Kubernetes is often sold as the one-size-fits-all platform for scaling applications. But when it comes to Node.js, the reality looks very different: sluggish autoscaling, bloated cloud bills, and resource models that don’t align with Node’s event-driven nature.
In this episode of The Node (& More) Banter, we cut through the hype and explore why Kubernetes isn’t always the perfect fit for Node.js workloads—and what teams can do about it.
We’ll dig into:
✅ Why autoscaling lag hurts bursty Node.js traffic
✅ How CPU/memory limits clash with Node’s async model
✅ The hidden costs of “elastic” scaling in Kubernetes
✅ Why treating Node.js like Java leads to waste and inefficiency
✅ Smarter strategies for scaling Node.js effectively in the cloud
If you’ve ever wondered why your Kubernetes setup feels slow, expensive, or unpredictable with Node.js, this conversation will help you rethink the defaults—and save both performance and money.
For enterprises, Node.js apps bring both agility and complexity. Over-provisioned clusters, slow autoscaling, and disconnected metrics can silently drive up costs and risk outages.
The future lies in predictive operations: runtime-aware scaling, unified observability, and cache intelligence—all working together to keep systems fast and efficient.
In this episode, we’ll explore how modern Node.js operations help enterprises:
✅ Cut infrastructure costs by up to 30% with smarter scaling
✅ Prevent outages before they happen with Event Loop–aware monitoring
✅ Gain full-stack visibility—from Kubernetes to caching efficiency
✅ Shift from firefighting to proactive operations
If you care about performance, cost, and operational clarity, this is the conversation you don’t want to miss.
APIs are the backbone of modern applications—but how often do they break because of mismatched payloads or vague documentation? That’s where type safety comes in. Type-safe clients don’t just save you from runtime errors; they make development faster, safer, and way less stressful.
In this episode, we explore why type-safe client generation matters, how it works under the hood, and why it’s becoming essential for teams building complex systems in Node.js and beyond. From automated TypeScript definitions to schema-driven code generation, this is the roadmap to more predictable and maintainable integrations.
We’ll cover:
✅ What “type-safe client generation” actually means
✅ How it eliminates guesswork and reduces integration bugs
✅ The role of OpenAPI and JSON Schema in generating trusted clients
✅ Real-world examples of broken integrations—and how type safety prevents them
✅ How type safety improves onboarding and long-term maintainability
If you’ve ever wrestled with unclear API docs, runtime surprises, or the dreaded undefined is not a function, this episode is your guide to making API integration reliable—without slowing down your team.
Sticking to an outdated Node.js version might feel safe—but under the hood, it can be a ticking time bomb. From unpatched security flaws to missed performance boosts, the tradeoffs aren’t always obvious until it’s too late.
In this episode, we’re joined by Erick Wendel to explore the hidden risks, technical debt, and missed opportunities that come from running on old Node.js releases—and why so many teams delay the upgrade in the first place.
We’ll dig into:
✅ How outdated versions silently impact performance and reliability
✅ The real security risks lurking in older releases
✅ Compatibility issues with modern libraries and tooling
✅ The gains you’re leaving on the table by not upgrading
✅ Practical strategies for safe, low-friction migrations
✅ The cultural, organizational, and technical reasons why teams resist updates
If your Node.js stack hasn’t been updated in a while—or you’re weighing the risks of upgrading—this conversation will give you the clarity (and the push) you need.
You can’t fix what you can’t see—and when it comes to performance issues in Node.js, flamegraphs are one of the most powerful tools you have. But what are they, really? And how do you actually use them?
In this episode, we break down everything you need to know about flamegraphs—from the theory behind them to their implementation in Node.js. Whether you’re chasing event loop delays, CPU hotspots, or blocking I/O, flamegraphs reveal what your app is really doing under the hood.
We’ll cover:
✅ What flamegraphs are and how they visualize stack traces over time
✅ How they’re implemented in Node.js (using V8 and async hooks under the hood)
✅ How to generate and capture them in dev and production
✅ How to read them: interpreting stack height, width, and call depth
✅ Real examples of uncovering performance bottlenecks
✅ How we’ve built automated flamegraph capture into
Platformatic WattIf you’ve ever struggled with unexplained latency, inconsistent P99s, or just want to understand your Node.js app at a deeper level—this episode is your crash course in seeing the invisible.
When it comes to speeding up API calls, many developers jump straight into custom caching layers—rediscovering problems that HTTP solved decades ago.
In this episode, we dig into why HTTP caching is still one of the most underused superpowers in web performance.
You’ll learn:
✅ How standard HTTP cache headers (Cache-Control, ETag, Vary) can do the heavy lifting
✅ Why revalidation beats blind refetching
✅ Common mistakes that lead teams to build fragile, homegrown caching hacks
✅ How proper HTTP caching strategies improve scalability, latency, and cost
Before you spin up another custom cache, make sure you’re using the protocol that was built for the job.
At a glance, setImmediate() seems like just another async tool in Node.js—but misuse it, and you could end up with unresponsive apps, delayed executions, or even missed events.
In this episode, we break down why setImmediate() is often misunderstood, where it actually fits into the event loop, and the hidden performance and logic traps it can introduce in real-world applications.
We’ll dig into:
✅ How setImmediate() really works (vs setTimeout and process.nextTick)
✅ Common pitfalls that can silently break your logic
✅ Real-world debugging stories from misused timing functions
✅ Safer patterns for deferring work in production
✅ Why understanding the event loop is still essential for Node.js devs
If you’ve ever added a setImmediate() and hoped for the best, this episode is for you.
From background tasks to cross-request tracing, many developers rely on async context to glue state across their code. But under the hood? It’s fragile, unpredictable, and often a performance landmine.
In this episode, we’re joined by Bryan English to dive into the lesser-known tradeoffs of async context management in Node.js—and how to build observability without breaking your app or your brain.
We’ll dig into:
✅ What async context really is (and what it isn’t)
✅ Why it breaks in production, especially under load
✅ The performance overhead of AsyncLocalStorage
✅ Real-world patterns for context propagation that don’t implode
✅ Lessons from building observability tools on top of it all
If you've ever struggled to trace a request, debug a memory leak, or just wondered why your context vanished mid-flight—this one’s for you.
AI tools are everywhere—but sharing context, conversations, and outcomes across a team? That’s still a mess. From copy-pasting chat logs to syncing prompts over Slack, most teams are stitching together brittle workflows that don’t scale.
In this episode, we dive into our latest release of AI-Warp, which brings stateful interactions and resumable connections to the table. We’ll walk through how we used it to build a Slack bot that lets our team pick up, share, and continue AI conversations—without losing context or flow.
We’ll dig into:
✅ Why most AI tools break down in team settings
✅ What it takes to make LLM interactions persistent and shareable
✅ How resumability changes the way teams collaborate around AI
✅ The real-world challenges we faced stitching AI-Warp into Slack
If your team is using AI but stuck treating it like a solo tool, this episode will show what’s possible when conversations—and context—can persist and evolve.
As enterprises race to integrate AI into their systems, a quiet shift is underway: the rise of the Model Context Protocol (MCP) as the de facto interface between models, services, and infrastructure. MCP isn’t just another wire format—it’s a way to give structure, semantics, and shared context to how systems communicate in the AI era.
In this episode, we're joined by Alex Olivier, Co-founder & CPO of Cerbos, to explore how MCP is reshaping how teams think about interoperability, policy, and context-aware behavior. We also go behind the scenes on our effort to bring MCP to Fastify—what we’ve learned about runtime integration, developer experience, and where the protocol is heading next.
We’ll dig into:
✅ Why MCP is gaining traction as the new “API” for model-aware systems
✅ The hidden complexity of securing and governing context-rich protocols
✅ How Fastify is evolving to support MCP natively
✅What it means to prioritize DX in a protocol-first world
If you’re building systems that talk to models—or models that talk to systems—this is the protocol shift you can’t ignore.
In the quest for consistency, platform teams often turn to templating—starter kits, golden paths, boilerplate generators. The goal? Faster onboarding, safer deployments, fewer surprises. The result? A growing zoo of subtly different snowflakes, broken assumptions, and yet another internal tool no one really wants to maintain.
In this episode, we dig into the dark side of templating culture—why efforts to standardize often backfire, and how the dream of "just use the template" slowly erodes into chaos.
We’ll explore:
Why starter kits rarely stay aligned with reality
If you've ever cloned the internal boilerplate, spent hours unpicking someone else's one-line deviation, or tried to refactor the "official" template repo that no one owns anymore—this one’s for you.
Sidecars were meant to isolate concerns and standardize infrastructure. Instead, many teams end up managing a growing fleet of proxies, daemons, and coordination glue that quietly slows everything down.
In this episode, we uncover the sidecar trap—how a well-intentioned pattern can balloon into operational drag, and what happens when the architecture starts optimizing for the infrastructure, not the app. Roberto Bianchi, Staff Engineer at Spendesk, joins us to share what his team learned as they experimented moving away from a sidecar-heavy setup—and how Watt can help simplifying operations and boost performance.
We’ll dig into:
✅ Why sidecars often become bottlenecks at scale
✅ The hidden tax on observability, networking, and deploy times
✅ How Spendesk re-imagined their stack using Watt
✅ What changes when infrastructure becomes app-native
If you're deep in the weeds of service mesh, observability agents, or just tired of debugging YAML for yet another sidecar—you’ll want to hear this one
Composable architectures have focused heavily on APIs, services, and build-time integrations—but what about composition at runtime?
In this episode, we explore the often-ignored layer of modern systems: how code actually runs, cooperates, and scales after deployment. We unpack the shift from static, pre-built delivery pipelines to runtime-aware platforms that dynamically orchestrate logic across languages, processes, and systems.
Topics include:
✅ Why runtime composition matters more as stacks become more distributed
✅ Running multiple runtimes (Node, PHP, WASM) together, intentionally
✅ How orchestration at the execution layer beats orchestration over the network
✅ The limits of dev-centric frameworks when operations are an afterthought
If you’ve ever questioned why your “modular” system still feels fragile in production, this conversation connects the missing dots.
Internal platforms were supposed to streamline development. Instead, many teams find themselves maintaining internal systems more complex than the apps they support.
In this episode, we explore the platform trap—how well-meaning abstractions can evolve into burdens, and what separates effective internal platforms from over-engineered detours.
We’ll dig into:
✅ When “platform” becomes a second product
✅ How internal tools silently shape team structure and velocity
✅ The fine line between empowering developers and creating lock-in
✅ The rise of runtime-aware platforms and why they might offer a better path
If you’re building developer platforms, or stuck maintaining one that no one really loves, this is your episode.
In today’s composable world, teams face a critical decision: make the pieces work together—or build the platform themselves. On paper, the flexibility is liberating. In reality, it comes with trade-offs that hit engineering velocity, cost, and maintainability.
In this episode, we break down the models behind modern digital platforms:
✅ The true cost of "just integrating" SaaS and headless tools
✅ When PaaS simplifies operations—and when it limits you
✅ Why on-prem isn't dead, especially for control and cost at scale
✅ The illusion of no-code glue and the burden it shifts to developers
We’ll also explore how “build” doesn’t have to mean from scratch—and how smart reuse, automation, and runtime-level composition are changing the equation.
Whether you're scaling a commerce platform or rethinking your content stack, this episode gives you a clear lens on one of the most misunderstood choices in modern architecture.
Modern architectures promise flexibility—mix the best services, swap parts anytime, go faster. But somewhere between headless CMSs, personalization engines, CDNs, and frontend frameworks... things start to drag.
In this episode, we unpack the hidden cost of assembling your tech stack like LEGO:
✅ Why delivering content at scale gets harder with every new service
✅ The tension between personalization and performance
✅ How frontend frameworks aren’t solving orchestration, just shifting the pain
✅ Where caching helps—and where it makes things worse
We’ll also reflect on what the MACH movement (Microservices, API-first, Cloud-native, Headless) got right—and where it left teams with more integration work than innovation.
If you’re building the modern web and wondering why it feels like a treadmill instead of a rocket… this episode is for you.
In this episode, we take a hard look at the Backend-for-Frontend (BFF) pattern: why it was introduced, where it actually helps, and where it starts to fall apart in real-world microservices architectures.
We explore the core problems BFFs were meant to solve—API orchestration, frontend-specific shaping, security boundaries—but also the unintended costs: duplication of business logic, latency introduced by over-network composition, and fragile coupling to backend services.
We’ll also dive into practical solutions:
✅When to use a BFF and when to consolidate
✅How caching can save or sink your BFF
✅Design tips for reducing orchestration overhead
✅Emerging approaches like multi-runtime co-location (Node + PHP, serverless + monoliths) that challenge the need for standalone BFFs entirelyIf you're scaling a frontend-heavy app or drowning in edge-layer glue code, this one’s for you.
PHP once ruled the web—and for good reason. Its simplicity and ubiquity made it the go-to runtime for millions of developers. But in today’s cloud-native world, Node.js has taken center stage for event-driven performance and modern tooling.
At Platformatic, we’re not picking sides—we’re bridging them.
In this episode, we dive into why PHP and Node.js aren’t rivals but the perfect pair for modern backend architecture.
We talk about:
✅ Why PHP still powers critical workloads across the internet
✅ How we enabled PHP to run inside worker threads in Node.js with WATT
✅ The power of inter-process communication (IPC) in simplifying legacy integration
✅ And why this fusion is key to consolidating fragmented microservices into efficient, maintainable systems
If you're wrestling with legacy PHP services or scaling Node.js in a complex architecture, this one's for you.
The boundary between physical and digital is dissolving. With OpenAI reportedly partnering with Jony Ive to build next-gen AI-powered hardware, a new era of intelligent IoT devices is taking shape—one where APIs need to do more than just respond. They need to understand, predict, and adapt.
In this episode, we explore what it takes to modernize traditional APIs for this future.
We break down:
✅ The shift from request-response to long-lived, state-aware connections.
✅ Why protocols like MQTT are foundational for real-time, resilient device communication.
✅ The architecture implications of resumable sessions, edge inferencing, and handling connection volatility.
✅ And how to embed AI-readiness into your system design—before the wave hits.
Whether you're a backend engineer, API architect, or just curious how the web of devices is becoming the nervous system of AI, this episode is for you.
In this episode, we dive into two powerful architectural patterns that are often misunderstood: Command Query Responsibility Segregation (CQRS) and Event Sourcing. We unpack how CQRS can bring clarity and performance to complex systems by decoupling reads from writes, and how Event Sourcing allows you to track every state change as a series of immutable events.But it’s not all sunshine and scalability — we also discuss when these patterns introduce more complexity than value.
You’ll learn:
✅The core principles of CQRS and Event Sourcing
✅Real-world use cases where they shine (think audit logs, complex workflows, and high-scale systems)
✅Situations where they add unnecessary overhead (like simple CRUD apps)
✅How to design a system around eventual consistency
✅Common pitfalls and best practices from the trenches
Whether you’re building a fintech platform, a distributed e-commerce system, or just exploring architectural patterns, this episode gives you the clarity to decide: Is CQRS and Event Sourcing the right fit — or a solution looking for a problem?
