A load balancer is the front door for a service. It accepts traffic on one address, then spreads requests across multiple backend endpoints so the service stays responsive when demand jumps, and it stays available when an endpoint fails. This post is for platform teams, network teams, and application owners who want a clear mental model of load balancing, plus the practical decisions that matter in real environments. In this post you will learn The request path from client to backend, and what the load balancer does at each step How health checks and traffic steering work during failures and deployments When Layer 4 is enough, and when you need Layer 7 routing How to standardise load balancing across Kubernetes, VMs, and infrastructure To get started you can also watch Thomas Graf, CTO, Isovalent @ Cisco, dive into what is Isovalent Load Balancer in less than 60 seconds; How Does a Load Balancer Work? In most environments, a load balancer sits between clients and backend servers. A client request arrives at a single entry point, often a virtual IP address or a DNS name. The load balancer selects a backend server that is healthy and has capacity. The load balancer forwards the incoming request to that backend. The backend replies, either directly to the client or back through the load balancer, depending on the setup. Two concepts matter in almost every design: Frontend and backend servers: The frontend is the address clients connect to. The backend servers are the set of instances that actually handle the work. Health checks: A load balancer continuously checks whether a backend is healthy. If a backend fails, the load balancer reroutes traffic to healthy servers so the service stays up. Creating a good mental model of this architecture, the load balancer is the traffic director, it aims to keep the experience steady by spreading network traffic across multiple servers. Key Functions and Benefits of Load Balancing Load balancing is not just about spreading traffic. Modern load balancing solutions usually deliver a a number of reliability and performance outcomes: Improved performance and lower latency: When requests are distributed evenly, each server does less work per unit time, so responses tend to be faster. High availability and fault tolerance: If one backend goes down, traffic can be shifted to alternative healthy backend servers, which reduces downtime. Scalability during peaks: You can scale out by adding instances, then the load balancer can start sending new incoming requests to those new backends. Better operational control: Features such as gradual rollouts, connection draining, and traffic shifting let application teams update services with less risk. Security support: Many application level load balancers can help with TLS termination, routing policies, and rate limiting. Even when the load balancer is not your security layer, it can reduce blast radius by controlling how traffic reaches services. Load Balancing Algorithms and How They Differ A load balancer needs a method to decide which backend should receive the next request. These methods are called load balancing algorithms. Common load balancing algorithms include: Round robin load balancing: Requests are sent to each backend in sequence. This is simple and works well when all backends are similar, such as resource availability (think CPU and Memory configuration). Least connections: The next client request is sent to the backend with the fewest active connections, which can help when session length varies. IP hash: Requests are mapped to a backend based on the client IP address. This can help with session stickiness, but can cause uneven distribution if a small set of client IPs dominates. Weighted distribution: Backends receive traffic according to assigned weights, which is useful when servers have different capacity. Random selection: This method can be surprisingly effective at times, especially with enough backends, though it is usually paired with health checks and other safeguards. You will sometimes see these grouped as static load balancing algorithms versus dynamic load balancing algorithms. Static load balancing is when decisions are made with limited real time feedback, for example a simple round robin approach. Dynamic load balancing uses live signals, such as active connections, health check results, or latency signals, to make better choices when load changes. Different Types of Load Balancers There are several ways to classify types of load balancing. Two of the most common are by network layer, and by how the load balancer is delivered. Layer 4 vs Layer 7 load balancing Layer 4 load balancers, operating at the transport layer: Route traffic based on IP addresses and TCP or UDP ports. This is often used for high performance, protocol agnostic forwarding. Layer 7 load balancers, operating at the application layer: Make routing decisions using HTTP attributes such as hostnames, paths, headers, or cookies. This enables features such as path based routing and can support richer security and observability capabilities. Hardware vs software load balancers Hardware load balancers: These can be powerful physical boxes that take up space in your rack in your datacenter, but scaling often means buying more appliances, and automation can be harder. Software load balancers: Run on commodity servers, VMs, or inside a platform. This usually makes scaling and automation simpler, especially in cloud and platform environments. Cloud and global load balancing Elastic load balancing: Often used to describe cloud provider managed load balancing where capacity scales with demand. Global server load balancing: Distributes users across multiple datacenters or regions, often combined with dns load balancing so clients are directed to a nearby or healthy region. Kubernetes load balancing Kubernetes introduces its own patterns, such as Service type LoadBalancer, and integrations with external load balancers. The key point is that Kubernetes needs a way to expose services reliably to clients outside the cluster, and load balancing is the common solution. Examples of Load Balancing in Action Here are a few concrete examples of load balancing work you probably rely on every day: A public website handling variable demand: The load balancer spreads incoming requests across multiple web servers so a traffic spike does not overload a single server. I think this is the most common, and pretty much most common global websites you use today will be fronted by some kind of load balancing technology. Microservices traffic inside a platform: Requests from one service to another are distributed across multiple backend pods or instances, which keeps internal APIs responsive. How about when you access your banking app on your mobile and request a statement? That request will go via the app to various servers, services and backends to pull the data for you into a PDF! Enterprise SaaS uptime requirements: Load balancing provides failover so a single server failure does not take down a revenue critical application. Such as Splunk Cloud! Load Balancer Technology in the Cloud Native Era In many environments, load balancers sit in front of the platform and need to be automated like everything else. In cloud native environments, teams want the opposite, software based load balancers that are programmable, automated, and able to run wherever workloads run. Modern software load balancers can deliver high throughput without proprietary appliances. This matters when you need consistent performance and consistent operations across many environments. How Isovalent Load Balancer Helps Simplify Load Balancing Isovalent Load Balancer is built for teams that want one load balancing platform across Kubernetes, VMs, on premises, and public cloud. If you are tired of running different products in each environment, this gives you a single operational model you can apply across your estate. What you get Coverage from Layer 3 through Layer 7, so you can handle both transport services and application aware routing A high performance dataplane built on eBPF and Envoy Controls that fit enterprise operations, including RBAC so you can safely support multiple teams and tenants Deployment flexibility, run it standalone on bare metal Linux or as VMs, or deploy it in cluster as a Kubernetes add on when that fits your platform model How it fits in practice Put it at the edge for north south traffic into a platform Use it internally for shared services, APIs, and multi cluster patterns Migrate safely by introducing it alongside existing load balancers, then moving services one by one End result, fewer moving parts, simpler ownership boundaries, and a consistent way to deliver load balancing wherever your workloads run. Load Balancer FAQ What is a load balancer? A load balancer is a network component, hardware or software, that distributes incoming requests across multiple servers/backends, so no single endpoint becomes overloaded. This improves availability, performance, and reliability. What does a load balancer do? A load balancer routes user requests to backend servers based on factors such as health status, current load, and policy rules. If a server fails, the load balancer can reroute traffic to healthy backends. How does a load balancer work? A load balancer receives a client request at a frontend address, selects a healthy backend server using a load balancing algorithm, forwards the request, then handles the response path based on the chosen architecture. What are the benefits of using a load balancer? Common benefits include improved performance, higher availability, simpler scaling, reduced downtime during failures, and better operational control during deployments. What are the different types of load balancers? Common types include: Layer 4 load balancers, transport layer routing Layer 7 load balancers, application aware routing Hardware load balancers Software load balancers Cloud load balancers, including global server load balancing and dns load balancing patterns What are common load balancing algorithms? Common algorithms include round robin, least connections, IP hash, weighted distribution, and random selection. Static load balancing algorithms typically use simpler distribution rules, while dynamic load balancing algorithms use real time signals such as health, connections, or latency. Closing A load balancer is one of the simplest building blocks for keeping services available and responsive. It spreads network traffic across multiple servers, helps services survive failures, and gives teams safer ways to scale. If you are standardizing load balancing across Kubernetes, VMs, and data center environments, Isovalent Load Balancer is designed to provide one software defined approach across those platforms, with modern control surfaces and an eBPF and Envoy powered dataplane. Are you in need of load balancing capabilties for your applications?