How adtech companies can future-proof their infrastructure

As the name suggests, adtech and its success is firmly rooted in the infrastructure that supports it. Adtech companies are measured by their ability to deliver advertising services within the finest of margins and the minutest of milliseconds.

The industry has matured rapidly since the first display ad appeared on HotWired in 1994. Today, it operates across the internet, retail media, social platforms and Connected TV (CTV), with infrastructure required to support not just real-time bidding, but large-scale data processing and privacy-first targeting across multiple channels and regions.

Delivering these modern demands requires high performance, low latency infrastructure. And as we enter into the age of AI and Machine Learning, that infrastructure must also handle the heavy compute loads these technologies require.

But the deadline of around sub-100 milliseconds for SSPs to receive a response from DSPs in many programmatic auctions still stands. Adtech companies need infrastructure that delivers consistent performance at scale, even as data volumes, AI workloads, and regulatory demands grow.

Knowing what drives and supports these systems is key to making the right decisions. To see why these capabilities are so critical, breaking down the core components of an adtech company’s infrastructure is essential in finding out how their operations will remain resilient for years to come.

Why storage, bandwidth, latency - and intelligence - matter in adtech

There are three key service providers within the world of adtech: demand side platforms (DSPs), supply side platforms (SSPs) and ad exchanges.

DSPs need storage

A DSP is a software used for programmatic media buying; automating the process of real-time bidding so that there is very little input required when securing placement for an advert. Matching a campaign to the most relevant audience on the publisher’s website, a DSP aggregates inventory from multiple different resources including ad exchanges and SSPs, calculating prices and delivery based on data.

These platforms are generally made up of a bidder, DSP ad server, data platform, UI, banker, campaign tracker, reporting database, user profile data base and third-party integrations. Together they provide real-time analytics, audience targeting, bidding, budgeting, and campaign management to ensure advertisers reach the right audience across multiple channels.

As such, DSP infrastructure requirements center around storage for inventory, as well as support for tools such as Kafka for analytics and Aerospike for databases. With the number of transactions taking place, alongside the data analytics tools, it doesn’t take long for storage to become a key challenge for DSPs from a cost, space, and configuration perspective.

SSPs need bandwidth

SSPs on the other hand are most concerned about bandwidth. SSPs work with publishers - websites, video games, social media sites for example - to monetize their platform by managing, selling, and optimizing inventory or in other words, ad space.

Like DSPs, for SSPs need a number of components to provide these services including integrations with other adtech platforms (DSPs, ad servers and ad exchanges), trackers collecting data about a publisher’s website and audience, and a reporting database to generate reports and view campaign analytics.

Thanks to the sheer volume of information being sent from their SSP ad servers and platforms, supply side platforms need infrastructure that provides cost-effective and reliable levels of bandwidth.

Ad exchanges need low latency

Lastly, ad exchanges are the transaction points where money is exchanged for an advert that matches a particular target audience on a publisher platform. At a high level, it's an exchange between a DSP’s ad inventory and an SSP’s ad space on a publisher’s medium. Most of the time a DSP or SSP will have an integrated ad exchange/network functions within their platform.

It’s here that ping - the time it takes for a request to be sent from an SSP to a DSP and return with the winning bid - becomes most important, and it needs to be below 100 milliseconds. A low-latency network is crucial to delivering these industry-standard ping times.

Where performance meets intelligence

So, in 2026, where does AI come into play?

AI and machine learning are now optimizing bids, predicting user intent, personalizing creative delivery, and detecting fraud in real time. This shift places new demands on infrastructure, including support for accelerated computing, GPU-enabled workloads, MLOps pipelines, and high-throughput data platforms capable of feeding models with fresh data at scale.

As these models become more central to ad delivery, the way data is collected, governed, and processed becomes just as critical as the compute behind them. In a cookieless privacy-first environment, platforms must integrate consent frameworks, data governance layers, and privacy-enhancing technologies such as clean rooms and browser-based standards, making infrastructure placement and regional data handling a core part of AI readiness.

The cloud promise

The hyperscale cloud providers hold the largest market share of the adtech infrastructure space. A key reason for that success are the free credits that they provide to start-ups and young adtech companies to build their platforms on the hyperscalers infrastructure. And why not? Money is always tight at the inception of a new company and if adtech companies can save themselves significant infrastructure costs by taking advantage of the free credits, who can blame them?

The problem is that those free credits have a cut-off point. And as companies become more successful and start to grow, they naturally also start to require more bandwidth and storage.

These requirements aren’t always predictable or stable. Like the stock market, there can be predictable daily highs and lows, but during busy advertising periods such as the Superbowl, major retail events such as Black Friday, or large-scale streaming and CTV campaigns (the final season of Stranger Things being a recent example), peaks can occur at any time. These peaks require a substantial increase in infrastructure resources such as CPU, GPU and network capacity.

This growth in resource requirements, plus the unpredictable peaks of bids/requests that run through adtech platforms, mean that adtech companies need infrastructure that can scale easily. Hyperscale cloud providers can deliver. But it comes at a price and a premium price at that.

The cloud reality

So, what does hosting your infrastructure long-term with a hyperscale cloud provider look like?

Paying-per-use becomes paying through the nose for what you need. It’s quite rare to have an application or service hosted on hyperscale cloud infrastructure that can be fully scaled down in real-world adtech environments, particularly when platforms must remain highly available across regions and channels at all times.

If the adtech company is successful, demand for those services will likely increase and the resources needed to support them will grow in parallel. While paying-per-use might sound attractive at the beginning, when you consider the reality of constant availability, regulatory overhead and growing resource requirements, it very quickly becomes an expensive option.

Flexibility can also turn into vendor lock-in. If an adtech company builds deeply into proprietary cloud services, that platform is often optimized around a specific provider’s ecosystem.

When costs start to ramp up and the company wants to change course, the money, time, and engineering resources involved in moving to another environment can be significant. Some organizations even bring in dedicated teams to manage usage, governance, and cost optimization to prevent spend from spiralling out of control.

As AI and machine learning become central to ad delivery and measurement, infrastructure costs are increasingly driven by compute-intensive workloads. Without careful optimization, these workloads can significantly amplify cloud spend, making visibility, governance, and FinOps practices essential parts of long-term infrastructure strategy.

Future-proofed adtech infrastructure

Future-proofing adtech infrastructure starts with designing for consistency in an environment defined by constant change. As platforms take heavier AI and machine learning workloads, the underlying architecture needs to provide both performance stability and long-term flexibility.

Bare metal is increasingly being used as a foundation for performance-critical components where predictable latency and direct control over compute and networking makes a difference. This level of control can be particularly valuable for workloads that need to operate within defined regulatory boundaries or handle sensitive data at scale.

By being deliberate about where workloads and data are placed, adtech companies can align their infrastructure more closely with evolving privacy frameworks, compliance obligations, and regional hosting requirements, without introducing unnecessary complexity into day-to-day operations.

Public cloud remains an important part of the picture, especially for handling variable demand, short-term projects, and environments that benefit from rapid provisioning. When these models are combined, teams can create a hybrid infrastructure layout that supports steady, always-on services while still allowing for growth and sudden peaks in demand without the drawbacks of vendor lock-in and overspending.

Building for the future in adtech is ultimately about creating an architecture that can absorb new channels and technologies as they emerge. Infrastructure that is designed to be adaptable gives technical teams the confidence to support innovation today, while remaining resilient to the demands of tomorrow.