - **Delivery instability** – volatility in throughput or quality as AI scales. - **Individual effectiveness** – the developer’s ability to harness AI responsibly and productively. These signals reveal whether speed actually translates into resilience and value across the software delivery lifecycle. When you look closer, low friction paired with high valuable work often predicts cleaner handoffs, steadier delivery, and fewer priority resets — the hallmarks of a mature, AI-empowered engineering culture. … For example, **High Impact–Low Cadence** teams show strong outcomes but uneven operations performance, a pattern that often hides thin on-call coverage or slow approvals cycles. **Constrained by Process** teams, by contrast, wrestle with excess coordination cost and brittle cultural practices, which erode their ability to adapt or scale AI practices effectively.

GitOps and Infrastructure as Code (IaC) are increasingly recognized as foundational DevOps practices, providing a robust framework for managing both infrastructure and application configurations. GitOps is rapidly becoming the standard approach, making Git repositories the single source of truth for declarative infrastructure and applications. ... Serverless computing and cloud-native architectures continue being major focus areas for DevOps teams in 2025. The serverless approach lets developers concentrate primarily on writing code without underlying infrastructure management complexities, leading to increased efficiency and faster development cycles. Microservices adoption, where applications are structured as small, independent service collections, is a key cloud-native architecture characteristic. Containerization technologies like Docker and orchestration platforms like Kubernetes are fundamental to this approach, enabling greater application scalability and resilience. ... ## Challenges and Solutions in DevOps Implementation in 2025 While DevOps benefits are widely acknowledged, organizations in 2025 continue facing several challenges when implementing and scaling these practices. Understanding these challenges and corresponding solutions is crucial for successful DevOps adoption. One primary hurdle is cultural resistance. Shifting to a DevOps model often requires significant organizational cultural change, involving breaking down traditional silos between development and operations teams and fostering collaboration and shared responsibility culture. To overcome this, organizations need to: - Emphasize clear communication across teams - Promote a culture of continuous learning and experimentation - Secure visible buy-in from leadership - Celebrate small wins to demonstrate value - Create cross-functional teams to break down silos Skill gaps represent another significant challenge. The DevOps landscape constantly evolves, requiring teams to possess diverse skill sets in areas like cloud computing, automation, and security. Essential steps to address these gaps include: - Investing in comprehensive training and upskilling programs - Hiring individuals with specialized DevOps expertise - Encouraging a culture of continuous learning and knowledge sharing - Creating mentorship programs to transfer skills internally - Providing access to learning resources and certifications The increasing number of tools available for various DevOps lifecycle stages can lead to tool integration and complexity challenges. Organizations need to streamline toolchains, adopt platform engineering approaches to provide integrated self-service platforms, and focus on tools offering better interoperability to mitigate this complexity. Ensuring security integration (DevSecOps) throughout the development process remains a critical challenge. Implementing DevSecOps practices by integrating security checks early and continuously, automating security testing and monitoring, and fostering collaboration between security and development teams are crucial solutions. Scaling DevOps practices across large and complex organizations can also be difficult. Adopting platform engineering principles, standardizing processes, breaking down complex tasks into smaller modules, and utilizing automation and centralized monitoring solutions can help organizations effectively scale DevOps initiatives. The presence of legacy systems often incompatible with modern DevOps tools and practices poses another challenge. Gradual migration of legacy systems to modern architectures, containerization and microservices use where feasible, and hybrid approach adoption can help integrate these systems into DevOps pipelines. Finally, selecting the right metrics to measure DevOps implementation success can be challenging. Organizations should focus on metrics aligning with business goals, such as throughput, stability, and user satisfaction, and use data-driven insights to identify improvement areas. … However, the journey of DevOps implementation is not without its challenges. Organizations must address cultural resistance, bridge skill gaps, manage tool complexity, ensure robust security integration, effectively scale practices, and contend with legacy systems. Proactive strategies and investments in continuous learning, automation, and fostering collaboration are essential to overcome these hurdles.

# 12 Biggest DevOps Challenges in 2025 (and How to Fix Them) ... DevOps teams in 2025 face 12 major challenges including cultural resistance, security vulnerabilities, tool selection paralysis, developer access barriers, poor visibility, governance difficulties, cost overruns, CI/CD performance issues, scalability problems, metric selection confusion, skill shortages, and over-focusing on

All too often they had conflicting goals and little insight into other “camps”. Developers would spend 3-4 months building a ton of features and then try to merge their code. This process was slow and tended to produce lots of errors. After a looooong integration, devs would hand their code to QA. … ## Step 5: Build your DevOps MVP The scale of change can be overwhelming, so it’s better to approach it in manageable chunks. Start with main pain points and identify where DevOps practices can bring the most benefits. - If major issues happen at the coding/build stage – start with CI/version control. - If you suffer from poor test coverage – invest in test automation. - If the pain comes from infrastructure and deployment – continuous delivery is the way to go. … Along the way, you might encounter some major possible setbacks: - Cloud costs that get out of hand. - System failures that trigger no alerts. - Issues with logging that prevent you from finding the failure causes. - Backups that don’t work. - Test automation scripts that fail routinely, etc. If you experience such issues, it might be worth a return to step 1 of a DevOps implementation strategy and assess the current state.

So, let’s get started on the real story of devops challenges and its solutions. 1. **The Cultural Shift Challenges in DevOps** This is usually the first great challenge, but most of the time it is not technical-it is cultural. There needs to be a dismantling of the silos between development, operations, QA, and sometimes even security teams. However, humans are creatures of habit. Developers tend to dislike doing operations tasks, while the ops team members are afraid of being replaced by automation; some management members do not quite believe in the DevOps mindset. … 1. **Tooling-DevOps Challenges** Ask any DevOps engineer-toolset is both boon and bane. The tools used in DevOps are Jenkins, GitLab CI, Docker and Kubernetes, along with observability tools like Prometheus, Grafana, and Splunk. Each claims to provide operational efficiencies, but when too many combined create an artificial monstrosity through complexity. DevOps Problem Statement “How do we manage dozens of tools without productivity just falling through the floor?” Working Theory for Solution Toolchain strategy-the pipeline needs: source control, build, testing, deployment, monitoring-then select tools that integrate well as a whole. 1. **Security and Compliance of DevOps Problems** Speed isn’t dear to security, and speed is the main feature of DevOps. This two won’t go together, thus fireworks. The answer is with devsecops: putting security into the earliest stages of pipelines. Not that simple to introduce. Improper optimization could lead to slowed pipelines due to automated vulnerability scans, access control, and compliance checks. Example Security checks slow releases down after developing patient-facing applications for a healthcare company subject to HIPAA compliance. Skipping checks would expose risk to a million-dollar penalty. 1. **Managing Legacy Systems** You can’t do magic with DevOps on systems that are older than your engineering interns-at least not easily. Many organizations continue to run legacy applications designed not for micro-services or even cloud deployment. Trying to implement CI/CD or containerization in such a setting would be more like trying to learn ballet with an elephant. Solution Approach Containerize what can, refactor little by little, and hybrid approach (some parts automated, some manual). 1. **DevOps Pipelines under Difficulty Scaling** Jenkins pipelines and a few microservices are enough for a small team. Scale that to thousands of builds per day, hundreds of services, and global teams-chaos. What is the Most Challenging Thing That You’ve Done as a DevOps Engineer? For many, one of the most challenging things to manage is scaling pipelines so that their speed and reliability are excellent. Imagine acquisition and deployment for an e-commerce app during Black Friday sales-downtime is not an option. Solution: Invest in pipeline observability, use cloud-native CI/CD, and keep testing parallelized. 1. **Skill Gaps and Lack of Training** DevOps is not just “know Docker and Kubernetes.” It is not just a mindset, and along with this, lots of various skills are being: coding, cloud, automation, testing, monitoring, and collaboration. What organizations usually lack is the right blend of engineers needed. Overwhelmed students learn much, while professionals switching careers into DevOps would often not know where to start. … One of the most challenging problems in DevOps, monitoring is always a disaster. Logs, metrics and traces are all over different systems. Things don’t tend to get easier when the observability fails, for it’s more like detective work after that. Solution: Centralized logging, metrics dashboards, and distributed tracing are must-haves. 1. **Cost Management in DevOps** Cloud seems to be unlimited-until monthly bills come in. DevOps indeed heavily leans on cloud platforms, but their over-provisioning of clusters, idle test environments, duplicated pipelines-all these can cut costs even faster. Example: A startup discovered that their **Kubernetes** clusters with active pods, which did nothing, cost hundreds of dollars per month. Solution: Cost watch automation, resource limits, and pipeline cleanups. 1. **Resistance to Automation** No one seems to trust automation. Manual deployments seem to feel safer for ops teams because they often think their control over the process may be threatened or even their jobs. At the same time, developers might think that automation hides their bugs. … ## Devops Challenges- The Right Track Yes, these **DevOps challenges** also come as proof that development is on the right track. Each problem well identified reflects that teams are reaching new limits, building faster, and adapting to modern needs: from cultural resistance to cost overruns, these will be a passage during DevOps journey. Whether you are a novice in the field, all you need is to have your head up. Even every DevOps engineer at one point made a mistake or had sleepless nights because of Jenkins jobs. If you are experienced, remember: the hardest problems you have solved are the ones to have made you an expert.

DevOps isn't just about shipping code faster, it's about doing it smarter, safer, and saner. But let's be real: even the best teams make mistakes. Some are harmless. Others take down production on a Friday afternoon (yes, *that* Friday deploy). Here are 10 common DevOps mistakes in 2025, how to avoid them, and a few moments that might hit a little too close to home. ## 1. Treating Infrastructure as Code Like a One-Off Script You wrote Terraform once, it worked, and now it lives untouched in a dusty repo folder. That's not IaC, that's tech debt. **Avoid it**: - Version control your IaC. - Apply formatting and linting. - Test it with tools like `terraform plan`or `terratest`. ## 2. Not Enforcing Version Control on CI/CD Configs Your pipeline files are changing, but without versioning, there's no easy way to debug regressions. **Avoid it**: - Store all CI/CD config files (like GitHub Actions, GitLab CI, etc.) in version control. - Treat pipeline logic like any other critical code. ## 3. Poor Secrets Management Hardcoding secrets in code or using `.env` files without encryption is a fast way to land on HN for the wrong reasons. **Avoid it**: - Use Vault, Doppler, AWS Secrets Manager, or SOPS. - Rotate secrets regularly. ## 4. No Rollback Strategy You deploy. Something breaks. And there's no plan B. **Avoid it**: - Use blue-green or canary deployments. - Automate rollbacks on failure. - Always have a `rollback.sh`or previous image ready. ## 5. Ignoring Observability Until It's Too Late Monitoring isn't just about uptime. You can't fix what you can't see. **Avoid it**: - Add metrics, logs, and traces from day one. - Use tools like Prometheus, Grafana, and OpenTelemetry. ## 6. Too Many Tools, Not Enough Integration Your stack has 25 tools. None of them talk to each other. And your alert fatigue is real. **Avoid it**: - Consolidate tools where possible. - Favor tools that integrate well with your existing stack. ## 7. Manual Approval for Every Tiny Change A typo fix shouldn't need a 3-person review and a Slack war. **Avoid it**: - Set up clear policies: auto-approve safe changes, gate critical ones. - Use GitHub environments, OPA, or custom bots to help. ## 8. No Documentation = Single Point of Failure "Ask Alex, they built it." Alex is on vacation. **Avoid it**: - Write docs as you go. - Use tools like Backstage, Docusaurus, or just plain Markdown. - Encourage a culture of async knowledge sharing.

**Our goal**: to surface not just pain points, but structural truths and help reframe the DevOps conversation around clarity, sustainability, and systemic value. **Theme 1: Toolchain Overload and Integration Fatigue** Toolchain fragmentation remains one of the most cited challenges in DevOps today. As we explored in The Real Cost of Tool Sprawl, this tool bloat isn’t just a technical inconvenience, it’s a strategic cost center; the accumulation of specialized tools without orchestration creates hidden complexity that erodes engineering efficiency. “Every company I’ve worked with has a bloated DevOps stack… Every tool fixes one thing but breaks another.” Toolchain fragmentation remains one of the most cited challenges in DevOps today. While Terraform, Jenkins, Helm, and ArgoCD each offer critical functionality, their lack of cohesion often results in fragile, over-engineered systems. Debugging pipelines becomes a specialized skill. Onboarding slows. Velocity suffers. This is not a tooling problem. It’s an orchestration failure. The takeaway for leaders: **prioritize platform cohesion over tooling abundance.** Without a shared orchestration layer, even best-in-class tools will compound complexity. **Theme 2: Burnout Is Not an Anomaly, It’s a Pattern** The symptoms of burnout: low deployment confidence, constant firefighting, and operational fatigue are not isolated. We’ve unpacked this dynamic in From Delays to Deployment Confidence, we explore how reliable delivery pipelines can act as a lever for restoring developer trust and focus. “I don’t know a single DevOps engineer who isn’t constantly tired.” Burnout in DevOps is not a temporary phenomenon, it’s an architectural outcome. Engineers report persistent exhaustion driven by brittle systems, manual remediation loops, and on-call volatility. Beyond individual resilience, the issue points to systemic fragility: - Observability gaps that prolong incidents - Pipelines that fail silently - Deployments that lack confidence-building safeguards … - Continuous feedback loops - Testable, versioned configurations - Recovery mechanisms that match their deployment logic When automation can’t be monitored or understood, it doesn’t reduce effort, it relocates uncertainty. **Theme 4: Governance Without Bureaucracy** “It took three months to get a custom IAM role approved. That’s not security. That’s gridlock.”

## 7 Common DevOps Mistakes to Avoid By DevOps Practitioners in 2025 ### 1) Focusing Solely on Speed at the Expense of Quality The allure of faster delivery often tempts organizations to prioritize speed over quality. While rapid deployment is a key tenet of DevOps, sacrificing quality to meet tight deadlines can lead to technical debt, production failures, and customer dissatisfaction. The rush to deliver can also result in inadequate testing, leading to unstable builds and recurring bugs. **Why This Happens:** - Pressure to meet aggressive timelines. - Lack of automated testing in the pipeline. - Insufficient focus on quality metrics. **Impact:** When quality is neglected, organizations may face costly rollbacks, downtime, and erosion of customer trust. Over time, this approach increases maintenance costs and slows down future development efforts. **What Veritis Recommends:** - Implement **DevOps best practices**like automated testing and continuous integration (CI). - Prioritize robust testing at every stage of the pipeline. - Establish benchmarks that balance speed with reliability to meet business goals. ### 2) Treating DevOps as a Dedicated Team Instead of a Practice One of the most critical DevOps mistakes is viewing it as a standalone team or department. While it may seem logical to assign DevOps responsibilities to a dedicated group, this approach often results in silos, with limited collaboration between development and operations teams. This structure contradicts the core principles of DevOps, which emphasize shared ownership and cross-functional collaboration. **Why This Happens:** - Misunderstanding the concept of DevOps. - Desire to centralize responsibilities for ease of management. **Impact:** Isolated teams undermine the cultural transformation required for DevOps success. This results in miscommunication, slower workflows, and diminished value from DevOps practices. **What Veritis Recommends:** … ### 3) Setting Unrealistic Expectations DevOps implementation requires time, effort, and a cultural shift. However, organizations often set unattainable goals, expecting immediate results or attempting to transform all processes simultaneously. This creates undue pressure on teams, often leading to poor implementation or burnout. **Why This Happens:** - Overestimating the speed of DevOps transformation. - Lack of phased planning or realistic timelines. **Impact:** Unrealistic goals lead to missed deadlines, employee frustration, and incomplete adoption of DevOps practices. This ultimately results in suboptimal outcomes and a failed implementation. **What Veritis Recommends:** - Start with small, measurable milestones that align with long-term objectives. - Use KPIs like deployment frequency and lead time to track progress. - Take an iterative approach to tackle DevOps implementation challenges incrementally. ### 4) Rigidity in DevOps Processes While the principles of DevOps provide a robust framework, being overly rigid in their application can stifle innovation and adaptability. Every organization has unique DevOps challenges and requirements, and a one-size-fits-all approach may not yield the desired results. **Why This Happens:** - Reluctance to deviate from prescribed methodologies. - Fear of disrupting established workflows. **Impact:** Rigid processes limit the flexibility needed to address evolving business needs, preventing organizations from fully leveraging DevOps benefits. **What Veritis Recommends:** - Tailor DevOps processes to suit organizational needs. - Encourage flexibility by regularly refining workflows. - Leverage emerging technologies to enhance scalability and agility. … **Impact:** Failure to monitor systems can lead to unplanned downtime, degraded user experience, and increased operational costs. **What Veritis Recommends:** - Use continuous monitoring tools to track system performance and identify issues proactively. - Establish feedback loops to drive continuous improvement. - Monitor KPIs in real time to ensure stability and efficiency. … **Impact:** This hybrid approach undermines the agility and efficiency that DevOps aims to achieve, resulting in slower workflows and missed opportunities for innovation. **What Veritis Recommends:** - Fully transition to DevOps by modernizing toolsets and processes. - Train teams to bridge the gap between traditional and DevOps methodologies. - Evaluate legacy systems for compatibility with DevOps tools to ensure seamless integration. ### 7) Overlooking Security in DevOps Pipelines Security is often an afterthought in DevOps, especially in fast-paced environments focused on rapid delivery. This oversight exposes organizations to vulnerabilities, data breaches, and compliance violations. With the rise of microservices and containerized environments, security becomes even more critical. **Why This Happens:** - Lack of awareness about DevSecOps. - Focus on speed over secure practices. **Impact:** Ignoring security increases risks, costly breaches, and potential reputational damage. It also complicates regulatory compliance, especially in industries like finance and healthcare. **What Veritis Recommends:** - Integrate security at every stage of the pipeline with DevSecOps practices. - Automate security checks and compliance verifications. - Ensure robust role-based access control (RBAC) and secure coding practices.

In 2025, DevOps continues to grow and change quickly, helping teams deliver software faster and more securely. But as systems become more complex with microservices, cloud platforms, and AI-driven tools, new challenges arise. Teams now need to balance speed with security, manage too many tools, control rising cloud costs, and still maintain high-quality software. … ## Understanding the Complexity of Modern DevOps Environments DevOps bridges development and operations to deliver software quickly, reliably, and at scale. However, evolving infrastructure and organisational needs have intensified the landscape. **Security Risks in Complex, Distributed Environments** **Managing Complex Microservices and Distributed Systems** **Toolchain Complexity and Fragmentation** **AI and Machine Learning Integration** **Multi-Cloud and Hybrid Cloud Management** **Controlling Costs in Cloud and AI Workloads** **Bridging the Skills Gap** … #### Security challenges include: **Expanded attack surface:**Every microservice and API interaction creates a potential entry point for attackers. **CI/CD pipeline vulnerabilities:**Security lapses can occur anywhere during build, test, or deployment stages, risking compromised software releases. **Shadow APIs and services:**Untracked or unmanaged endpoints increase the risk of breaches. **Data leakage across distributed systems:**Sensitive data moves dynamically, making safeguarding difficult. **Complex compliance management:**Ensuring all distributed components comply with regulations like HIPAA, GDPR, or SOX is a constant challenge. … ### 2. Managing Complex Microservices and Distributed Systems Modern applications are rarely monolithic. Using microservices, serverless functions, and container orchestrators like Kubernetes allows rapid development but results in complex dependencies that are tough to troubleshoot. #### DevOps teams struggle with: - Pinpointing root causes in convoluted transaction paths. - Understanding service dependencies and health status across dynamic clusters. - Monitoring performance and latency in near real-time to maintain a good user experience. … ### 3. Toolchain Complexity and Fragmentation DevOps teams rely on many tools covering CI/CD, infrastructure automation, configuration, monitoring, security, collaboration, and testing. The rapidly expanding DevOps toolchain often results in tool sprawl, causing fragmented workflows, inconsistent data flows, and complex integration challenges across environments. #### Common issues include: - Different tools that do not integrate well, causing siloed data and processes. - Difficulties maintaining and scaling tooling platforms. - Too many tool choices slow down decision-making. - Lack of unified monitoring gives incomplete system insights. #### How APM Helps: Leading APM solutions offer integrations with popular DevOps tools and platforms, providing a centralized monitoring and alerting hub. ... … ### 5. Multi-Cloud and Hybrid Cloud Management Organizations pursue multi-cloud and hybrid strategies to optimize costs, avoid vendor lock-in, and enhance resilience. However, managing uniform configurations, security policies, and monitoring performance across heterogeneous environments is complex. #### Common obstacles are: - Ensuring consistent security policies across cloud and on-prem systems. - Gaining comprehensive visibility spanning all environments. - Preventing performance degradation due to misconfigurations or resource contention. … ### 6. Controlling Costs in Cloud and AI Workloads The dynamic, consumption-based pricing of cloud resources, especially for AI and data-heavy workloads, makes cost management challenging. Without proper oversight, organizations risk significant budget overruns. #### Cost pitfalls include: - Idle Kubernetes pods or forgotten test environments incurring charges. - Overprovisioned cloud infrastructure. - Costly data transfers across clouds or regions without optimization. … ### 7. Bridging the Skills Gap The fast-evolving DevOps ecosystem demands diverse expertise in automation, cloud, security, and AI. However, many organizations face shortages of skilled professionals capable of managing and innovating complex DevOps workflows. #### Key challenges are: - Recruiting and retaining talent with the right skills. - Continuous upskilling to keep pace with new tools and methodologies. - Managing burnout due to high expectations and rapid change. … ## FAQs ### 1. What is the core challenge DevOps faces with modern, complex applications? Modern applications, built on microservices, containers, and serverless functions, introduce a high degree of complexity that makes monitoring difficult. DevOps teams struggle to achieve full visibility across this interconnected and distributed environment. The absence of a single, comprehensive view leads to "monitoring blind spots" and an inability to understand how various components interact. … ### 2. What is the biggest issue with inconsistent environments in DevOps? One of the most persistent issues is "it worked on my machine," where an application functions correctly in a development or testing environment but fails in production. This happens when environments are not consistent, and teams waste significant time chasing bugs caused by configuration discrepancies.