Less work per packet
XLB rewrites and routes packets without copying application data through a userspace proxy process.
XLB vs HAProxy
Move packets instead of proxying every byte.
HAProxy is a proven general-purpose proxy. At extreme Layer 4 scale, accepting one connection and creating another means more socket work, more payload movement, and a larger fleet to operate. XLB routes the connection’s packets in the Linux kernel for lower latency, more stable performance under load, and no proxy tuning.
About half the serversLower, steadier latencyCompletely tuning free
Why teams choose XLB
A better load-balancing tier at serious scale.
HAProxy handles far more protocol and proxy use cases. XLB is purpose-built for the highest possible IPv4 and TCP load-balancing performance with the least operational work.
A smaller fleet
For planning, XLB targets roughly half the load-balancer servers required for the same pure L4 workload.
No tuning cycle
There are no proxy workers, buffers, connection pools, thread maps, or application-specific knobs to tune.
| What matters | HAProxy | XLB |
|---|---|---|
| Packet path | TCP stream is accepted and relayed through the proxy process | Packets are selected, rewritten, and routed in the Linux kernel |
| Connection model | Client-side and backend-side connections | One end-to-end client connection routed through XLB |
| Application payload | Moves through userspace proxy buffers | No userspace payload copy |
| Latency under load | Affected by proxy processing, scheduling, and configuration | Lower and more consistent with kernel packet processing |
| Server footprint | General-purpose proxy fleet sized for workload and configuration | About half the servers as a practical evaluation target for pure L4 |
| Tuning | Heavy or specialized deployments may tune connections, file descriptors, threads, CPU mapping, buffers, and reuse | Completely tuning free |
| Kubernetes backends | Requires an integration or configuration automation around the proxy | Native Kubernetes Service and Ready Pod discovery |
| Monitoring | HAProxy statistics and external monitoring ecosystem | Built-in console plus premade Grafana and Datadog configurations through OpenTelemetry |
| Commercial model | Open source, HAProxy Enterprise, or third-party support | Flat negotiated license with commercial support |
Why the fast path wins
One connection. No payload copies.
Conceptually, both products send a connection from a client to a backend. The work in the middle is different. HAProxy relays a stream between a client socket and backend socket. XLB keeps the application data on the kernel packet path and only performs the work needed to select and route the backend.
Lower latency
Less socket, scheduling, and memory-copy work reduces the load balancer’s contribution to connection and packet latency.
More stable at peak traffic
The short kernel path avoids a userspace proxy process becoming a scheduling or buffer-management bottleneck as traffic rises.
Less to operate
XLB combines its focused data path with automatic Kubernetes discovery, built-in visibility, autoscaling metrics, and zero proxy tuning.
Common questions
XLB and HAProxy.
Why is XLB faster than HAProxy for pure Layer 4 traffic?
HAProxy TCP mode accepts a client connection and relays its stream to a backend connection. XLB does not terminate and proxy the application stream. It selects a backend and rewrites packets in the Linux kernel, avoiding userspace payload copies and the work of maintaining two bidirectional sockets.
Does XLB require the same tuning as a large HAProxy fleet?
No. XLB is completely tuning free. There are no application-specific worker, buffer, connection-pool, thread, or proxy settings to benchmark and maintain. Provide the listener and backends, and XLB configures its fast path automatically.
How many XLB servers replace an HAProxy fleet?
For planning, XLB typically targets about half the load-balancer servers for the same pure Layer 4 workload. The evaluation should use your real connection rate, packet size, traffic distribution, and hardware before final sizing.
Can XLB keep up with Kubernetes backend changes?
Yes. XLB can watch a Kubernetes Service, select Ready Pods, and update the local backend pool automatically as Pods scale, roll, or become unavailable. Multiple XLB instances can also autoscale from CPU and network metrics.
Official product references
Competitor details on this page are based on the vendor’s own documentation.
Prove it with your workload
See what your load balancer could cost.
Bring your current topology, traffic profile, or cloud bill. We’ll scope a representative XLB evaluation.