Processing bid requests in milliseconds is not an easy task, and sticking to the public cloud may not be the smartest strategy for companies that need processing power, stability, and reliability. 

This article goes over the line of thought that leads to what should be an obvious answer: dedicated servers are the way to go for AdTech platforms.

AdTech Platforms Need Blazing-Fast Infrastructure

AdTech platforms operate in one of the most demanding real-time environments out there. At the core is Real-Time Bidding (RTB), where exchanges typically expect end-to-end responses within about 100 milliseconds.

Once you subtract network transit and fan-out/fan-in overhead, a DSP often has roughly 50–80 milliseconds to parse the request, fetch features, price, and respond. If you’re even ~50 milliseconds too slow, you lose that impression to a faster bidder.

Speed isn’t the only thing that matters. AdTech platforms also need rock-solid reliability and strong security. These systems handle sensitive user data and process millions of dollars in transactions daily. They need to stay online 24/7 and scale smoothly when traffic spikes during big events or holiday shopping seasons.

The technical side is complex. AdTech stacks continuously process huge amounts of data: tracking user behavior, analyzing bid patterns, segmenting audiences, and measuring campaign performance, much of it in real time. That creates infrastructure demands that can push generic cloud environments to their limits.

Why Dedicated Servers Are Perfect for AdTech

Dedicated servers (also called bare metal) solve several key problems that AdTech platforms face every day.

No More Sharing Resources with Other Companies

Virtualized public-cloud servers can suffer from performance variability due to noisy neighbors and shared I/O. Dedicated servers give you consistent, predictable performance, which is exactly what AdTech needs. 

Your bid processing never competes with another tenant’s workload for CPU time, memory bandwidth, or network headroom. In a system measured in milliseconds (and components in microseconds), that consistency directly impacts win rates and revenue.

Exclusive access to high-performance hardware also means you can keep larger working sets in memory for instant decisions, run complex algorithms to optimize bids, and handle thousands of requests per second without avoidable jitter.

Complete Control Over Your Setup

AdTech platforms often need configurations that aren’t possible, or are awkward, on standardized cloud instances. With dedicated servers, your team can optimize from the ground up:

• Tune kernel and network stack parameters for the lowest possible latency.

• Shape storage hierarchies for high-throughput ingest and durable logging

• Deploy specialized monitoring and on-host profiling to track tail latency in real time.

You can fine-tune network settings to achieve the lowest possible latency, set up storage systems to handle massive amounts of data, and install specialized monitoring tools to track performance in real-time.

This control extends to security and compliance: you can enforce custom encryption at rest/in transit, deploy specialized firewalls, and build private networks aligned to your specific constraints rather than a provider’s lowest common denominator.

Powerful Hardware for Heavy Workloads

Modern dedicated servers ship with high-frequency CPUs, large RAM footprints (commonly 256 GB–1 TB+), NVMe SSDs, and 10/25/50/100 GbE network options. That’s a great fit for AdTech workloads that must process data quickly, analyze performance in real time, and handle intense bid fan-out simultaneously.

What are the Hardware Requirements for AdTech Workloads?

DSP Infrastructure: Built for Billions

Running a modern DSP means handling huge QPS at tight SLOs. At peak, leading platforms process massive volumes with sub-100 ms decision times. Typical production guidance:

• CPU: High-frequency multi-core processors; favor fast per-core performance.

  • 24–64 cores is a realistic approach for large-scale operations

  • Prioritize higher clocks over sheer core counts

• Memory: 256–512 GB RAM per server

  • Critical for maintaining user profiles and campaign data in memory

  • Pre-generated features for apps and users are stored in low-latency key-value storage

• Network: Plan for ≥10–25 GbE per host (or higher) and multiple NICs

  • 1 GbE is a bottleneck on the modern stack

  • Multiple network interfaces for redundancy

• Storage: 1–2 TB of NVMe SSDs for warm data, features at rest, and telemetry

  • NVMe handles the warm sets and logs

  • Keep the bidding hot path in RAM/low-latency K/V stores

SSP Infrastructure: Managing the Supply Side

SSPs manage billions of impressions daily and interoperate with many DSPs, creating heavy concurrency:

• CPU: Similar to DSPs but with emphasis on parallel processing

  • 24-48 cores for handling concurrent bid requests

  • Strong per-core performance

• Memory: 256GB to 1TB RAM

  • Essential for managing publisher inventory catalogs

  • Real-time yield optimization algorithms also require substantial memory allocation

• Network: 25-100 GbE links and multiple interfaces

  • SSPs frequently see hundreds of thousands to millions of QPS (with traffic shaping and per-bidder caps)

  • Low device latency targets help (sub-microsecond class)

• Storage: Hybrid approach with SSD and HDD

  • Fast SSDs for active inventory/auction artifacts

  • Large HDD or object storage for historical reporting

DMP Infrastructure: The Data Powerhouse

DMPs prioritize data processing and organization over sub-100 ms response paths:

• CPU: Balanced approach between cores and frequency

  • 12-24 cores with emphasis on parallel processing capabilities

  • Support for advanced instruction sets (AVX-512) for data processing

• Memory: 256GB to 2TB RAM depending on dataset size

  • Critical for in-memory analytics and segmentation

  • More RAM also means faster audience segment creation

• Network: High bandwidth is more important than ultra-low latency

  • 10–40 GbE is typical

  • Focus on sustained throughput rather than burst performance

• Storage: Massive capacity with a tiered approach

  • NVMe SSDs for active segments and real-time processing

  • Large HDD arrays (100TB+) for historical data storage

  • Integrate with specialized databases or lakes

Ad Exchange Infrastructure: The Speed Demon

Exchanges orchestrate the entire auction in real time and push for the lowest variability:

• CPU: Prioritize high-frequency parts and minimize NUMA effects

  • 32-64 cores with the highest available clock speeds

  • Scale horizontally across several single-socket servers for lower latency

• Memory: 512GB to 2TB RAM standard

  • Maintaining state for millions of concurrent auctions

  • Caching for frequently accessed publisher and advertiser data

• Network: 25/50/100 GbE+ in the fabric

  • Multiple high-throughput connections

  • Direct peering connections to major DSPs and SSPs

• Storage: All-flash for hot operational data is common, but keep the hottest keys in memory/K/V

  • Microsecond-level access times required

  • Redundant storage for zero-downtime operations

Scaling Considerations

The harsh reality? The Trade Desk spent $264 million on platform operations during the first nine months of 2023, or $730,000 per day. This includes cloud infrastructure costs that can quickly spiral out of control if not managed carefully.

Smart teams adopt hybrid strategies. For example, building bid processors with minimal local state enables aggressive scale-out/in and even the use of preemptible/spot resources where appropriate. But for the core real-time path where every millisecond counts, dedicated bare-metal remains the most reliable way to get consistent performance at a predictable unit cost.

The key is understanding your workload patterns. If you’re processing billions of bid requests daily, the upfront and operational investment in dedicated infrastructure pays for itself via lower per-transaction costs and tighter performance control. Smaller or early-stage platforms can begin on hyperscalers for speed and flexibility, but must watch spend and design for portability from day one.

Why Latitude.sh Gets Results for Demanding Companies

Latitude.sh has built a solid reputation helping companies that need serious performance from their infrastructure, with proven results across demanding, latency-sensitive industries.

Customers report significant efficiency gains: up to 86% lower compute costs and 3× better system performance compared with similarly sized virtualized deployments. Latitude.sh particularly shines in real-time use cases: e.g., Neon Labs achieved real-time response targets while cutting cloud costs by 60%. For globally distributed experiences, companies like Zyte leverage Latitude.sh’s footprint to place workloads closer to users and reduce latency.

Customers also highlight the predictable performance that matters most in auctions. As Benjamin Hunter from Decent Land Labs puts it: “Latitude.sh provides consistent, high-performance compute without the ‘noisy neighbor’ effect common in AWS. More than that, we get lower latency and more predictable throughput.” That predictability is exactly what AdTech needs to meet strict SLOs consistently.

The platform handles mission-critical workloads well: Algolia improved user-perceived latency, while MUBI increased streaming quality metrics. Improvements like these translate directly to AdTech performance, where every millisecond can boost win rates and campaign outcomes.

Why Your Infrastructure Choice Matters A Lot

For AdTech platforms, infrastructure directly affects revenue. If your servers can’t keep up, you don’t just lose impressions, you lose advertiser trust.

Bare metal provides the consistent performance, dedicated resources, and customization options that AdTech platforms need to succeed. 

While the initial setup can be more involved than spinning up generic cloud instances, the long-term gains in performance reliability, cost control, and competitive advantage make dedicated infrastructure a smart choice for serious AdTech operations.

As online advertising evolves and performance requirements tighten, the companies investing in solid, dedicated infrastructure today will be best positioned for tomorrow. The real question isn’t whether your current setup is “good enough,” it’s whether it gives you the edge in a market where success is measured in milliseconds.

Ready to get the speed and performance-per-dollar your AdTech operation needs to scale? Spin up your bare metal servers at Latitude.sh today.

FAQ

How fast do AdTech platforms need to respond to bid requests?

Exchanges commonly expect end-to-end responses within ~100 ms. Accounting for network overhead, a DSP typically has ~50–80 ms to decide. Being ~50 ms late is often the difference between winning and losing the impression.

What exactly is the OpenRTB protocol, and why does it matter?

OpenRTB is a standardized API spec that lets programmatic participants interoperate. It defines how requests/responses are structured, including per-request time budgets (tmax). Without it, every integration would be bespoke, and the ecosystem wouldn’t scale.

What's the main problem with shared cloud servers for AdTech?

Resource contention (noisy neighbors) and shared I/O can cause unpredictable latency spikes—fatal in millisecond-sensitive bidding paths. Dedicated servers remove that variability.

What kind of savings can dedicated servers provide?

Customer reports show cost reductions up to 86% versus like-for-like public-cloud deployments, along with 3× performance improvements on comparable workloads. Your mileage will vary based on architecture and workload mix, but unit economics typically improve as scale grows.

What's the real infrastructure cost of running a major AdTech platform?

At the high end, large independent platforms have reported spending in the hundreds of millions of dollars annually on platform operations. Mid-market DSPs/SSPs can still see infrastructure costs in the tens to low hundreds of thousands per month, depending on QPS, regions, and data retention. Unchecked cloud bills have bankrupted companies before. Cost governance and architecture matter.

Can I start with cloud infrastructure and migrate to dedicated servers later?

Absolutely, and many do. Start on a hyperscaler to validate, but design to be infrastructure-agnostic: keep local state minimal, externalize features, and build stateless services. As QPS and data gravity grow, move the hot path to dedicated bare metal while keeping burst or batch workloads elastic.

Why is infrastructure choice so critical for AdTech success?

Because it sets your latency floor, your cost per decision, and your ability to scale without surprises. In AdTech, those three variables determine whether you consistently win auctions and keep advertiser trust.