What is a Kubernetes Load Balancer?
Load balancing in Kubernetes is the process of distributing network traffic across multiple pods to ensure high availability, scalability, and optimal performance. Because pods are ephemeral and their IP addresses change frequently, Kubernetes uses Services and Ingress to provide stable endpoints for accessing applications.
Types of internal and external load balancing:
Kubernetes offers several native methods to manage traffic depending on whether it originates from inside or outside the cluster:
- ClusterIP (Internal): The default service type. It provides a stable internal IP address accessible only from within the cluster. It is primarily used for service-to-service communication.
- NodePort (External): Exposes a service on a specific port (30000–32767) on every node’s IP. It allows external access but is typically used for development or testing.
- LoadBalancer (External): The standard for production. It provisions a cloud provider's load balancer (e.g., AWS ELB, Google Cloud Load Balancer, or Azure Load Balancer) to route external traffic to your service.
- Ingress (Layer 7): An API object that manages external HTTP and HTTPS routes. Unlike basic load balancers, Ingress supports advanced features like URL-based routing, SSL termination, and name-based virtual hosting.
Kubernetes load balancers work by abstracting the complexity of routing requests to the correct pods, even as those pods are created, destroyed, or rescheduled across nodes. This abstraction is critical in environments where workloads are constantly changing. By integrating load balancing into the architecture, Kubernetes enables developers and operators to build fault-tolerant applications without manually configuring traffic routing for each deployment.
In this article:
Kubernetes environments are dynamic by design. Pods are created, destroyed, and rescheduled frequently, which makes static routing unreliable. Load balancing keeps traffic flowing correctly despite these changes:
- Dynamic pod lifecycle: Pods are ephemeral and can change at any time. Load balancing routes traffic to healthy, running instances without manual updates.
- High availability: Distributing traffic across multiple pods prevents a single point of failure. If one pod fails, others continue serving requests.
- Scalability: Applications often scale horizontally by adding or removing pods. Load balancers adjust traffic distribution as the number of pods changes.
- Service abstraction: Kubernetes services provide a stable endpoint, while load balancing hides the underlying pod complexity. Clients do not need to track individual pod IPs.
- Efficient resource utilization: Traffic is spread evenly across pods, preventing some instances from being overloaded while others remain idle.
- Support for internal and external traffic: Kubernetes handles both cluster-internal communication and external user requests. Different load balancing mechanisms address each scenario.
- Fault tolerance and resilience: Load balancers detect unhealthy pods and stop sending traffic to them.
Kubernetes supports multiple approaches to load balancing, both within the cluster (internal) and at the cluster boundary (external). Internal load balancing routes traffic between services and pods inside the cluster. External load balancing exposes applications to users or systems outside the cluster and handles inbound traffic from external networks.
ClusterIP (Internal)
ClusterIP is the default service type in Kubernetes, designed for internal communication within the cluster. When a service is created with the ClusterIP type, Kubernetes assigns it a stable virtual IP address accessible only from within the cluster’s network. This enables pods and services to communicate using consistent addresses, even as underlying pods are created or destroyed. The internal DNS system resolves service names to their ClusterIP.
ClusterIP uses kube-proxy to distribute requests among the available pods backing the service. This approach works well for microservices architectures where components communicate without exposing endpoints outside the cluster. However, ClusterIP cannot expose applications to external users, as the assigned IP is not routable from outside the Kubernetes environment.
NodePort (External)
NodePort exposes a Kubernetes service on a static port on each node’s IP address, making it accessible externally. When using NodePort, Kubernetes allocates a port from a predefined range, usually 30000–32767. Traffic sent to this port on any cluster node is forwarded to the service and its backing pods.
While NodePort is simple to set up and does not require cloud provider integration, it has limitations. Exposed ports must be managed carefully to avoid conflicts, and external traffic must target specific node IPs and ports. NodePort is often used in development environments or as a building block for more advanced load balancing solutions, such as those provided by external load balancers or ingress controllers.
LoadBalancer (External)
The LoadBalancer service type integrates with supported cloud providers to provision an external load balancer that routes traffic to the Kubernetes service. When a LoadBalancer service is created, Kubernetes requests a cloud-native load balancer, such as AWS ELB, Azure Load Balancer, or Google Cloud Load Balancer, and configures it to forward external requests to the service’s backing pods.
LoadBalancer services are suited for production environments where stable access from outside the cluster is required. However, each LoadBalancer service typically provisions a separate external load balancer, which can increase infrastructure costs. For more complex routing or to reduce the number of external load balancers, organizations often combine LoadBalancer with ingress controllers or use advanced networking plugins.
Ingress (Layer 7)
Ingress is a Kubernetes resource that provides layer 7, application layer, load balancing and routing. An ingress controller listens for HTTP and HTTPS traffic, then routes requests to backend services based on rules such as hostnames, URL paths, or headers. This allows multiple services to be exposed through a single external IP and reduces the need for multiple LoadBalancer or NodePort services.
Ingress also supports SSL/TLS termination, authentication, and rate limiting. Deploying and configuring ingress requires an ingress controller, such as NGINX, Traefik, or cloud-native options, and depends on proper rule management and infrastructure compatibility.
Prakash Sinha
Prakash Sinha is a technology executive and evangelist for Radware and brings over 29 years of experience in strategy, product management, product marketing and engineering. Prakash has held leadership positions in architecture, engineering, and product management at leading technology companies such as Cisco, Informatica, and Tandem Computers. Prakash holds a Bachelor in Electrical Engineering from BIT, Mesra and an MBA from Haas School of Business at UC Berkeley.
Tips from the Expert:
In my experience, here are tips that can help you better optimize Kubernetes load balancing:
1. Use topology-aware routing intentionally. For east-west traffic, enable topology hints or zone-aware routing where supported so requests stay in the same zone when possible. This cuts cross-zone latency and cloud data-transfer costs, but only works well when replicas are distributed evenly.
2. Tune connection draining before pod termination. Add a preStop hook and enough terminationGracePeriodSeconds so long-lived requests can finish before the endpoint disappears. Without this, rolling updates look healthy in Kubernetes but still cause client-side resets and intermittent 502s.
3. Watch conntrack saturation on busy nodes. Many “random” load-balancing issues are actually Linux conntrack table exhaustion under burst traffic. Monitor conntrack usage, raise limits carefully, and test failover during peak loads because kube-proxy behavior degrades badly when the node is state-table constrained.
4. Prefer ipvs or eBPF datapaths for high-scale services. At larger pod and service counts, iptables-based forwarding adds latency and operational noise during endpoint churn. IPVS or eBPF-based implementations usually converge faster and handle frequent endpoint updates more gracefully.
5. Separate health semantics for startup, readiness, and overload. A pod can be alive and technically ready but still not safe to receive full traffic. Expose a readiness signal that reflects dependency health, warm caches, and queue depth so the balancer stops sending traffic before saturation becomes visible to users.
To create an external load balancer in Kubernetes, define a service with the LoadBalancer type. This instructs Kubernetes to provision a cloud-based load balancer that routes external traffic to your application. Instructions are adapted from the Kubernetes documentation.
Prerequisites
You need a running Kubernetes cluster and a configured kubectl CLI. The cluster must run in an environment that supports external load balancers. Use a cluster with at least two worker nodes.
Step 1: Create a LoadBalancer Service
Define the service using a YAML manifest by setting type: LoadBalancer:
apiVersion: v1 kind: Service metadata: name: example-service spec: selector: app: example ports: - port: 8765 targetPort: 9376 type: LoadBalancer
Apply the manifest with:
kubectl apply -f service.yaml
Alternatively, create the service directly using kubectl:
kubectl expose deployment example \ --port=8765 \ --target-port=9376 \ --name=example-service \ --type=LoadBalancer
This command creates a service that uses the same label selectors as the referenced deployment.
Step 2: Retrieve the External IP
Once the service is created, Kubernetes provisions an external load balancer. To find its IP address, run:
kubectl describe services example-service
In the output, look for the LoadBalancer Ingress field. This shows the external IP assigned to your service.
If you are using Minikube, retrieve the URL with:
minikube service example-service --url
Step 3: Configure Client IP Preservation (Optional)
By default, backend pods do not see the original client IP. To preserve it, set externalTrafficPolicy to Local in your service spec:
spec: externalTrafficPolicy: Local type: LoadBalancer
This routes traffic only to node-local endpoints and keeps the original client IP. It may result in uneven traffic distribution across pods.
You can also define a specific health check port using healthCheckNodePort if needed.
Notes and Limitations
External load balancers distribute traffic across nodes, not individual pods. Since they are not aware of how many pods run on each node, traffic may not be perfectly balanced at the pod level.
Uneven Traffic Distribution
Uneven traffic distribution occurs when a load balancer does not evenly spread requests across available pods, leading to some pods being overloaded while others remain underutilized. This imbalance can result from misconfigured load balancing algorithms, network latency, or limitations in the underlying infrastructure.
In Kubernetes, kube-proxy and external load balancers may use round-robin or random selection, which do not always account for pod health or current load. The consequences include degraded performance, increased latency, and a higher risk of pod failures due to resource exhaustion.
Cost of External Load Balancers
External load balancers provided by cloud platforms incur additional costs, as each load balancer service often provisions a dedicated resource with its own pricing. In environments with many exposed services, these costs can add up and affect the overall budget.
This becomes more pronounced in clusters that scale dynamically or operate in multiple regions. To control costs, assess whether each service needs its own external load balancer or whether traffic can be consolidated through an ingress controller.
Complexity in Multi-Cloud Setups
Managing load balancing across multiple cloud providers introduces complexity due to differences in networking models, APIs, and load balancer implementations. Each provider provisions and configures load balancers differently, which can lead to inconsistent behavior across environments.
This makes it harder to maintain a unified deployment and routing strategy when applications span multiple clouds. These inconsistencies affect service exposure, health checks, and traffic routing policies. For example, features like SSL termination, idle timeouts, or session affinity may behave differently depending on the provider. Teams often need provider-specific configurations, which increases operational overhead and reduces portability.
Organizations should consider the following practices to improve load balancer performance in Kubernetes.
1. Use Readiness and Liveness Probes to Control Traffic Flow
Readiness probes determine whether a pod can receive traffic. If a probe fails, Kubernetes removes the pod from service endpoints, so load balancers stop routing requests to it. This prevents sending traffic to pods that are starting up, overloaded, or temporarily unhealthy.
Liveness probes detect when a pod needs a restart. Together, these probes ensure that only healthy pods participate in load balancing. Tune probe intervals and thresholds carefully. Aggressive settings can cause unnecessary restarts or traffic drops, while loose settings may allow unhealthy pods to serve requests.
2. Choose the Right Load Balancing Layer (L4 vs. L7)
Layer 4, transport-level, load balancing routes traffic based on IP and port. It is suitable for TCP/UDP services and high-throughput workloads. Kubernetes services like ClusterIP and NodePort operate at this layer.
Layer 7, application-level, load balancing understands HTTP/HTTPS semantics. It can route based on paths, hostnames, headers, or cookies. Ingress controllers operate at this layer and are suited for microservices and APIs that require fine-grained routing, TLS termination, or authentication. Choose between L4 and L7 based on application requirements.
3. Implement Autoscaling with Traffic Awareness
Horizontal pod autoscaler (HPA) adjusts the number of pods based on metrics like CPU, memory, or custom metrics such as request rate. When combined with load balancing, this ensures traffic is distributed across a dynamically scaling set of pods.
For better results, use metrics that reflect real traffic, such as requests per second or queue length. Consider scaling delays and stabilization windows. Rapid scaling up or down can create instability if load balancers continuously adjust to changing endpoints.
4. Add Security at the Load Balancer Layer
Load balancers are a natural enforcement point for security controls. You can terminate TLS at the load balancer to encrypt traffic and offload cryptographic work from application pods. Additional protections include IP allowlists, rate limiting, and web application firewalls (WAF).
You can also integrate authentication mechanisms such as OAuth or mutual TLS at this layer. This ensures that only verified users or services can access applications before requests reach backend pods. Centralizing security controls at the load balancer reduces the burden on individual services and provides a consistent enforcement point for access policies.
5. Use Global Server Load Balancing
Global server load balancing (GSLB) distributes traffic across multiple clusters or regions. It uses DNS or geo-routing to direct users to the closest or healthiest endpoint. This approach is useful for multi-region deployments and disaster recovery. If one region becomes unavailable, traffic can be redirected to another.
Account for DNS caching and propagation delays, which can affect failover speed. This can lead to users being routed to unavailable endpoints for a short period after a failure. To mitigate this, configure low DNS time-to-live (TTL) values and use health checks with automated failover to redirect traffic more quickly to healthy regions.
Kubernetes load balancing is essential for distributing traffic across containerized workloads, maintaining availability, and supporting application scalability across dynamic environments. However, uneven traffic distribution, exposed ingress points, and multi-cloud complexity can introduce operational and security risks. Radware helps organizations optimize Kubernetes load balancing by combining traffic intelligence, runtime protection, and adaptive security controls designed for cloud-native environments.
Radware Kubernetes Web Application Firewall (KWAAP) provides Kubernetes-native protection for ingress controllers, APIs, and application workloads. It integrates directly into Kubernetes environments to secure traffic flows while maintaining visibility into service communication and workload behavior. Adaptive protections help mitigate Layer 7 attacks, malicious requests, and API abuse targeting exposed load-balanced services. Real-time analytics also improve operational awareness across distributed container environments.
Radware Cloud WAF Service strengthens external load balancing security by protecting internet-facing Kubernetes services against OWASP Top 10 attacks, bot abuse, and application-layer DDoS attempts. AI-driven traffic inspection identifies malicious requests before they reach backend workloads, reducing the risk of overloaded services or disrupted traffic distribution. Adaptive protections also help enforce consistent security policies across hybrid and multi-cloud deployments. This improves resilience for externally exposed Kubernetes applications.
Radware Application Protection Service delivers integrated runtime protection for applications and APIs operating behind Kubernetes load balancers. Behavioral analysis and ML-driven detection help distinguish legitimate traffic spikes from malicious activity, improving traffic management and reducing false positives during scaling events. Centralized visibility supports policy consistency across distributed environments and dynamic workloads. These capabilities help organizations maintain secure and reliable application delivery.
Radware Bot Manager mitigates automated abuse targeting Kubernetes-hosted applications, including scraping, credential stuffing, reconnaissance, and resource exhaustion attacks. Advanced detection differentiates legitimate automation from malicious bots attempting to exploit exposed ingress points or overwhelm services behind load balancers. Reducing automated traffic noise improves performance stability and helps maintain balanced traffic distribution across workloads. Continuous monitoring also provides visibility into evolving attack patterns.