Systems rarely fail because of a single request.

They fail because of too many.

A product launch drives traffic beyond expectations. A botnet sends bursts of requests. A client integration misbehaves and floods an API with retries.

Resources are finite. CPU, memory, thread pools, connection pools — all have limits. When requests arrive faster than they can be processed, latency increases. Queues fill. Timeouts multiply.

Left unchecked, the system exhausts itself.

Rate limiting and throttling introduce boundaries around consumption.

Controlling the Flow

Rate limiting restricts how many requests are allowed within a given window of time.

The limit may apply per user, per API key, per IP address, or globally across the system.

Instead of allowing unbounded traffic, the system measures and enforces a maximum rate.

When the limit is reached, additional requests are rejected or delayed.

The effect is not to eliminate demand, but to smooth it.

Protecting Shared Resources

Without limits, burst traffic can overwhelm shared infrastructure.

A sudden spike in requests can exhaust thread pools, saturate databases, and cascade into failures across services.

With rate limiting in place, excess traffic is contained at the edge.

Some requests receive a clear rejection, often with a 429 “Too Many Requests” response. The rest proceed within sustainable bounds.

The system remains responsive for the majority rather than collapsing for all.

Fairness and Tradeoffs

Rate limiting requires decisions about fairness.

Should every user have the same allowance?
Should premium users receive higher limits?
Should limits be global or partitioned by tenant?

There is also a tradeoff in user experience. Legitimate requests may be rejected during bursts. Clients must handle throttling gracefully, often with their own backoff logic.

Yet the alternative — complete resource exhaustion — is worse.

Limiting traffic is an explicit choice to preserve availability.

Techniques and Implementation

Common algorithms include token buckets, leaky buckets, and fixed or sliding windows. Each provides a different balance between precision and simplicity.

The underlying principle is consistent: track request counts over time and enforce boundaries.

In distributed systems, these limits may be enforced at API gateways, load balancers, or within application code.

When It Matters Most

Rate limiting is particularly important for public APIs, multi-tenant systems, and services exposed to unpredictable traffic patterns.

Internal services can benefit as well, especially when upstream components might misbehave under failure conditions.

Throttling is not only about malicious traffic. It is about protecting the system from itself.

A Deliberate Constraint

Unlimited throughput sounds attractive.

In practice, it is unsustainable.

Rate limiting acknowledges that capacity is finite. It enforces discipline at the boundary.

By constraining request flow, the system preserves its ability to serve.

Sometimes resilience comes from saying no.