Tracking performance, reliability and cost under unpredictable traffic is a problem you cannot ignore. Therefore, you need a control plane that not only spreads traffic safely but also aligns capacity to demand. And for that, you will have to understand how autoscaling and load balancers work.
- Load balancers distribute requests to healthy backends, so no single node becomes a bottleneck.
- Autoscaling adjusts the amount of compute, so your environment tracks real workload instead of a guess. Moreover, the business risk is concrete.
ITIC’s recent report claims that 90% of firms say one hour of downtime costs $300,000, with 41% estimating $1–5 million per hour. Such scenarios make it important to conduct upfront engineering by understanding the differences between load balancing and autoscaling.
What is Load Balancing (Cloud Computing)?
A load balancer sits in front of a target group and distributes requests using algorithms such as round robin or least outstanding requests, while continuously removing unhealthy targets from rotation.
For example, AWS Application Load Balancer (ALB) distributes HTTP/HTTPS requests and supports round-robin and least-outstanding-requests algorithms; optional cookie stickiness can pin sessions at L7.
A Network Load Balancer (NLB) operates at Layer 4 and uses 5-tuple flow-hash strategy, so all packets in a connection reach the same target. As a result, it is effective for TCP-heavy or long-lived connections.
Elastic Load Balancing (ELB)-managed load balancers scale themselves and can shard for extreme spikes, handling millions of requests per second with low latency.
Cross-zone load distribution further evens load across Availability Zones to improve resilience when one zone is pressured. It is alwas on for ALB and is configurable for NLB (now no extra data charge in many regions). You will have to enable it to smooth zonal skew.
How do modern load balancers work?
- Health checks run per target group and routing algorithms decide which healthy target receives each request.
- Application Load Balancer (ALB) commonly uses round robin or least outstanding requests while LB uses flow hashing per connection.
- Zone-aware spreading via cross-zone load balancing helps absorb zonal imbalance without overprovisioning.
- Managed LBs scale control-plane capacity automatically and can be sharded for unusual surge events.
What is Autoscaling (Compute)?
Autoscaling adds or removes instances or pods according to policies tied to metrics like CPU, request count per target, queue depth or custom SLO signals.
All major clouds support metric-based and schedule-based approaches, which lets you pre-provision for predictable peaks while reacting to real telemetry.
In this, target tracking holds a metric near a value, while step policies change capacity in configured increments.
How does autoscaling work in cloud computing?
- Autoscaling observes CPU, memory, ALB RequestCountPerTarget, queue depth or custom Monitoring metrics. Target tracking maintains a metric near a setpoint while step or predictive policies scale by configured steps or forecasts.
- Guardrails like min and max capacity, instance warm-up and cooldowns prevent oscillation. You can schedule fixed capacity for launches or sales. For example, on Google Cloud, a managed instance group supports up to 128 scaling schedules.
- Favor horizontal scaling for stateless tiers and reserve vertical scaling for constrained or stateful services. Coordinate with LB health checks and connection draining so traffic shifts only after instances become ready.
How does autoscaling decide when and how far to scale?
- Before rollout, you will have to align triggers and bounds with business SLOs. Common triggers include CPU, ALB request count per target, queue depth, custom metrics and schedules.
- Target tracking keeps a metric near a target, while step and predictive scaling change capacity by pre-set increments or forecasted demand. Cooldowns and warm-up windows reduce thrash during rapid shifts.
What are the differences between Load Balancing and Autoscaling?
Load balancing decides where each request goes at that instant, keeping traffic on healthy targets and smoothing localized hotspots. Whereas, Autoscaling decides how much capacity to run by adjusting instances or pod counts based on metrics and schedules.
These mechanisms operate on different control loops, since load balancing reacts in milliseconds while autoscaling changes apply over minutes. Health checks belong to the balancer, which gates traffic away from failing nodes, whereas autoscaling replaces impaired nodes to restore redundancy.
| Decision area | Load balancing | Autoscaling |
|---|---|---|
| Primary decision | Decides where each request should be sent at that instant. | Decides how much capacity should run to meet demand. |
| Scope of control | Governs traffic distribution across only healthy targets. | Governs the population of targets by adding or removing instances. |
| Decision timing | Reacts per connection or request within milliseconds. | Reacts over tens of seconds to minutes (cooldowns, warm-ups, safety bounds). |
| Health model | Uses active or passive health checks to gate traffic. | Replaces unhealthy nodes (via ASG/VMSS/MIG health) and scales population on metric breaches. |
| Capacity effect | Never creates capacity, only reallocates demand across targets. | Explicitly adds or removes capacity to match sustained load. |
| Performance impact | Smooths hot spots immediately and limits queue buildup. | Prevents sustained latency growth by right-sizing the fleet. |
| Failure isolation | Quickly removes unhealthy targets from rotation. | Launches replacements to restore headroom and resilience. |
| Integration in practice | Registers healthy instances and begins routing after checks pass. | Boots instances, then relies on readiness before traffic arrives. |
| Scale-in safety | Drains in-flight requests during deregistration. | Initiates termination after draining completes and guardrails allow. |
| Metric bridge | Operates on per-request outcomes and target health. | Scales on CPU, request count per target or custom SLO metrics. |
| Workload fit | Best for any online tier needing zonal high availability. | Best for spiky or seasonal tiers with variable demand. |
| State handling | Can honor sticky sessions (L7 cookies/L4 source) but does not manage state. Externalize sessions for safe scale-in. | Requires externalized sessions and caches for safe scale-in. |
| Cost posture | Improves utilization of existing nodes only. | Removes idle nodes during troughs to trim spend. |
| Operational knobs | Tune algorithms, health checks and cross-zone distribution. | Tune min and max capacity, cooldowns and step sizes. |
| Observability | Focus on distribution skew, 4xx and 5xx rates and target saturation. | Focus on utilization, queue depth, SLOs and scale events. |
| Example under surge | Routes only to healthy targets and evens zonal pressure. | Adds nodes until per-target load returns to the target. |
| Example during recovery | Maintains stable latency as demand falls. | Scales in gradually to the defined minimum. |
| Ownership pattern | Platform team typically owns configuration and guardrails. | Product and capacity owners define policies aligned to SLOs and cost. |
A balancer never creates compute, it only redistributes demand. However, sustained load requires autoscaling to right-size the fleet and hold latency. During growth, new instances register with target groups and receive traffic after health checks, warm-ups and readiness pass.
During scale-in, autoscaling deregisters targets so the balancer drains connections and prevents resets. Use load balancing for online tiers needing zonal availability and autoscaling for spiky or seasonal demand.
Our recommendation:
- Externalize sessions and caches so scaling does not depend on stickiness, then set bounds, cooldowns and step sizes to avoid thrash.
- Together they keep users protected during surges while trimming idle spend during lulls, which aligns performance, reliability and cost.
When to Use Load Balancing or Autoscaling or Both?
If you are planning to decide between the two or trying to use both the methods, match the mechanism to the workload pattern you observe.
- Use load balancing only for steady-state services that need high availability across zones where capacity is fixed and predictable.
- Use autoscaling only for batch or internal pull-queue jobs that do not accept inbound requests through a balancer.
- Use both for spiky web, APIs, mobile commerce, events and launches where inbound demand is bursty.
Common Pitfalls in Load Balancing and Autoscaling
You must plan for operational edge cases so scaling protects SLOs, not undermines them during peaks and recoveries.
1. Cold starts and warm-ups
New capacity often starts cold, which produces latency spikes under bursty traffic. We suggest you use readiness probes, ALB slow start, instance warm-up and small canary batches before full traffic.
2. Sticky sessions and state
Session affinity traps users on instances that may scale in unexpectedly. Therefore, you should externalize sessions and caches, then prefer stateless handlers so scale-in and zone shifts remain safe.
3. Aggressive scale-in and thundering herd
Rapid removals force retries that overload remaining targets. Hence, configure deregistration delay and connection draining, cap step-down size and apply conservative cooldowns to prevent oscillation.
4. Health checks and replacement
Overly strict or short health checks cause flapping and needless replacements. You should align thresholds with application startup, enable ELB health checks in the Auto Scaling group and set a healthy grace period.
5. Policy ping-pong
Unpaired rules on different metrics create contradictory actions. We advise you to pair scale-out and scale-in on the same metric with hysteresis, distinct thresholds and adequate cooldowns to stabilize control loops.
6. Zonal imbalance and capacity holes
Traffic or capacity can skew by zone during incidents or sales. Therefore, enable cross-zone distribution, set per-zone minimums and monitor imbalance so scaling restores even headroom.
7. Work draining and background jobs
Instances often run consumers or scheduled tasks that outlive HTTP requests. Put the workers on lifecycle hooks, ensure idempotency and hand off unfinished work to queues before termination.
8. Observability and drills
Blind scaling hides root causes behind averages. Make sure you track per-target load, p95 latency, 5xx rates and scale events, then rehearse game-day scenarios to validate warm-up and drain behavior.
Load Balancing vs Autoscaling: Key Takeaway
Load balancing decides where to send traffic and shields users from unhealthy targets, while autoscaling decides how much capacity to run to hold latency within SLO. Together they protect experience and budget when demand is volatile.
With AceCloud you can pair multi-zone networking, managed Kubernetes and a 99.99%* SLA to keep services reachable while scaling predictably. You can front workloads with Kubernetes ingress and service load balancing while enabling health checks and connection draining.
Then apply horizontal pod autoscaling and node autoscaling so capacity tracks real demand. Moreover, you can mix on-demand and Spot capacity to reduce expenditure without sacrificing resilience.
What are you waiting for? Connect with our friendly cloud experts today and make the most of free consultations!
Frequently Asked Questions:
No. The load balancer distributes traffic to healthy targets, while autoscaling adjusts instance or pod count to match demand.
Stand up the load balancer and health checks, then attach autoscaling so new capacity is registered and unhealthy capacity is replaced.
Yes. Elastic Load Balancing scales automatically to the vast majority of workloads without manual intervention.
CPU, ALB RequestCountPerTarget, queue depth, p95 latency via custom monitoring metrics or GCP load balancer serving capacity on MIGs are common and effective choices.
About 69% of 2024 holiday orders were mobile. Set up autoscaling by scaling on p95 latency/RPS, pre-scaling min capacity, warming instances and ensuring DB/cache headroom. For load balancing, you can use latency-aware/least-requests, edge termination and keep-alive, slow-start, cross-zone and avoid stickiness (or use a shared store).
