Streaming infrastructure complexity rarely arrives all at once. Platforms often begin with smaller content catalogues, predictable traffic patterns and limited geographical reach, making early workloads steady and more manageable. Coupled with the possibility of hyperscale cloud free credits, it’s easy to see how teams can move quickly without feeling immediate pressure.
But as the business matures - and as free credits dissipate - their infrastructure needs to keep pace with a very different reality. Growth introduces variables such as traffic volatility and regional expansion, yet in many cases, delivery, encoding, and storage strategies remain structurally unchanged.
Without alignment between workload behavior and infrastructure design, overprovisioning becomes the default safety mechanism. Capacity is added to avoid performance risk, rather than placed intentionally. And it’s not until teams grow that this strain becomes too big to ignore - 78% of business leaders estimate that 20% to 50% of their cloud budget goes to waste.
The issue isn’t limited to hyperscale cloud. Any environment where all workloads are treated identically - whether public cloud, on-premise, or colocation - will struggle under the burst-driven demands of modern streaming.
Optimization means aligning your architecture to three structural pressures that determine both performance and cost.
The three structural pressures of streaming at scale
As platforms mature, strain shows up across the system. Performance expectations increase, cost behavior becomes harder to forecast, and governance requirements grow more complex as regions and audiences expand. These pressures shape how infrastructure performs, scales, and how sustainable it becomes over time.
Performance pressure
The more users your streaming platform attracts, the more resource is needed to keep up with the demand. Where it starts to get more complex is balancing sustained throughput with burst viewership.
During live events or major releases, traffic can surge within minutes. As audiences grow, so does the sensitivity to delay, bitrate fluctuation and session instability. Infrastructure that once handled steady traffic comfortably must now support sustained throughput alongside instant surges. Suddenly, you must handle shifts in demand without degrading playback quality or increasing latency, or you risk interrupting the viewing experience for the end user.
Cost pressure
Rising traffic means rising costs. Egress volumes increase, more storage is required, and encoding requirements diversify across formats and resolutions.
Growth ultimately determines how much you need to spend. A baseline level of usership always remains, but as that baseline climbs, peak demand rises with it. The higher the floor, the sharper and more expensive the spikes become. Environments optimized for flexibility are protected against sudden bursts, but that protection carries a premium. Environments designed purely around baseline demand may offer predictable costs, but costs will unpredictably escalate when traffic surges.
Without deliberate workload alignment, inefficiency grows quietly alongside the platform, causing margins to tighten even when performance appears stable.
Governance pressure
Each new market you grow into introduces a different regulatory nuance. It is this level of expansion where infrastructure decisions begin intersecting directly with legal and contractual frameworks; data may need to remain within specific jurisdictions, live sports rights might restrict where redundancy back-up streams are located, and even simple decisions - like which CDN to use in a region - can be shaped by contractual terms.
What begins as a checklist item gradually influences architecture. Governance shapes where content lives, how it moves, and which environments are appropriate. As scale increases, designing with these constraints in mind prevents friction later.
Not all infrastructure is created equal
To withstand these three streaming pressures as you grow, platforms must tailor their environments to suit their workloads. If not, you are likely to face one – or more – of the following drawbacks:
| Drawback |
What this is |
Its impact on streaming platforms |
| Cost volatility |
Traffic spikes and steady baseline workloads are billed under the same commercial model, regardless of how predictable or unpredictable they are. |
Monthly infrastructure spend becomes difficult to forecast. Major events or seasonal peaks create sudden cost surges, making financial planning reactive rather than controlled. |
| Over-provisioning |
Capacity is permanently allocated to protect against worst-case peaks, even when standard usage is significantly lower. |
Platforms pay for headroom that sits idle most of the year. Margins erode quietly as these buffers become embedded in the operating cost. |
| Performance interference |
Different workload types - from live streaming to analytics and encoding - compete for the same compute, storage, or network resources. |
Latency-sensitive streams can experience instability during background processing or traffic surges, affecting viewer experience at critical moments. |
| Vendor lock-in |
As more services, data, and delivery logic are built into a single provider or architecture, switching becomes progressively harder – particularly with hyperscale cloud. |
Migration risk increases as strategic flexibility narrows, limiting the platform’s ability to optimize cost or expand into new regions efficiently. |
Unfortunately, the solution isn’t as simple as ‘optimizing harder’. Adding more monitoring or bringing in FinOps oversight can improve visibility, but it rarely changes the underlying structure. When fundamentally different workloads are forced into the same environment, optimization becomes incremental while the pressure continues to climb.
You need structural alignment
Finding your perfect infrastructure setup doesn’t have to involve pitting one option up against the other. It’s not hyperscale cloud vs bare metal. As we’ve seen, both have their positives, but equally, both have their negatives when used incorrectly. This binary outlook is where many teams slip up, not aware of the alternatives that are found by utilizing both.
A hybrid infrastructure for streaming does just that.
Your sustained, always-on baseline needs to sit on infrastructure designed for consistency. If you can clearly identify predictable demand, there’s no reason why the cost and performance of that demand shouldn’t be predictable, too. That’s where bare metal excels.
Tailored to your requirements, you can customize the specifications of bare metal hardware including storage, RAM and network, so that your performance is stable and with no wasted spend.
At the same time, burst-heavy or experimental workloads can retain their elasticity with cloud, scaling out only when needed without permanently inflating baseline spend. Now, different workload behaviors operate under different cost models: peaks no longer distort the economics of steady traffic, and steady traffic no longer carries the premium of flexibility.
This structural shift restores clarity. Spend aligns with behavior, making for far more accurate forecasting, and margins that once leaked through architectural compromise become visible and controllable. Not only this, but separating workloads in this way also reduces dependence on any single provider, helping platforms avoid vendor lock-in without undertaking disruptive migrations.
By placing workloads according to how they actually behave - steady where stability matters, elastic where flexibility is required - you can design your infrastructure around streaming pressures, instead of reacting to them. It’s a model used by platforms worldwide that balances performance, cost and compliance, without forcing trade-offs between them.
