At a glance:
- p99 latency looks similar at baseline on both platforms, but shared tenancy on hyperscaler compute lets the tail drift in ways dedicated bare metal doesn't.
- Hyperscaler compute wins on cost at low bid volumes; the crossover point shifts toward bare metal as data movement and support costs scale with volume.
- Incident accountability is cleaner on dedicated infrastructure. Hyperscaler does offer a path to more isolated tenancy, just at a real cost premium over standard compute.
- Architectural portability comes down to how much of the platform depends on cloud-specific managed services, not which infrastructure category it runs on.
Hyperscaler compute vs. dedicated bare metal for real-time bidding
Four questions usually dominate this comparison: what happens to latency under real auction load, how costs change as bid volume grows, who owns what when something breaks, and how hard it would be to change direction later.
Latency consistency under sustained and peak RTB load
Hyperscaler
At baseline load on c6i or similar compute-optimized instances, you're typically looking at p50 bid response latency in the 2-5ms range for in-process bid computation, with p99 in the 6-15ms range depending on instance utilization.
Shared physical resources come under contention from co-tenant workloads, and average latency won't tell you how much p99 will move as that contention shifts with time-of-day load patterns.
RTB exchanges enforce timeout thresholds of 100ms or less, so hitting your p50 target doesn't guarantee every bid makes the auction, especially during a peak event when it's the tail that matters.
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On our dedicated nodes, p99 doesn't show the same variance under load, since physical CPU and memory aren't shared with other tenants, and the p50-p99 gap stays much smaller under sustained high QPS.
Net-new capacity isn't something you spin up reactively like hyperscaler auto-scaling. Our team works with you ahead of a known peak or growth curve to provision what you'll need before you need it, so planning happens on your timeline, not during an incident.
If p99 is contractual or partner-facing, dedicated bare metal gives a more predictable latency profile; if the bigger concern is reacting to unpredictable spikes, hyperscaler compute has the advantage.
Cost-per-bid behavior at three volume thresholds
Hyperscaler
At low bid volumes, reserved compute is usually the more cost-efficient option. As volume grows, three costs get harder to ignore: inter-AZ data egress, NAT gateway processing, and the warm pool capacity needed to keep p99 under control during surges.
NAT gateway processing produces a gap between the compute invoice and the workload's real cost, especially on multi-region architectures with heavy inter-AZ movement.
Enterprise support adds to this: priced as a percentage of spend, it grows with the platform and can run into hundreds of thousands of dollars annually for large RTB workloads.
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On dedicated bare metal, the cost structure is more predictable: node count and bandwidth are the main drivers. Data movement is billed per gigabyte, or per terabyte at the volumes many adtech customers run at, and neither stacks up like inter-AZ traffic does on hyperscaler platforms. Support is part of the account model, not a separate spend-based tier.
At low volumes, the fixed cost is harder to justify against light or variable on-demand usage. At sustained high volumes, lower data movement costs keep cost per bid steadier.
Where the crossover sits depends on the architecture, the traffic profile, and how much data the platform moves.
Operational accountability during a latency incident
Hyperscaler
During a latency incident, accountability is divided: the hyperscaler owns the physical layer and shared virtualization infrastructure, the platform team owns everything at and above the OS.
If the problem is co-tenant contention or network congestion, the platform team has limited visibility into what's happening on the host. There's a direct fix, moving to dedicated-tenancy hardware, but it costs a real premium over standard on-demand compute and isn't the default. Absent that, you're working through support tiers on timelines that may not match a live auction's urgency.
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On dedicated infrastructure, the boundary is cleaner: hardware, network, and facility issues are ours to resolve, application-layer issues belong to the platform team.
During a live incident, the response is our adtech-focused support team, not general-purpose cloud support; our engineers know what auction timeout dynamics look like, how p99 behaves on dedicated nodes, and where to start when latency spikes.
That division still requires coordination, but clear boundaries mean both teams start investigating immediately instead of figuring out who owns what.
Architectural portability and switching cost
Hyperscaler
The longer a platform runs on a hyperscaler, the more likely it's picked up cloud-specific integrations: managed Kubernetes, Lambda functions in the bid pipeline, proprietary monitoring, cloud-native replication. Each one makes the next migration more involved, since moving later means migrating the workload and everything built around it.
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Application workloads on our bare metal don't carry cloud-specific integrations by default. The platform team controls the OS image, application build, and tooling, so migration is about moving the workload and managing the parallel-running period, not untangling proprietary integrations.
For containerized workloads, that migration is lighter than it sounds: our managed Kubernetes service deploys on bare metal, so a platform already on Kubernetes is moving an orchestration layer it already knows, not adopting a new one.
The catch: managed services beyond Kubernetes, built heavily around AWS or GCP, still have to be refactored before the workload can move.
How hard it is to move later depends less on the infrastructure itself than on how deeply the platform has been built around cloud-specific services.
