Choosing a Kubernetes Policy Engine#

Kubernetes does not enforce security best practices by default. A freshly deployed cluster allows containers to run as root, pull images from any registry, mount the host filesystem, and use the host network. Policy engines close this gap by intercepting API requests through admission webhooks and rejecting or modifying resources that violate your rules.

The three main options – Pod Security Admission (built-in), OPA Gatekeeper, and Kyverno – serve different needs. Choosing the wrong one leads to either insufficient enforcement or unnecessary operational burden.

Choosing a Local Model#

The most expensive mistake in local LLM adoption is running a 70B model for a task that a 3B model handles at 20x the speed for equivalent quality. The second most expensive mistake is running a 3B model on a task that requires 32B-level reasoning and getting garbage output.

Matching model size to task complexity is the core skill. This guide provides a framework grounded in empirical benchmarks, not marketing claims.

Choosing a Log Aggregation Stack#

Logs are the most fundamental observability signal. Every application produces them, every incident investigation starts with them, and every compliance framework requires retaining them. The challenge is not collecting logs – it is storing, indexing, querying, and retaining them at scale without spending a fortune.

The choice of log aggregation stack determines your query speed, operational burden, storage costs, and how effectively you can correlate logs with metrics and traces during incident response.

Choosing a Monitoring Stack#

Monitoring is not optional. Without metrics, you are guessing. The question is not whether to monitor but which stack to use. The right choice depends on your cost tolerance, operational capacity, retention requirements, and how much you value control versus convenience.

Decision Criteria#

Before comparing tools, clarify what matters to your organization:

• Cost model: Are you optimizing for infrastructure spend or engineering time? Self-managed tools cost less in licensing but more in operational hours. SaaS tools cost more in subscription fees but less in engineering effort.
• Operational burden: Who manages the monitoring system? Do you have an infrastructure team, or are developers responsible for everything?
• Data retention: Do you need metrics for 15 days, 90 days, or years? Long retention changes the equation significantly.
• Query capability: Does your team know PromQL? Do they need ad-hoc analysis or mostly pre-built dashboards?
• Alerting requirements: Simple threshold alerts, or complex multi-signal alerts with routing and escalation?
• Team expertise: An organization fluent in Prometheus wastes that investment by switching to Datadog. An organization with no Prometheus experience faces a learning curve.

Options at a Glance#

Prometheus + Grafana (Self-Managed)#

Prometheus is the de facto standard for Kubernetes metrics. It uses a pull-based model, scraping metrics from endpoints at configurable intervals, and stores time series data on local disk. Grafana provides visualization. Alertmanager handles alert routing.

Choosing a Secret Management Strategy#

Secrets – database credentials, API keys, TLS certificates, encryption keys – must be available to pods at runtime. At the same time, they must not be stored in plain text in git, should be rotatable without downtime, and should produce an audit trail showing who accessed what and when. No single tool satisfies every requirement, and the right choice depends on your security maturity, operational capacity, and compliance obligations.

Choosing an Autoscaling Strategy#

Kubernetes autoscaling operates at two distinct layers: pod-level scaling changes how many pods run or how large they are, while node-level scaling changes how many nodes exist in the cluster to host those pods. Getting the right combination of tools at each layer is the key to a system that responds to demand without wasting resources.

The Two Scaling Layers#

Understanding which layer a tool operates on prevents the most common misconfiguration – expecting pod-level scaling to solve node-level capacity problems, or vice versa.

Choosing an Ingress Controller#

An Ingress controller is the component that actually routes external traffic into your cluster. The Ingress resource (or Gateway API resource) defines the rules – which hostnames and paths map to which backend Services – but without a controller watching those resources and configuring a reverse proxy, nothing happens. The choice of controller affects performance, configuration ergonomics, TLS management, protocol support, and operational cost.

Unlike CNI plugins, you can run multiple ingress controllers in the same cluster, which is a common pattern for separating internal and external traffic. This reduces the stakes of any single choice, but your primary controller still deserves careful selection.

Choosing Kubernetes Workload Types#

Kubernetes provides several workload controllers, each designed for a specific class of application behavior. Choosing the wrong one leads to data loss, unnecessary complexity, or workloads that fight the platform instead of leveraging it. This guide walks through the decision criteria and tradeoffs for each type.

The Workload Types at a Glance#

Decision Criteria#

The choice comes down to four questions:

CI/CD Patterns for Monorepos#

A monorepo puts multiple packages, services, and applications in a single repository. This simplifies cross-package changes and dependency management, but it breaks the assumption that most CI systems are built on: one repo means one build. Without careful pipeline design, every commit triggers a full rebuild of everything, and CI becomes the bottleneck.

The Core Problem#

In a monorepo, a commit that touches packages/auth-service/src/handler.ts should build and test auth-service and its dependents, but not billing-service or frontend. Getting this right is the central challenge of monorepo CI.

Circuit Breaker and Resilience Patterns#

In a microservice architecture, any downstream dependency can fail. Without resilience patterns, a single slow or failing service cascades into total system failure. Resilience patterns prevent this by failing fast, isolating failures, and recovering gracefully.

Circuit Breaker#

The circuit breaker pattern monitors calls to a downstream service and stops making calls when failures reach a threshold. It has three states.

States#

Closed (normal operation): All requests pass through. The circuit breaker counts failures. When failures exceed the threshold within a time window, the breaker trips to Open.