What is Inference Scaling?

Success metrics include system uptime, model performance stability, deployment velocity, and operational cost efficiency.

Scale vertically first by upgrading to faster CPUs/GPUs and optimizing the model. This is simpler and cheaper until you hit hardware limits. Scale horizontally by adding more serving instances when vertical scaling plateaus or when you need fault tolerance. Horizontal scaling adds complexity like load balancing and request routing but offers unlimited ceiling. For most models under 10,000 requests per second, vertical scaling plus basic autoscaling is sufficient and much simpler to manage.

Analyze traffic patterns over 4 weeks to identify peak and trough volumes. Set baseline capacity at average traffic plus 20% buffer. Configure autoscaling to handle peaks with a 2-minute warm-up window. Use spot or preemptible instances for non-latency-critical batch inference, saving 60-80% versus on-demand pricing. Monitor utilization metrics and right-size monthly. Most teams overprovision by 40-60% when they first deploy, so the first optimization pass typically yields significant savings.

Scale on request queue depth rather than CPU usage since CPU-based scaling reacts too slowly for latency-sensitive models. Use custom metrics like p95 latency or pending request count for more responsive scaling. Configure scale-up aggressively and scale-down conservatively to avoid oscillation. Set minimum instances high enough to handle sudden traffic spikes during the scaling lag. Test your scaling configuration under load before relying on it in production.

Scale vertically first by upgrading to faster CPUs/GPUs and optimizing the model. This is simpler and cheaper until you hit hardware limits. Scale horizontally by adding more serving instances when vertical scaling plateaus or when you need fault tolerance. Horizontal scaling adds complexity like load balancing and request routing but offers unlimited ceiling. For most models under 10,000 requests per second, vertical scaling plus basic autoscaling is sufficient and much simpler to manage.

Analyze traffic patterns over 4 weeks to identify peak and trough volumes. Set baseline capacity at average traffic plus 20% buffer. Configure autoscaling to handle peaks with a 2-minute warm-up window. Use spot or preemptible instances for non-latency-critical batch inference, saving 60-80% versus on-demand pricing. Monitor utilization metrics and right-size monthly. Most teams overprovision by 40-60% when they first deploy, so the first optimization pass typically yields significant savings.

Scale on request queue depth rather than CPU usage since CPU-based scaling reacts too slowly for latency-sensitive models. Use custom metrics like p95 latency or pending request count for more responsive scaling. Configure scale-up aggressively and scale-down conservatively to avoid oscillation. Set minimum instances high enough to handle sudden traffic spikes during the scaling lag. Test your scaling configuration under load before relying on it in production.

Scale vertically first by upgrading to faster CPUs/GPUs and optimizing the model. This is simpler and cheaper until you hit hardware limits. Scale horizontally by adding more serving instances when vertical scaling plateaus or when you need fault tolerance. Horizontal scaling adds complexity like load balancing and request routing but offers unlimited ceiling. For most models under 10,000 requests per second, vertical scaling plus basic autoscaling is sufficient and much simpler to manage.

Analyze traffic patterns over 4 weeks to identify peak and trough volumes. Set baseline capacity at average traffic plus 20% buffer. Configure autoscaling to handle peaks with a 2-minute warm-up window. Use spot or preemptible instances for non-latency-critical batch inference, saving 60-80% versus on-demand pricing. Monitor utilization metrics and right-size monthly. Most teams overprovision by 40-60% when they first deploy, so the first optimization pass typically yields significant savings.

Scale on request queue depth rather than CPU usage since CPU-based scaling reacts too slowly for latency-sensitive models. Use custom metrics like p95 latency or pending request count for more responsive scaling. Configure scale-up aggressively and scale-down conservatively to avoid oscillation. Set minimum instances high enough to handle sudden traffic spikes during the scaling lag. Test your scaling configuration under load before relying on it in production.