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Stay up to date with our latest articles about cloud-native technologies, Kubernetes, and DevOps.
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Kubernetes load balancing should be considered on three separate levels: the public entry at the edge, forwarding into the cluster network, and distribution to the actual workloads. L4 and L7 serve different purposes. The ayedo Edge Cloud enables this separation independently of the provider – with ayedo Managed Kubernetes as well as with externally operated clusters.
TLS termination at the edge is not merely a certificate task. It defines where HTTPS transitions into processable HTTP requests and where routing, protection mechanisms, and load balancing occur. For APIs, this transition connects TLS, L7 routing, and backend shielding into a central architectural decision.
In L4 load balancing, the original client connection often terminates at the edge. The backend initially only sees the IP address of the load balancer. The Proxy Protocol transmits the original connection data as preliminary metadata. For this to work reliably, the edge, target protocol, and backend must have the same expectation regarding format, position, and trust boundary.
Session persistence ties consecutive requests from a client to the same backend. This may be necessary for legacy-oriented, stateful applications but degrades scaling, failover, and load distribution. Therefore, L7 load balancing should first check if the application state can be managed centrally or distributed outside of individual backends.
Backend pools are not just a configuration aid but an architectural decision. They determine which backends receive traffic together, which health checks apply, and how failover functions. A sensible structure is oriented towards application, protocol, and operational responsibility—not solely on server locations or cluster affiliation.
HTTP Load Balancing processes requests at Layer 7, allowing it to incorporate HTTP methods, paths, headers, or hostnames into routing decisions. Pure TCP forwarding remains protocol-agnostic. For APIs and web applications, this difference is architecturally significant: Layer 7 enables application-aware routing, centralized TLS termination, and more targeted operational and security controls.
TCP Load Balancing distributes connections at the transport layer without evaluating HTTP content. It is suitable for protocols and services where transparency, protocol fidelity, and low processing depth are more important than URL or header-based routing. Key factors include appropriate backend pools, health checks, and a clear understanding of existing TCP connections.
L4 L7 load balancing is not about a universally better technology. TCP load balancing distributes connections without understanding higher protocol content. HTTP load balancing understands requests and can therefore route, protect, and terminate them specifically. The key factors are protocol, traffic visibility, and the required routing functions.
Digital sovereignty in the network is not achieved by location alone, but through controllable technical dependencies. An own Autonomous System, proprietary network infrastructure, and manageable routing decisions increase operational responsibility and reduce dependency on individual providers. The key is who can actually control routing, protection, and accessibility.
BGP routing determines which network path a client uses to reach the edge. However, it does not indicate whether a specific backend is operational. This task is handled by health checks and backend failover within the edge cloud. Separating these two failure domains prevents false expectations and simplifies operational analysis.
With Kubernetes 1.37, `metrics.k8s.io` is released as `v1`, marking it as a Stable API. The Resource Metrics API has been a part of the established infrastructure of a Kubernetes cluster for years: it provides current CPU and memory usage data for nodes and pods, is consumed by `kubectl top`, and forms the basis for resource-based autoscaling via the HorizontalPodAutoscaler.
Historical application structures and evolved monoliths often form the operational backbone of established companies. However, as the demands on digital business processes become more dynamic, these traditional infrastructures increasingly become costly barriers to innovation—especially when every code change or platform migration threatens business-critical downtime.
In tenders and procurement processes within the industrial, financial, and critical infrastructure sectors, mid-sized service providers are observing a fundamental shift: Pure functionality promises and ISO certificates are no longer sufficient for large corporations. In the context of **NIS-2**, **DORA**, and stringent supply chain audits, purchasers and security officers demand explicit proof that business-critical data flows and service workflows are portable in an emergency and not held hostage by individual US SaaS monopolies.
In many medium-sized IT organizations, identity and access management has organically evolved into a confusing patchwork over the years. Local user databases in isolated SaaS tools, manual password lists, and inconsistently enforced multi-factor procedures open dangerous attack vectors and make regulatory evidence impossible in critical situations. With the implementation of strict supply chain security requirements like **NIS-2** and industry-specific KRITIS audits, this identity chaos threatens to become a direct exclusion criterion in the awarding of framework contracts.
In the commercial mid-market, standard SaaS was considered the economic optimum for years: no acquisition costs for servers, seemingly transparent per-user pricing, and zero administrative effort. However, as the workforce grows and compliance requirements increase, the cost calculation shifts. Linear licensing models, opaque feature tierings, and annual price increases of 15 to 25% turn the supposedly lean cloud strategy into a financial bottomless pit.
In many medium-sized service and industrial companies, the IT landscape resembles a patchwork of isolated SaaS tools: Zendesk for tickets, Microsoft Teams for chats, SharePoint for files, and DocuSign for signatures. What appears modern in isolation proves to be an operational bottleneck in daily business, slowing employees down with constant context switching and scattering business-critical data across countless US clouds.
Many medium-sized industrial and service companies are lulled into a false sense of security: Contracts with US hyperscalers specify server locations in Frankfurt or Dublin, and compliance dashboards show green checkmarks. However, due to intensified supply chain security audits and the expansion of regulations like **NIS-2**, operators of critical infrastructures (KRITIS) increasingly demand comprehensive evidence of actual data access rights.
In the dynamic e-commerce business, the client portfolio of agencies and digital service providers often grows faster than the underlying hosting infrastructure. Over the years, established single-server setups, historically grown configuration differences, and fragmented hosting providers become massive stability and security risks beyond a certain operational size. When marketing campaigns, TV appearances, or seasonal events like Black Friday generate sudden traffic spikes, monolithic single installations reach their physical limits—with fatal consequences for availability commitments and business relationships.
In modern CI/CD pipelines, fast release frequency is often considered the primary success metric. However, for platform operators and software providers in regulated markets, this unchecked dynamism increasingly leads to severe security risks: When external base images, third-party libraries, and ephemeral dependencies are rolled out uncontrollably into production clusters, the software supply chain becomes an unpredictable entry point for attackers. The binding requirements of the NIS-2 directive and the Digital Operational Resilience Act (DORA) therefore demand a fundamental shift in direction—away from trusting deployments, towards a seamlessly verifiable software supply chain security.
In highly regulated industries such as banking and insurance, modern SaaS business models rarely fail due to application logic, but rather due to restrictive hosting requirements of enterprise customers. While agile fintechs aim to scale their platforms in standardized cloud environments, conservative institutions and public entities demand on-premises operations behind the corporate firewall due to data classification and compliance reasons. For software manufacturers, this discrepancy traditionally leads to costly codebase fragmentation and significant friction losses in platform engineering.
In regulated financial and software environments, two opposing worlds collide: development teams demand maximum release speed through automated CI/CD pipelines, while bank auditors and regulators, following DORA (Digital Operational Resilience Act) and MaRisk, require comprehensive, tamper-proof evidence for every single system change. In practice, this tension often leads to bureaucratic ticket systems and manual approval processes that slow down modern DevOps cycles and still cannot prevent configuration drift on production systems.
For regulated financial service providers and SaaS vendors, building on proprietary US hyperscaler services was long the fastest path to market readiness. However, with the binding requirements of the Digital Operational Resilience Act (DORA), risk assessment has fundamentally shifted: perceived efficiency advantages through managed relational databases, proprietary secret management, or cloud-specific ingress controllers have become significant concentration risks. Banks and regulators now demand proof that platforms can be ported within defined timeframes without months-long code refactoring crippling operations.
Many IT decision-makers are lulled into a false sense of security when using modern Observability SaaS solutions: After all, supposedly only technical health checks and availability data are processed. However, in regulated industries and mature platform architectures, this blind spot is increasingly proving to be a legal and operational liability risk. What appears on paper as non-critical uptime monitoring in practice continuously transmits sensitive metadata across European borders.
In dynamic cloud-native environments, manual configuration of monitoring targets is one of the greatest operational risks. When microservices are deployed multiple times a day via GitOps, documentation and maintenance of external health checks inevitably lag behind. The result is unmonitored shadow endpoints in production that only become noticeable when customers report connection issues or security-related misconfigurations escalate.
