Ad Tech Stack

To clearly understand what is an ad tech stack, it helps to see it as a coordinated ecosystem of ad tech tools that automate how digital ads are bought, sold, delivered, and measured.
Each layer of the ad tech stack plays a distinct role, but real value appears when they work together.

For Advertisers (Demand Side)

✦ Demand-Side Platforms (DSPs)
DSPs allow advertisers to buy ad inventory across thousands of websites and apps in real time. Using data and bidding logic, DSPs decide which ad to show, to whom, and at what price.
✦ Advertiser Ad Servers
Ad Servers host creatives, track impressions, clicks, and conversions, enforce frequency capping, and provide independent measurement across platforms. They ensure consistency and transparency when campaigns run across multiple DSPs and publishers.
✦ Data Management Platforms (DMPs and CDPs)
These Ad Tech tools collect, structure, and activate audience data. They help advertisers build segments using first and third party data, a critical function as third-party cookies continue to disappear.

For Publishers (Supply Side)

✦ Supply-Side Platforms (SSPs)
SSPs help publishers sell their ad inventory efficiently by exposing it to multiple DSPs and exchanges, maximizing yield through real-time auctions.
✦ Publisher Ad Servers
These platforms manage ad delivery on publisher-owned properties, enforce rules like frequency capping, and ensure ads are rendered correctly.

The Marketplace Layer

✦ Ad Exchanges
Ad exchanges are real-time marketplaces connecting DSPs and SSPs. They enable automated buying and selling of impressions at massive scale within milliseconds.

Supporting Ad Tech Tools

✦ Ad Networks — aggregate inventory and resell it to advertisers
✦ Analytics and Measurement Platforms — attribution, brand lift, ROI analysis
✦ Creative Management Platforms (CMPs) — build, test, and optimize ad creatives

In simple terms, an ad tech stack works like a layered factory where data platforms define audiences, DSPs bid for impressions, SSPs offer inventory, and analytics tools measure results, all automated for speed and scale.

Selecting appropriate programming languages is a foundational decision in adtech software development, directly affecting system performance, reliability, and scalability.

‣ Go, Java — commonly used for core real-time services such as bidding engines, request routing, and latency-sensitive processing, where concurrency and predictable performance are critical.
Based on Asteriosoft’s experience, Go is better suited for parts of AdTech systems that require extreme speed, such as bidders. Bidders written in Go can achieve bid request processing times of up to 5 milliseconds. At the same time, using Go to build an enterprise-level system is often impractical. For such systems, Java is a better choice, as it offers a rich ecosystem of mature libraries that accelerate development and help reduce the overall cost of the solution.

‣ Python — We recommend using Python for data analysis and machine learning, as the language is well suited for these purposes. An additional advantage is the extensive ecosystem of libraries available for ML. Python is also widely used for developing and working with Large Language Models. At the same time, Python is relatively slow in high-throughput processing, making it unsuitable as the core language for an entire system. The optimal approach is to use Python for analytics, machine learning, and LLM-related components.

‣ JavaScript / TypeScript — standard choices for dashboards, integrations, and client-facing interfaces, as well as certain backend service.
Asteriosoft uses JavaScript frameworks such as React.js to accelerate and optimize user interface development. JavaScript and TypeScript are also used for scripts that run on web pages and within mobile applications. Prebid.js, which is an industry standard for real-time auctions, is also written in JavaScript.

‣ SQL and NoSQL databases — used to store and process event streams, reporting data, and audience information, depending on consistency and scalability requirements.
Some data needs to be processed extremely fast, and there are databases designed specifically for this purpose. We use Aerospike for handling such data, as it is one of the fastest databases available. AdTech systems also require storage and processing of massive data volumes, along with a high number of calculations. This should be carefully considered when choosing the right database solution. Key questions include how much data the database can process and what the associated costs will be. For some systems monthly costs can be very high, which is why these questions should be addressed early, at the architectural design stage.

High-quality Ad Tech software development prioritizes concurrency control, fault tolerance, and observability. These capabilities are essential for systems that must operate continuously, process large data volumes, and maintain predictable performance under variable load.

Artificial intelligence is increasingly embedded into modern adtech platforms as a supporting layer for optimization and decision-making, rather than as a standalone component.

Common AI-assisted capabilities include:

• Bid and budget optimization based on historical and real-time signals
• Fraud detection and traffic quality assessment
• Performance forecasting for creatives and campaigns
• Automation of selected campaign management and optimization tasks

When implemented with appropriate data pipelines, monitoring, and human oversight, AI can significantly reduce manual effort and improve operational efficiency. However, these systems typically augment decision-making rather than operate as fully autonomous, self-optimizing platforms.

Modern Ad Tech solutions are cloud-based and designed to support high-throughput, low-latency advertising workloads. Within an ad tech stack, infrastructure choices directly impact performance, scalability, and cost efficiency.

Enterprise-grade AdTech platforms are commonly deployed on leading hyperscale cloud providers, including:
Amazon Web Services (AWS) — frequently used for real-time bidding, event streaming, and globally distributed, low-latency architectures
Google Cloud Platform (GCP) — often selected for data-intensive workloads, advanced analytics, and machine-learning-driven optimization
Microsoft Azure — widely adopted in enterprise environments, particularly where hybrid infrastructure and deep integration with corporate systems are required

Cloud-based AdTech stack enables teams to:

• Process large volumes of bid requests and events in real time
• Deliver advertising services with low latency across multiple geographic regions
• Scale infrastructure dynamically in response to traffic fluctuations
• Optimize infrastructure costs through autoscaling and usage-based pricing models

For high-load advertising systems operating in real-time environments, cloud-native architecture has become an industry standard, providing the flexibility, resilience, and scalability required for modern programmatic advertising.

A mature adtech stack is more than technical infrastructure — it represents accumulated engineering knowledge, operational experience, and proprietary workflows.

Organizations that invest in custom adtech software development gain:
✔ Greater independence from closed or inflexible third-party platforms
✔ Faster experimentation and iteration cycles
✔ Improved transparency and control over data, costs, and margins

For this reason, many AdTech companies treat their technology stack as a long-term business asset and a source of competitive differentiation, rather than simply a collection of tools.