Hardware costs are climbing, and so are hyperscaler bills. More streaming teams are doing something they used to skip, like auditing where their cloud spend goes instead of treating the invoice as a fixed cost of doing business.
The audit tends to turn up the same pattern across most video technology vendors. They're paying one price for different types of workloads that belong on different kinds of infrastructure. A cost optimization strategy that leans entirely on a hyperscaler, or entirely on dedicated hardware, misses the workloads that don't fit that model.
The teams making real progress on cloud cost optimization are splitting their infrastructure by how each workload behaves, then pricing each type of behavior in the cheapest way that still holds up. Three levers matter here: bare metal, reserved capacity, and burst pricing. Here's where each one earns its place.
What cloud cost optimization means for a streaming stack
Cloud cost optimization means uncovering idle instances, forgotten snapshots, oversized machines running at 10% utilization. For a streaming platform, the bigger opportunity is mismatch.
Encoding, transcoding, origin, packaging, and CDN infrastructure don't behave like a typical web app's traffic. It runs constantly, near capacity, all year, priced for a flexibility it never uses.
Cloud cost management shows you where the money's going. Cloud cost optimization is what you do with that information. Match each workload to what it costs to run, instead of chasing one blanket discount across the whole bill.
One infrastructure choice doesn't fit a streaming workload
You’ve probably seen the pattern:
- Provision for your best night, and you're paying peak prices for an average Tuesday.
- Provision for your average night, and one live event or viral moment leaves you scrambling.
Egress compounds the problem. You're billed for every stream you deliver out of the cloud, and video is bandwidth-heavy by nature. That single line item can dwarf compute on a video-heavy bill.
We've broken down that baseline-versus-surge cost structure in more detail in How to reduce cloud costs for streaming platforms. Let’s talk about which pricing model fits which kind of workload.
The three levers
Bare metal: for steady, predictable baseline load
Bare metal is dedicated, non-virtualized hardware, so there's no overhead from a hypervisor and no noisy neighbors competing for the same resources. You're paying for physical capacity, not a shared slice with someone else's spike built in.
That makes it the right fit for workloads that run the same way every day: encoding pipelines, transcoding, origin storage, steady-state delivery. Running that kind of load on dedicated bare metal typically costs around 30% less than the equivalent on-demand hyperscaler instance.
The trade-off is provisioning time. Pre-configured builds are ready in around 40 minutes, and custom configurations can take 24 hours or more. That's a fine trade for infrastructure you can forecast months out, and a poor one for a spike you're already inside of.
Reserved capacity: for growth you can see coming
Reserved capacity sits between bare metal and full on-demand pricing. You commit to a set amount of capacity over a defined period, in exchange for a lower rate than paying for the same resources on demand.
This works when you have solid data behind it. A platform that knows its subscriber growth curve, or a live-sports package with a known season, can reserve capacity ahead of need and lock in a better rate than waiting and paying full price later.
However, reserving capacity you don't end up using costs more than never reserving it at all. If your traffic pattern is still forming, this lever isn't ready for you yet.
Burst and on-demand pricing: for spikes you can't predict
On-demand hyperscaler capacity earns its cost when you need more capacity right now, with no lead time, and you can't predict when that need will hit.
A launch, a live event that runs bigger than expected, or a single clip going viral can all justify paying elastic pricing, because not being able to serve your audience during the biggest moment of the year costs more than the premium does.
The mistake is running your entire baseline on burst pricing, every day, for capacity that never spikes.
A quick comparison
| Bare metal | Reserved capacity | Burst / on-demand | |
|---|---|---|---|
| Cost predictability | High, flat and known | High, if usage is forecast correctly | Low, scales with actual usage |
| Best fit | Steady-state: encoding, transcoding, origin, delivery | Forecastable growth: known subscriber curves, scheduled events | Genuine spikes: launches, live events, viral moments |
| Provisioning speed | 40 minutes (pre-configured) to 24+ hours (custom) | Set up ahead of the commitment period | Instant |
| Risk if mis-sized | Underprepared for a real spike | Paying for committed capacity you don't use | Baseline permanently priced like a spike |
| Typical cost profile | Roughly 30% less than equivalent on-demand compute and egress costs | Discounted vs. on-demand, in exchange for commitment | Highest per-unit cost, no commitment required |
Where video technology vendors are landing
The pattern showing up most among the video technology vendors and standalone OTT platforms we work with—companies running encoding, transcoding, origin, packaging, and CDN infrastructure behind multiple OTT clients—looks like a blend of all three levers (bare metal, reserved capacity, and the hyperscaler) rather than a single choice.
Baseline workloads move to bare metal first. Forecastable growth, a new client onboarding or a subscriber curve backed by solid data, gets reserved capacity. Burst pricing stays in reserve for events nobody can fully predict. That combination, dedicated hardware for baseline plus hyperscaler capacity for growth and spikes, is what most people mean by hybrid cloud in a streaming context.
Real-time streaming is a smaller, more active corner of the market on its own, and protocols like MoQ are changing what "burst" means for that use case. That's a separate topic, but it's one more reason the three-lever approach needs to flex by workload type, not just by platform.
Getting started with cloud cost optimization
Cloud cost optimization for a streaming platform means matching bare metal, reserved capacity, and burst pricing to how each workload behaves, so you pay steady prices for steady work and spike prices when there's a real spike.
The simplest place to start is your own traffic data. Pull the last three to six months and find your baseline. That number shows how much of your infrastructure is predictable, and predictable load is the only load worth moving off on-demand pricing.
Once you know your baseline, see what running it on dedicated infrastructure would cost. Take a look at what servers.com offers streaming platforms and video technology vendors, built around the encoding, origin, and delivery workloads that make up most of a streaming stack's steady-state spend.
PS, the servers.com team and I will be having this same conversation with people at IBC2026 this September. If you plan to be there, come find us at booth 5.A81 for a coffee and a chat!
Cloud cost optimization FAQs
Is reserved capacity the same as bare metal?
No. Reserved capacity is a pricing commitment, usually on hyperscaler infrastructure. Bare metal is a different kind of infrastructure entirely: dedicated, non-virtualized hardware. You can use either on its own, or combine them.
Do I have to pick one lever exclusively?
No. Most of the platforms getting this right blend all three, based on how each workload behaves.
How fast can I get dedicated capacity running?
Pre-configured builds are ready in around 40 minutes. Custom hardware configurations take longer, plan for up to 24 hours or more.
What's the cost lever most teams overlook first?
Egress. It's rarely the first thing teams look at, but for video-heavy workloads, it's often the biggest line item on the bill.
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