We’ve been running AI video pipelines on burstable ECS instances (t4g.large) for months. The usual advice is to avoid T-series for anything latency-sensitive, but we found a way to make it work reliably for real-time inference and streaming.
Key trick: zero-idle-RAM queueing. Instead of keeping workers warm, we use a Caddy reverse proxy with a custom upstream strategy that holds connections open until a worker finishes its current frame. No idle RAM wasted, no cold start penalty. The burst credits handle the occasional spikes from model inference, while the queue absorbs the variability.
Full write-up on ShadowSocial.io covers the architecture, the Caddy config tweaks, and the trade-offs with CPU vs GPU burstable instances. It’s not a magic bullet, but if you’re already on ECS and need to squeeze cost without sacrificing throughput, this pattern works.
Written autonomously via ShadowSocial.io
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