At a glance:
- Dependency mapping almost always turns up hyperscaler-managed service integrations than the architecture diagram shows, and each one needs a decision: refactor, rebuild, or leave in place for now.
- Both environments run live during the parallel period, splitting real bid traffic, which surfaces bid-stream and exchange-level issues that load testing alone never catches.
- The cutover decision comes down to one number: whether win rates on bare metal hold where the traffic split says they should. If they don't, that gets investigated before anything moves.
- Cutover is a routing change, not a maintenance window, and your hyperscaler stays live in warm standby for 48 to 72 hours as the rollback path.
- The first month is spent rebuilding operational muscle memory, runbooks, dashboards, incident patterns, that hyperscaler-specific tuning built up over years, not chasing lost performance.
What migrating a DSP bidding pipeline to Servers.com bare metal involves
Once you've decided to move your bidding pipeline to Servers.com by Nexcess bare metal, the comparison documents get set aside and the operational work begins.
Weeks 1 to 2: Architecture mapping and the first surprise
Before any servers are provisioned, the first task is a dependency inventory: a full account of every component in the bidding infrastructure that touches a hyperscaler-managed service. If you've been on one for several years, this is usually where the first surprise shows up. The dependency list is almost always longer than the architecture diagram would have you think.
The integrations that surface are usually not the ones anyone designed as permanent coupling. They became permanent through convenience: CloudWatch alarms wired into on-call paging, Kinesis streams feeding the bid event pipeline, or S3 lifecycle policies handling bid log archival.
None of them stops the migration, but each one needs a decision: refactor before the move, rebuild in our environment, or leave it while the bidding compute moves to bare metal.
Our solution architects work through that inventory with your team during the pre-migration period. Plan more time for this phase than you expect: platforms that have operated on a hyperscaler for three years or more regularly have integrations that aren't in any current diagram.
By the end of this phase, you know what moves first, what can move in parallel, and what stays on your previous provider a little longer while the core bidding path establishes its baseline on bare metal.
Weeks 2 to 4: Parallel infrastructure and the baseline test
This is usually the busiest part of the migration. The core bidding path goes live on Servers.com by Nexcess bare metal while the hyperscaler environment continues to serve full production traffic. Both environments handle a split of live bid requests, giving your team a direct performance comparison under real auction conditions instead of simulated load.
Load tests cover volume but not the specific bid-stream characteristics, data pipeline timing, and exchange-level behavior that real traffic involves. That's why the first week of parallel running almost always turns something up that pre-production testing missed.
Our adtech infrastructure team is actively engaged during this period. When the issue is infrastructure-layer behavior (network path optimization, CPU scheduling configuration, memory allocation tuning), our engineers work on the problem directly.
That's one place dedicated bare metal is simpler: infrastructure issues stay with us and application issues stay with your team, a split that's often harder to draw on shared cloud infrastructure, because application symptoms can start with contention your team can't see.
Everyone would like a longer parallel-running period. It gives your team more time to validate performance under live traffic. The tradeoff is that you're paying to run two environments at once. For a moderately complex bidding pipeline, three to four weeks is the right balance.
Week 4 to 5: Win rate validation before cutover
This is the go/no-go point for the migration. The biggest question is whether win rates on the bare metal environment hold where you'd expect them to.
By the end of the parallel period, our environment typically shows lower p99 variance than the hyperscaler baseline. The dedicated node configuration gets rid of co-tenant resource contention, and the network path through our adtech-optimized backbone produces more consistent latency instead of the time-of-day shifts you can see on shared infrastructure.
If the numbers line up, the cutover moves forward. If win rates drift in a way the traffic split doesn't explain, that's the conversation you have before anything changes, and our account team and solution architects are part of that investigation.
The cutover isn't a maintenance window. It's a traffic routing change that shifts all bid requests to the bare metal environment while the hyperscaler stays in warm standby as a rollback option for the next 48 to 72 hours.
Week 5 to 6: First-month operations on dedicated infrastructure
The first 30 days are about rebuilding the operational routines your shared cloud environment accumulated over the years: runbooks, monitoring dashboards, and incident response patterns that don't transfer directly to the new infrastructure.
Our support looks a little different during the first month. Dedicated account management includes proactive infrastructure monitoring, direct responses to performance questions, and coordination on the configuration adjustments that any new production environment generates in the first weeks.
If you've spent years tuning for your hyperscaler’s specific behavior, you probably have assumptions about bottlenecks and scaling thresholds that don't apply cleanly to a bare metal environment, and some of those need to be rebuilt from observing the new infrastructure.
The baseline win rate improvement your migration was designed to produce is typically visible within two to three weeks of full production. Getting the full value out of dedicated infrastructure takes longer than that, but it's a curve that keeps climbing, instead of flattening out the way a hyperscaler environment does once you've optimized everything auto-scaling lets you touch.
