DevOps Consulting for Media & Streaming
Streaming infrastructure is judged in real time by an audience that will not wait for a retry. A live event compresses a day of traffic into ten minutes; a transcoding backlog delays every release behind it; and between CDN egress, storage and encoding compute, media platforms carry some of the ugliest unit economics in software. The work is classic platform engineering with the volume turned up: elasticity measured in seconds, pipelines measured in throughput, and cost measured per stream.
Live events spike traffic faster than autoscaling reacts
Kickoff hits, concurrent viewers multiply within minutes, and reactive autoscaling is still booting nodes while origins saturate — the audience meets a spinner at the exact moment that matters most.
How we fix it — Pre-scaling for the schedule you already know, autoscaling on leading signals like request rate and stream starts rather than lagging CPU, origin shielding so the CDN absorbs the surge, and load tests shaped like a kickoff — a vertical wall, not a gentle ramp.
The transcoding pipeline is either backlogged or burning money
Encoding fleets sized for the big ingest sit idle between releases; sized for the average, a large drop delays every title behind it. Either way somebody pays — in compute or in time-to-publish.
How we fix it — Queue-driven elasticity: transcoding workers autoscale on queue depth, run on spot capacity with checkpoint-and-retry so interruptions cost seconds, and scale to zero when the queue empties. Throughput follows the work; spend follows the throughput.
Egress and CDN costs dominate the bill and nobody owns them
Delivery cost scales with every viewer, cache misses silently multiply origin egress, and the invoice arrives as one opaque number no engineer can act on.
How we fix it — Cache-hit ratio engineered and monitored as a first-class metric, origin shield and tiered caching to cut origin egress, storage classes matched to access patterns, and per-title, per-channel cost attribution — delivery cost becomes a curve you manage, not a bill you dread.
You learn about playback failures from social media
Infrastructure dashboards show green while startup times crawl and rebuffering climbs — because nothing measures what the viewer experiences, only what the servers report.
How we fix it — Viewer-facing telemetry wired into the same observability stack as the platform: stream-start success, startup latency, rebuffer and error rates, alerted on as SLOs. When quality degrades, engineering knows before the audience posts about it.
Engineered for the minutes everyone shows up.
Averages are meaningless in streaming — the platform is judged at peak concurrency and the economics are judged per stream. We build for both ends: elasticity that meets the spike, and unit costs that survive the growth.
- Event-day readinessLoad tests shaped like a premiere — vertical ramp, sustained concurrency — run before the event, with a rehearsed scale plan and runbook.
- Origin protectionShielding, tiered caching and request coalescing so a million viewers translate into a trickle of origin traffic.
- QoE as an SLOStartup time, rebuffer ratio and stream-start success monitored and alerted on — the metrics viewers actually feel.
- Cost per streamEgress, encoding and storage attributed per title and channel, so growth decisions come with a unit cost attached.
Two free tools, no signup: estimate your cloud waste with the cost calculator, or score your production posture on the security scorecard. Fixed-scope packages and prices are on the pricing page.
Frequently asked
Can you get us ready for a big live event?
Yes — that is the most common streaming engagement. We load-test at the concurrency you expect with the ramp shape of a live audience, fix the bottlenecks it exposes (usually origin capacity, connection limits and autoscaling lag), set pre-scaling for the schedule, and can be on hand during the event itself if you want a second pair of eyes.
We already use a CDN. Why is egress still killing us?
Usually cache-hit ratio: misconfigured cache keys, short TTLs, uncacheable headers and no origin shield mean the CDN forwards far more traffic to your origin than it should — and you pay egress on all of it. Measuring and engineering the hit ratio is often the single largest cost fix on a media stack.
Is multi-CDN worth the complexity?
At scale, often — for regional performance and failover leverage during events. But most platforms have cheaper wins first: cache-hit ratio, origin shielding and storage tiering. We help you sequence it so multi-CDN arrives when the traffic justifies the operational cost, not before.
We use managed media services like MediaConvert or Mux. What is left for DevOps?
Everything around them: the APIs, auth, catalog and recommendation services, the ingest pipelines, the Kubernetes platform they hang off, and the observability and cost attribution the managed layer does not give you. Managed encoding removes one hard problem; the platform around it is still yours.
Hold the stream when the whole audience arrives.
A free audit of your streaming stack — origin resilience, autoscaling behaviour, cache-hit ratio and cost per stream — with a prioritized fix list, before your next big event finds the gaps.
NO SIGNUP · NO OBLIGATION · REPORT IS YOURS TO KEEP