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Tag: devops

PlayerZero Adds Ability to Simulate Code to AI Platform

PlayerZero has extended its namesake platform that leverages artificial intelligence (AI) to analyze codebases by adding the ability to simulate code created using generative AI coding tools.

Fresh off raising an additional $15 million in funding, PlayerZero CEO Animesh Koratana said CodeSim makes it possible to predict how that code will impact a codebase without having to rely on legacy testing tools that were created for a previous era of application development.

PlayerZero has developed a platform that makes use of AI agents to analyze a codebase, its history, the tickets created, runtime telemetry, documentation and user analytics. The code simulation capability extends that capability using Sim-1, an AI model purpose-built to refine code understanding into scenarios and concepts that can be used to simulate code behavior.

DevOps teams can then launch natural language queries to better understand how code works, why it may be broken and how it should be fixed, noted Koratana.

That will be crucial in the age of AI as the volume of code being created overwhelms legacy testing tools that will not be able to keep pace with citizen and professional application developers that are, for example, embracing vibecoding to rapidly prototype and build applications, he added. PlayerZero is specifically designed to onboard new developers in a matter of hours, said Koratana.

Most software engineering teams in the enterprise are already spending up to 70% of their time on activities such as investigating and fixing software bugs instead of coding. A different approach to testing software in the AI era that does not require DevOps teams to set up and maintain a separate unit testing platform will be an absolute requirement, noted Koratana.

It’s not clear at what pace software engineering teams are embracing AI to accelerate software development, but legacy platforms will not be able to add the same level of insight simply because they are not designed to analyze all the codebase data that needs to be pulled from multiple sources, said Koratana.

The one thing that is all but certain is the volume of code being generated will soon put existing DevOps workflows to the ultimate test. Historically, whenever there is a major innovation most organizations use it to do things faster using the same processes. However, eventually there comes a time when the processes themselves are reinvented to realize the ultimate benefit of the innovation. Arguably, in the case of AI it won’t be too long now before software engineering teams start to revamp the entire process of building and deploying software on an end-to-end basis.

In the meantime, DevOps teams might want to start identifying which elements of the application development process can be reengineered today with an eye toward how other processes might one day be similarly transformed for the AI era. After all, DevOps, if anything, has always been about making a commitment to continuous improvement, which ultimately means there should never be any sacred cows when it comes to reliance on a particular tool or platform.

August 6, 2025
AI-Driven Observability: Fast, Context-Rich MCP Servers

Christine Yen—developer-turned-CEO—explains why Honeycomb built an MCP server and why every observability vendor may follow. MCP, short for Model Context Protocol, acts like a concierge for AI agents: It makes a product’s API, telemetry schema and helper tools easily discoverable so large-language models can ask precise questions instead of guessing. Yen sees it as table stakes for letting bots troubleshoot production just as humans do.

Speed is the first requirement. Engineers won’t wait minutes for an LLM to fetch a log slice or distribution graph. Honeycomb’s mantra—fast is a feature—now pays off when an agent fires off dozens of sub-queries during root-cause analysis. Yen jokes that no one wants to brew coffee while a model thinks, so the platform’s near-instant queries translate directly into snappier conversational answers.

Depth of data is the second pillar. Because customers pipe richly labeled events (“checkout_latency_ms”, “user_tier”) instead of fixed dashboards, even off-the-shelf models can map plain-English prompts to meaningful fields. Yen argues this beats the “narrow metric lists” many teams still rely on; a model that understands business context can pinpoint anomalies without endless prompt engineering.

Yen admits she no longer remembers every field in Honeycomb’s own dog-food cluster—but the MCP server does. She expects newcomers to treat agents as default copilots, while power users toggle between raw queries and natural language. The challenge for vendors, she says, is balancing both paths without forcing engineers into one camp.

Yen’s closing advice: start experimenting now, map where your context lives and demand that every tool expose it through open protocols. The future observability stack won’t just collect data; it will serve it—fast and in the right context—to whatever human or agent asks first.

August 4, 2025
Context on Tap: How MCP Servers Bridge AI Agents and DevOps Pipelines

Large language models can draft code or move artifacts, but without situational awareness they still trip over the basics. Cloudsmith CEO Glenn Weinstein tells Mike Vizard why a new piece of plumbing—the Model Context Protocol (MCP) server—is quickly becoming table stakes. Think of MCP as a receptionist for AI agents: it answers questions like “Which Docker images are in my repo?” and supplies environment-specific details the model would otherwise guess—or miss entirely.

Context alone isn’t enough. Developers increasingly chain agents together to run multi-step jobs—pull a package, scan it, publish it—without a human in the loop. That hand-off requires agent-to-agent (A2A) protocols so one bot can call another securely and without endless re-authentication. Google’s recent move to donate A2A to the Linux Foundation hints at how fast the ecosystem is converging on open standards.

More context and more agents lead to more builds—sometimes hundreds per day. Weinstein warns that existing CI/CD pipelines will bottleneck if artifact storage can’t keep up. Teams already accustomed to nightly releases will feel pain when AI turns “once a day” into “once an hour” unless repositories serve packages globally and caches stay warm.

There’s also a supply-chain angle. Hallucinating agents may suggest outdated or even nonexistent packages. An artifact manager that doubles as a control plane—tracking provenance, scanning for vulnerabilities and rejecting spoofed names—becomes a final checkpoint before code reaches production.

Weinstein’s takeaway is blunt: experiment with AI copilots today, but raise expectations for every tool in your stack. If a platform can’t expose its data through an MCP endpoint and play nicely with agents, it will feel ancient in a year. Start mapping where context lives, audit your APIs and assume that the next generation of developers will treat AI companions as a given. Your pipelines should be ready before they arrive.

August 4, 2025
The Evolution of DevOps Continues: How 2,000 Token-Per-Second AI Code Generation Changes Everything

The DevOps landscape has always been about speed, efficiency and streamlining development workflows. Today, we’re witnessing a paradigm shift that promises to eliminate one of the most persistent bottlenecks in modern software development: Waiting for AI-generated code.

The Flow State Problem

Every DevOps engineer and developer knows the scenario intimately. You’re deep in the zone, architecting a complex CI/CD pipeline or debugging a containerized application. Your mind is racing through solutions, and you reach for AI assistance to generate the code that matches your mental model. Then comes the wait.

Tokens trickle in at 50 per second. Your train of thought derails. By the time the AI finishes its response, you’ve mentally moved on to checking Slack or grabbing coffee. The context switch penalty is real, and it’s costing teams their most precious resource: Focused development time.

This isn’t just an inconvenience — it’s a fundamental impediment to the flow state that drives breakthrough engineering work. In DevOps, where rapid iteration and continuous deployment are core principles, these micro-delays compound into significant productivity losses.

Hardware Revolution Meets Software Evolution

The solution isn’t incremental optimization of existing systems. It’s a complete reimagining of the hardware foundation that powers AI inference. Cerebras has taken this approach with their Wafer-Scale Engine (WSE-3), creating what amounts to an entirely different category of computing architecture.

Instead of connecting multiple smaller chips, Cerebras manufactures an entire silicon wafer as a single, massive processor. The WSE-3 contains 900,000 AI cores and 44GB of on-chip SRAM. This design eliminates the memory bottlenecks that plague traditional GPU clusters, where model weights must be shuttled back and forth between memory and processors.

The practical result is transformative: 2,000 tokens per second of AI code generation. That’s 40 times faster than typical cloud AI providers. When integrated with development tools like Cline, this speed translates directly into preserved flow states and accelerated development cycles.

Open Source Models Close the Quality Gap

Speed without quality is meaningless, which makes the emergence of competitive open-source models exciting for DevOps teams. Qwen3 Coder represents a watershed moment; an open-weight model that matches or exceeds closed-source alternatives like Claude Sonnet and GPT-4 on coding benchmarks while running at breakthrough speeds on specialized hardware.

This convergence of open-source quality with proprietary inference acceleration creates unprecedented opportunities for DevOps organizations. Teams can now access frontier-quality AI assistance without vendor lock-in, while achieving performance that exceeds traditional closed-model approaches.

The implications extend beyond individual productivity. Open-source models allow organizations to customize and fine-tune AI assistants for their specific DevOps workflows, compliance requirements, and architectural patterns. Combined with instant inference speeds, this creates a foundation for truly personalized development automation.

Transforming DevOps Workflows

The impact of instant AI code generation ripples through every aspect of DevOps practice. Infrastructure-as-code development becomes conversational rather than iterative. Instead of writing Terraform configurations line by line, engineers can describe desired infrastructure states and receive complete, working configurations instantly.

CI/CD pipeline creation transforms from a templating exercise into a natural language interaction. DevOps engineers can describe deployment requirements, testing strategies, and rollback procedures, then receive fully-formed GitHub Actions workflows or Jenkins pipelines without breaking cognitive flow.

Container orchestration becomes more accessible as complex Kubernetes manifests are generated at the speed of thought. Rather than wrestling with YAML syntax and resource specifications, teams can focus on architectural decisions while AI handles implementation details.

The debugging process accelerates dramatically when log analysis and troubleshooting guidance arrive without delay. Production incidents demand rapid response, and instant AI assistance can mean the difference between brief service disruption and extended downtime.

“The leap to 2,000 tokens-per-second AI code generation, fueled by specialized hardware and the maturation of open-source models like Qwen3 Coder, is another step towards AI transforming DevOps and software engineering,” said Mitch Ashley, VP and practice lead, software lifecycle engineering at The Futurum Group. “The availability of tokens alleviates waiting for AI assistance, maintaining developers’ flow state. This helps AI move further into the mainstream of DevOps pipelines, infrastructure-as-code and software development.”

Practical Implementation for DevOps Teams

Getting started with this technology requires minimal overhead, a crucial consideration for DevOps teams already managing complex toolchains. The integration process takes under a minute: Obtain a Cerebras API key, configure the provider in your development environment, and select the appropriate model tier based on team size and usage patterns.

For individual contributors and small teams, free tiers provide substantial functionality with 64K context windows. Larger DevOps organizations can leverage professional tiers offering extended context and higher message limits, ensuring AI assistance scales with team requirements.

The provider-agnostic architecture ensures teams aren’t locked into specific vendors or models. As the AI landscape continues evolving rapidly, this flexibility protects technology investments while ensuring access to breakthrough capabilities as they emerge.

The Future of DevOps Automation

This shift toward instant AI code generation represents more than a productivity improvement; it’s a fundamental change in how we approach software operations. When AI assistance operates at the speed of human thought, it becomes a natural extension of engineering intuition rather than a separate tool requiring context switches.

The technology democratizes advanced DevOps practices by reducing the expertise barrier for complex operations. Junior engineers can leverage AI guidance to implement sophisticated monitoring, deployment, and scaling strategies that previously required senior-level knowledge.

As AI capabilities continue advancing and inference speeds increase further, we’re moving toward a future where the primary constraint on DevOps velocity isn’t technical implementation but architectural decision-making and business strategy alignment.

The revolution is here, and it’s measured in tokens per second.

August 4, 2025
Five Great DevOps Job Opportunities

devopsy.kinsta.cloud is now providing a weekly DevOps jobs report through which opportunities for DevOps professionals will be highlighted as part of an effort to better serve our audience.

Our goal in these challenging economic times is to make it just that much easier for DevOps professionals to advance their careers.

Of course, the pool of available DevOps talent is still relatively constrained, so when one DevOps professional takes on a new role, it tends to create opportunities for others.

The five job postings shared this week are selected based on the company looking to hire, the vertical industry segment and naturally, the pay scale being offered.

We’re also committed to providing additional insights into the state of the DevOps job market. In the meantime, for your consideration.

SimplyHired.com

Hims and Hers Health
Remote
Senior DevOps Engineer
$175,000 to $195,000

LinkedIn

Atlassian
Remote
Senior Enterprise Technical Architect – DevOps
$141,300 to $204,400

Indeed.com

Credit Acceptance
Remote
Staff DevOps Engineer
$140,625 to $206,250

The Job Network

T-Rex Solutions
Fort Meade, MD
DevOps Engineer
$140,000 to $170,000

Dice.com

U.S. Bank
Englewood, CO
Applications Manager
$111,095 to $130,700

August 4, 2025
BMC Extends Scope and Reach of DevOps Mainframe Workflows

BMC has extended its DevOps analytics tool for mainframe environments, dubbed BMC AMI zAdviser Enterprise, to now be able to collect Git usage data to make it simpler to identify bottlenecks and other workflow issues in addition to making available in beta a BMC AMI Assistant tool that enables IT teams to via a natural language interface chat with a generative artificial intelligence that BMC has trained to help optimize operations.

At the same time, BMC has also added a set of connectors for the identity management platform from Okta to better secure software supply chains.

Finally, BMC has also extended BMC AMI Cloud, a data management platform, to add support for enforcing password and IBM zMFA token entry to protect on-premises object storage and public cloud object storage data from unauthorized access, along with a multi-volume capability that makes it possible to store up to 255TB in data sets that are stored using object storage services in the cloud.

John McKenny, executive vice president and general manager for the mainframe business unit at BMC, said these additions to the platform will make it simpler to extend DevOps workflows across distributed computing environments that include mainframe platforms.

It’s not clear to what degree organizations are integrating mainframe environments with other distributed computing environments, but there is a clear need to extend existing DevOps expertise to a platform that still runs mission-critical applications. While historically many of those applications were built and deployed using waterfall methodologies, the pace at which distributed computing applications are updated is requiring the team that manages mainframes to adopt DevOps methodologies to enable them to build, deploy and update applications faster.

Many of the newer applications being developed are also written in Java and are destined to run on instances of Linux running on mainframes and x86 servers, noted McKenny.

Ultimately, the line between distributed computing environments and mainframes will only continue to blur in the age of AI. It will soon become, for example, feasible to leverage a common set of AI agents to manage multiple platforms. The overall goal is not to replace IT teams but rather make up for a chronic shortage of talent and expertise that worsens as more mainframe experts retire by augmenting existing teams, added McKenny.

It may be a little while before this next era of computing is realized, but in the meantime, IT teams should be creating an inventory of manual tasks that either already can be automated using AI agents or soon will be. In fact, there may soon come a day when managing IT is significantly less tedious than it is today, which should hopefully reduce burnout. After all, most of the tasks being automated using AI are not the ones that most IT professionals, especially, enjoy. The challenge and the opportunity now is to add value in ways that only human ingenuity can achieve, in collaboration with AI agents that are waiting for someone to not just tell them what to do next, but also review the accuracy of their work.

July 31, 2025
The Benefits of Using Kotlin for Cross‑Platform Development

Kotlin continues to solidify its place in today’s software engineering landscape. According to JetBrains’ State of Developer Ecosystem 2023, 66% of Kotlin developers use the language for Android and/or server-side applications, with the adoption of Kotlin multiplatform (KMP) steadily increasing. Meanwhile, Snapp Mobile’s 2024 Kotlin Multiplatform Developer Survey found that 46.7% of surveyed teams have considered KMP and plan to explore it further, signaling that a wave of cross-platform consolidation is already underway.

Kotlin stands as a modern, statically typed language that targets the Java Virtual Machine (JVM), JavaScript and native binaries. It pairs null safety with concise syntax and powerful abstractions while offering first-class tooling. As demand for mobile, web and desktop parity grows, organizations that tap Kotlin development services can streamline code sharing across platforms, delivering products faster, with fewer bugs and lower long-term maintenance costs.

The Multiplatform

Kotlin multiplatform lets teams reuse close to 100% of their code across Android, iOS, desktop and web. Rather than masking every user interface (UI) detail the way some hybrid frameworks do, KMP shares the business logic layer while allowing each platform free to retain its native UI. That design preserves native-level performance while streamlining backend consistency and simplifying developer onboarding.

Kotlin now ranks 13th on the PYPL index with a 1.75% market share in 2024 — proof that its reach stretches far beyond Android. Seamless Java interoperability — highlighted by WIRED — allows gradual modernization of legacy systems without ditching proven codebases or tooling.

Must‑Have Features

Unified Codebase

Kotlin multiplatform enables shared codebases between Android and iOS. It defines data models, networking and business rules once, then compiles for Android and iOS. This helps teams skip duplicate work, cut merge conflicts and shorten test cycles.

Type‑Safe APIs

Official documents show KMP enforcing type-safe interfaces across common and platform layers. Developers pass JSON or service data with confidence and avoid brittle, runtime-only bugs.

Native Performance

Low level virtual machine (LLVM)-based compilation for iOS and desktop yields binaries that match native speed and memory use. No virtual machine (VM) warm-up or heavy garbage collection (GC) means smoother animations and more responsive UIs — vital for media-rich or mission-critical apps.

Reduced Time‑to‑Market

According to Netguru, teams using KMP cut development time by 30–50% and reduce codebase size by around 40% compared to Java. With fewer files, cleaner abstractions and shared test coverage, teams can bring new features to market faster and focus more on innovation than duplication.

Java Interoperability

Kotlin’s ability to interoperate seamlessly with Java — highlighted in WIRED — allows companies to modernize gradually, rather than rewrite everything at once. Kotlin modules can be introduced into existing JVM stacks incrementally, preserving investments in frameworks such as Spring, Hibernate and existing CI/CD pipelines.

Growing Ecosystem

JetBrains’ Kotlin multiplatform case studies showcase successful implementations at Netflix, Baidu and Worldline, where KMP is used to share core business logic across mobile and desktop products. A growing set of community libraries for authentication, state management and real-time messaging reduces the number of edge cases that need bespoke implementations.

KMP does not just promise code reuse; it delivers reliable performance with lower tech debt, making it a practical path for cross-platform products at scale.

Best Practices for KMP Projects

Modularize Your Shared Library

Commence with a well-structured project, placing pure business logic and reusable utilities in the common module. Let the platform modules handle UI and native application programming interface (API) integration.

Leverage Expect/Actual Declarations

Use Kotlin’s expect/actual mechanism so common code stays platform-neutral while each platform module supplies its own specific implementation.

Adopt CI Early

Set up continuous integration (CI) pipelines (e.g., GitHub Actions, Bitrise or GitLab CI) to build and test for all target platforms. Run JVM-based unit tests on pull requests and include emulator/simulator tests for UI layers.

Embrace Multiplatform Libraries

Choose KMP-compatible libraries such as Ktor for HTTP, SQLDelight for databases, kotlinx.serialization for JSON and Napier for logging. They cut context switching and keep error handling consistent across environments.

Monitor Performance Across Targets

Run Android Profiler, Instruments for iOS and other platform-native profilers on desktop to spot bottlenecks in shared logic. Benchmark memory use, startup time and thread counts on a regular cadence.

Plan Incremental Rollouts

Start by migrating low-risk modules — e.g., date formatting, validation or API clients — before advancing to core business rules. This phased approach reduces risk and eases the learning curve for new teams.

Future Outlook

Cross-platform development is clearly gaining momentum. With 46.7% of engineering teams planning to adopt KMP, and Netguru estimating 30–50% time savings, industry analysts project that KMP-based projects will outnumber single-platform Kotlin apps by 2026. Expect JetBrains to further enhance tooling: Unified debugging across modules, faster Gradle sync and richer type inference in shared contexts.

Simultaneously, the open-source ecosystem is filling gaps in state management (like Decompose), navigation and cross-platform UI experiments. For enterprises with Android-heavy backends and iOS front ends, KMP offers the rare blend of engineering flexibility, long-term maintainability and native performance.

Conclusion

Kotlin has moved far past its Android origins and now serves as a robust foundation for modern cross-platform work. With type-safe APIs, native-level performance and seamless Java integration, teams can ship consistent, high-performance applications across Android, iOS, web and desktop without duplicating logic or splintering their engineering crews.

Companies that adopt KMP can future-proof their codebases, trim costs and accelerate release cycles. Backed by a widening ecosystem, rising enterprise adoption and active tooling support from JetBrains, Kotlin has graduated from being ‘just an alternative to Java’ into a strategic cornerstone for efficient, scalable software delivery in the multi-device era.

July 31, 2025
DevOps Meets Blazor in 2025: Streamlining .NET Web App Delivery for the Future
Let’s rewind for a moment. Back in the early days of web development, we were often stuck juggling multiple languages and frameworks — C# on the backend and JavaScript on the frontend. Enter Blazor, Microsoft’s full-stack web framework that enables the development of rich web user interfaces (UIs) using just C#. As of 2025, Blazor has become a mature, go-to technology for modern enterprise-grade .NET web apps, especially for teams already invested in Microsoft’s ecosystem.

Blazor’s two hosting models — Blazor Server and Blazor WebAssembly (WASM) — give developers flexibility based on app needs. Server-side Blazor provides fast startup times and thin clients, while WASM unlocks true cross-platform execution directly in the browser.

From a team point of view, our transition to Blazor reduced frontend JavaScript code by over 80%. We were able to maintain code consistency, improve collaboration between frontend and back-end developers and accelerate delivery cycles for internal apps.

Key DevOps Trends Shaping Blazor Application Delivery in 2025

Blazor’s rise aligns with transformative shifts in the DevOps landscape. In 2025, several key trends are redefining how we deliver Blazor apps at scale.

1. AI and ML Integration in DevOps

From automated anomaly detection to intelligent test generation, artificial intelligence (AI) plays a massive role in DevOps. For Blazor apps, AI-powered predictive testing is catching regressions before they hit production. Tools such as GitHub Copilot, Azure DevOps Test Plans and GitHub Advanced Security are enabling smart pipelines.

Through the use of GitHub Copilot, our team discovered that AI-generated unit tests reduced manual test-writing time by 40%, especially for complex Razor components.

2. DevSecOps Embedded in CI/CD

Security is no longer a checkpoint at the end — it’s baked in from the start. With DevSecOps, we incorporate OWASP guidelines, automated dependency checks, and container security scans into every Blazor deployment.

Example: After putting it to the test, we used Snyk to scan our Blazor project dependencies and identified vulnerabilities in older NuGet packages — issues that traditional manual review would’ve missed.

3. Cloud-Native DevOps for Blazor

Blazor works seamlessly with Azure, and when combined with Docker and Kubernetes, it becomes cloud-native ready. Hosting Blazor WASM apps in containers on Azure Kubernetes Service (AKS) makes distributed apps scalable and resilient.

Our findings show that Dockerizing Blazor apps cuts down environment mismatch issues, and deploying to AKS ensures zero-downtime rollouts.

Enhancing Blazor Development With Modern DevOps Practices

To really make the most of Blazor, you need modern, automated DevOps workflows. Here’s what we use and recommend:

Automated CI/CD With Azure DevOps and GitHub Actions

Azure DevOps and GitHub Actions remain the power duo for .NET-based continuous integration/continuous deployment (CI/CD). From automatic Razor compilation to builds publishing and Azure App Services, we’ve seen massive time savings.

Running concurrent GitHub Actions for building both server and WASM variants allowed for faster integration testing and faster deployment feedback.

Continuous Monitoring and Feedback

You can’t improve what you don’t measure. Application Insights, Datadog and New Relic are must-haves for Blazor apps. They provide live performance insights, user interaction heatmaps and even alerts for slow-loading pages.

Our tests revealed that using Application Insights helped reduce the initial page load time for our Blazor WASM app by 15% through the optimization of third-party scripts.

Dependency Management for .NET Libraries

.NET developers are lucky to have NuGet; however, it can also be a source of vulnerabilities if left unchecked. Tools such as Dependabot, WhiteSource Bolt and NuKeeper help keep libraries patched and secure.

Technology Comparison: Blazor vs. Other SPA Frameworks in 2025

Let’s look at how Blazor stands against other big players such as Angular and React in 2025:

Framework Comparison Table

Hosting Model Feature Comparison

Blazor Security and Quality Assurance in DevOps Pipelines

Blazor doesn’t get a pass on security. It may run on .NET, but the frontend is just as vulnerable if not managed properly.

Best Practices We’ve Used:
- HTTPS-only configurations via reverse proxies and app settings
- CORS policies defined in Startup.cs for granular control
- Static code analysis using SonarQube for both Razor and C# files
- Security headers such as Content-Security-Policy and
X-Content-Type-Options
- Authentication and authorization with Azure AD and IdentityServer
Based on our firsthand experience, integrating OWASP ZAP scans into CI pipelines helps uncover hidden XSS risks in component render cycles.

Future Outlook: Blazor and DevOps Synergy Beyond 2025

Looking forward, the synergy between Blazor and DevOps is only going to grow deeper.

1. AI-Driven DevOps Will Optimize Blazor Lifecycles

We expect smarter tooling — AI agents that fix broken builds, auto-update packages and even write regression tests for Razor components.

As per our expertise, this is already in motion with Azure’s Copilot and GitHub’s AI CI assistants.

2. Serverless and Microservices Go Mainstream

Serverless components powered by Azure Functions will enable Blazor apps to offload non-UI tasks such as image processing and payments. Additionally, micro frontends will let large teams develop different Blazor modules independently.

3. DevOps Ecosystem Tooling Will Mature

Expect new Blazor-centric DevOps tools — preview environments, instant rollback support, hybrid cloud deployment plugins and more.

Through trial and error, we discovered that the Blazor ecosystem grows faster when teams embrace full DevOps automation and lean into experimentation.

Conclusion

Blazor 2025 is not just a framework — it’s a key player in the future of enterprise web development. Combined with modern DevOps practices, it empowers teams to ship fast, scale effortlessly and secure their apps by design.

Whether you’re a .NET veteran or just exploring frontend modernization, investing in Blazor

+ DevOps today positions your team for long-term success in tomorrow’s digital landscape.

FAQs

1. Is Blazor ready for enterprise-scale applications in 2025?

Absolutely. With .NET 8, mature tooling and DevOps integrations, Blazor is now fully capable of powering enterprise portals, internal dashboards and even public SaaS platforms.

2. How does Blazor WebAssembly compare to React or Angular in performance?

Blazor WASM has a larger initial payload but closes the performance gap with AOT and CDN optimizations. React still leads in FCP, but Blazor shines in developer efficiency for .NET teams.

3. What tools do you recommend for CI/CD in Blazor projects?

Azure DevOps, GitHub Actions and Docker are excellent. For security, tools such as Snyk, WhiteSource and OWASP ZAP help enforce DevSecOps.

4. Can Blazor be used in microservices architecture?

Yes. Blazor frontends can easily consume microservice APIs via HTTP, SignalR or gRPC. Pairing with Azure Functions or containerized services makes scaling easier.

5. What’s the best way to host Blazor apps in 2025?

For internal apps — Azure App Services or AKS with Blazor Server. For public-facing apps — CDN-optimized Blazor WASM deployments in Docker containers.

6. Is JavaScript still needed in Blazor apps?

Rarely. You can write interop layers, but pure C# suffices for most UIs. Teams now focus more on shared logic and less on writing scripts.
July 31, 2025
Beyond the Firewall: Achieving True Observability in the Era of Hybrid Infrastructure

It’s 2025, and while DevOps teams focus 99% of their time on their code and databases, their applications increasingly rely on complex infrastructures that span private data centers, public cloud services, APIs, SaaS tools and networks they don’t own or control. User experience of modern apps is as dependent on the reliability of the internet as it is on the performance of your code.

Yet far too many organizations are relying on legacy observability tools built for a time when everything lived behind the firewall (usually in the cloud). These tools provide excellent insight into internal systems (code traces, event and logs, and infrastructure metrics) but fall short when problems occur in the “middle mile,” last-mile ISPs, or within third-party services your customers depend on.

This is a critical blind spot, and it’s putting reliability, performance and even brand trust at risk.

The Observability Gap

The average company experiences 72 internet disruptions per month, according to a 2025 Forrester Opportunity Snapshot. Moreover, of the companies surveyed, 42% note that those disruptions resulted in losses of over $500,000 in the month preceding the survey, adding up to over $6M annually.

Observability platforms are generally focused on application code and infrastructure (MELT – metrics, events, logs and code traces), which is critical for developer teams. If something goes wrong in your code, these platforms are needed to identify and resolve issues as well as to optimize performance and resource utilization. However, in the hybrid, distributed, service-oriented digital environment we operate in today, needs are fundamentally different.

Applications are now more often a collection of dozens of interdependent components and services that require reliable, performant connectivity. As an example, an average website requires hundreds of calls to dozens of providers to load a page: DNS, SSL, content servers, images stored on object storage, fonts, advertising components, tag management, tracking, video services, CDNs, etc.

Today’s incidents, whether an outage, a slow-loading application, or an app that is not accessible to some users, are most likely not due to a bug in application code or the cloud infrastructure it runs on but due to slow DNS resolution, an unresponsive API, latency or dropped packets between cloud services, or a peering issue between two ISPs halfway across the world – the list goes on. This is where traditional APM (application performance monitoring) tools end and where internet performance monitoring (IPM) begins.

True observability requires visibility into everything that can impact user experience. You can’t afford to ignore the components that your observability platform cannot monitor, or those that are not effective at monitoring.

What IPM Brings to the Table

IPM gives DevOps teams continuous, proactive visibility into how applications and services perform from the perspective of the end user. It’s actually doing more than tracing internal requests; it’s also measuring performance across the entire delivery chain, from the cloud to the CDN to the last-mile network, from the perspective of the user. This takes advantage of a network of thousands of agents deployed around the world. This approach gives teams a more holistic, birds-eye view of what’s going on, and like a GPS tracker, it can more quickly help teams map to the heart of issues as they happen in real time.

By integrating IPM into observability stacks, teams can accomplish a lot more, including: 1) Detecting issues before customers experience them and 2) Pinpointing whether the root cause lies within your infrastructure or an external dependency. Additionally, IPM can help reduce time-to-resolution by arming on-call engineers with precise, external telemetry and help build resilience by proactively monitoring internet routing changes, DNS resolution times, and synthetic transactions across geographies.

When something’s slow, knowing why, where and whether it’s in your control to fix it has never been more readily available or convenient.

From Defensive Firefighting to Offensive Prevention

By utilizing IPM, teams can make a shift from reactive to proactive observability. Rather than waiting for a fire, teams can proactively put them out before they even get started by detecting degraded performance trends or anomalies in any location, in real time. This approach often uncovers issues that would otherwise go undetected for hours (or only surface through a spike in help desk tickets or angry social media posts), which can save companies’ brand trust and millions of dollars in the long run.

Take the example of a leading eCommerce platform that integrated IPM into its workflow. One Monday morning, their site traffic appeared to drop significantly in parts of Asia. Their APM tools showed no errors, and cloud infrastructure metrics looked healthy. But IPM alerted the team of a regional DNS issue with a third-party provider, which prevented users from resolving their site domain.

Because of the quick insight, the DevOps team successfully rerouted traffic within minutes, minimizing lost revenue and preserving customer trust. Without IPM, that issue might have gone undiagnosed for hours, or worse, attributed to internal systems, wasting precious triage time.

Building an IPM-Ready Culture

For many DevOps teams, adding IPM means completing the big picture by adding a system that relies on synthetic monitoring, BGP, RUM and many internet-centric tests to ensure complete coverage of the internet stack and the analysis tools that provide the best view into a system: Dependency maps, experience scores, data exploration smartboards, etc.

But more than tools, it’s about mindset. Embracing IPM requires shifting the focus from system KPIs (like error rates, or CPU utilization) to user-centric metrics like experience level objectives with business goals in mind. It also involves treating the internet as mission-critical infrastructure, not a black box, and ensuring that performance metrics are shared across teams, not siloed in IT or networking. Observability teams become digital operations teams, ensuring every digital interaction that makes the business work.

What Does This Mean in the Long Run

At a time when hybrid infrastructure is the norm and internet dependencies are unavoidable, achieving true observability means embracing a broader view. The best DevOps teams of 2025 are those who understand that visibility must start with the user and include everything impacting their experience: Internal and external across the internet stack.

With internet performance monitoring, teams can eliminate critical blind spots, be better prepared to manage incidents and move confidently from reaction to prevention. Because when the next performance issue hits, it may not be coming from your code, but your customers will still expect you to solve it.

July 31, 2025
“Shove Left” – Dumping Downstream Tasks Onto Developers – A Recipe for Failure
As buzzwords go, “Shift Left” is a catchy, clever term that’s ubiquitous in DevSecOps circles. It’s memorable, fun to say and ideal for slides and animations. It promises strategic realignment, with the payoff of reducing risk, boosting quality and accelerating delivery.

But somewhere between trending headlines, boardroom ambitions and developer realities, Shift Left loses the plot. It becomes “Shove Left,” a poorly disguised attempt to dump responsibilities onto developers without the tools, training, collaboration or process changes to succeed. The result is burnout, bottlenecks and systems that are neither faster nor more secure.

This “make the developers do it” approach delivers worse outcomes. Shift Left success depends on collaborative process improvement and smart automation, informed by the summary knowledge of the constellation of experts.

The “Shift Left” Promise:

The many benefits that Shift Left initiatives can offer include:
- Early Feedback Loops: Find and fix bugs, vulnerabilities, or design flaws during development when they are cheapest and easiest to address.
- Build Quality In: Make quality, security and operability fundamental parts of the development process, not afterthoughts.
- Reduce Bottlenecks: Minimize delays caused by long, late-stage QA cycles or security reviews.
- Empower Developers: Give developers faster insights into the impact of their code.
The key principles underpinning effective Shift Left are automation, fast feedback, shared responsibility and crucially, developer enablement.

The “Shove Left” Problem:

Here’s where things go wrong: Instead of redesigning processes and providing tools, some organizations simply decree:
- “Developers must now write all integration tests!”–without clear strategies, test environments or access to expertise
- “Developers need to analyze and fix all findings from the new security tool!– without training or context.
- “Developers are responsible for deploying their services to Kubernetes!” –without Helm charts, CI/CD pipelines with guardrails, observability tools, or access to expertise.
This “Shove Left” anti-pattern stems from pressure to cut costs, a superficial understanding of DevOps or persistent organizational silos. The unfortunate result offloads tasks onto developers’ already full plates.

This misapplication of Shift Left doesn’t just fail to deliver benefits; it can actively lead to:

1. Developer Dissatisfaction and Burnout:
- Expanded Cognitive Load: It’s simply unsustainable to demand that developers bring deep experts in application logic, testing frameworks, security vulnerabilities and infrastructure configuration.
- Disruptive Context Switching: Asking developers to toggle between writing features and debugging complex pipeline configurations or interpreting security findings disperses focus and suppresses productivity.
- Inefficiency: Without sufficient tools or training, tasks take longer and outcomes are more likely to be suboptimal, leading to both inefficiency and frustration.
- Blame Targeting: When developers are given responsibility in areas where they lack expertise and support, they feel… unsupported.
2. Process Instability and Inefficiency:
- Bottlenecks Persist: They simply move from being QA bottlenecks to developer bottleneck as the development team struggles with unfamiliar tasks.
- Superficial Implementation: Checks might be implemented poorly or superficially just to satisfy a mandate, creating a false sense of security or quality.
- Slower Velocity: Core development work slows down significantly.
- Increased Risk: Ironically, quality and security can decrease when tasks are performed without the necessary rigor or expertise.
3. Erosion of Collaboration: This approach can create resentment. Developers feel dumped on, while specialists (QA, Security, Ops) may feel their expertise is undervalued or bypassed, damaging team cohesion.

True Shift Left as a Collaborative Transformation

Shifting Left is about collaboration and enablement powered by automation.
- Collaboration is Key:
- QA and developers partner to define test strategies, provide reusable test frameworks and data generators, and automate effective tests within the CI/CD pipeline. They focus on higher-level testing and exploratory testing.
- Security embeds automated security tools (SAST, DAST, secrets scanning, dependency checking) directly into the developer workflow (IDE, Git hooks, CI), provides secure base images and libraries, offers clear guidance and helps interpret complex findings.
- Operations provides developers with self-service infrastructure-as-code templates, robust and reliable CI/CD pipelines with built-in guardrails and easy-to-use observability platforms (monitoring, logging).
- Focus on Automation and Tooling: The goal is to provide developers with tools that give fast, actionable feedback directly within their existing workflow. Think linters, pre-commit hooks, fast unit tests, CI checks that complete in minutes, not hours.
- Shared Understanding & Responsibility: Everyone on the team – developers, QA, security, ops, product – understands that quality, security and reliability are collective responsibilities. Enable developers to succeed early, rather than acting solely as downstream gates.
- Empowerment, Not Delegation: Giving developers the tools, platforms, training, expert support and safety nets to confidently manage appropriate aspects of testing, security and deployment early in the cycle.
To Shift Left the right way:
1. Assess Your Current State: Identify the biggest bottlenecks, the longest feedback loops and the areas causing the most friction.
2. Start Small: Don’t try to boil the ocean. Pick one specific area (e.g., automating dependency security scanning in CI) and focus on doing it well collaboratively.
3. Foster Cross-Functional Collaboration: Break down silos. Create opportunities for developers, QA, security and ops to work together on defining processes.
4. Invest in the Right Tools: Prioritize tools that integrate seamlessly into the developer workflow, provide fast feedback and automate repetitive tasks.
5. Provide Training and Support: Ensure developers have the necessary knowledge and resources to use new tools and processes effectively.
6. Measure and Iterate: Track key metrics (e.g., lead time for changes, deployment frequency, change failure rate, bug detection rates, developer satisfaction) to understand the impact and continuously improve.
Shift Left holds incredible potential to improve software delivery speed, quality and security. But its power lies in thoughtful implementation – a collaborative transformation focused on refining processes, leveraging automation and empowering developers with the right tools and support.

Beware the “Shove Left” anti-pattern. Simply dumping downstream tasks onto developers without changing the system is a recipe for burnout, inefficiency and failure.
July 30, 2025