Many systems must operate across organizational boundaries where each participant maintains independent control over their resources. Medical research provides a clear example of this constraint. Patient data is typically stored within individual hospital systems due to privacy regulations, institutional policies, and legal requirements. When researchers want to develop machine learning models that leverage data from multiple hospitals, they cannot simply aggregate all data in one location.

This necessity drives the development of federated learning systems, where the model training algorithm travels to each hospital’s local data center, performs computations on the local data, and then shares only the model updates (not the raw data) with a central coordinating service. Stanford University’s DAWN project has implemented such a system for medical image analysis, allowing multiple medical centers to collectively train diagnostic algorithms without sharing protected patient data.

The constraint here is not technical but organizational and regulatory - the data must remain under the control of the original institution. The decentralization is necessary, not optional.

Some systems operate in environments where no single entity can or should be trusted with complete control. Financial systems often encounter this constraint, particularly when facilitating transactions between parties with limited trust relationships.

Bitcoin and other blockchain-based systems represent quintessential examples of trust-driven decentralization. Participants in these networks do not trust any single authority to maintain a ledger of transactions. Instead, the ledger is fully replicated across thousands of nodes, with a consensus protocol ensuring agreement on the transaction history despite potentially malicious actors.

The Bitcoin network processes hundreds of millions of dollars in transactions daily without requiring users to trust any central authority. This approach fundamentally differs from traditional banking systems where a central authority (the bank) maintains transaction records. The decentralization isn’t merely a design preference—it’s the core innovation that enables the system’s trustless operation.

Some systems are decentralized because their physical components must exist in specific geographic locations. Weather monitoring networks illustrate this constraint clearly. Weather sensors must be physically distributed across the geography they monitor—sensors in New York tell us nothing about the weather in Los Angeles.

The National Weather Service operates thousands of monitoring stations across the United States, each collecting local atmospheric data. This data feeds into predictive models that forecast weather patterns. The decentralization of data collection is an inherent requirement of the system’s purpose, not a design choice.

Similarly, the control systems for power grids must be partially decentralized because of the physical distribution of generation and transmission infrastructure. While centralized oversight exists, local control systems must make real-time decisions based on local conditions, as the time required to communicate with a central authority would make the system too slow to respond to rapidly changing conditions.

Increasingly, regulatory frameworks require certain types of data to remain within specific geographic or jurisdictional boundaries. The European Union’s General Data Protection Regulation (GDPR) and similar regulations worldwide have forced many global companies to decentralize their data storage and processing.

Microsoft, for example, operates distinct Azure cloud regions throughout the world, with data centers that keep European customer data within EU borders. This decentralization isn’t driven by technical considerations but by regulatory requirements that make centralization legally impossible.

Google’s search engine handles billions of queries daily across a massive distributed infrastructure. When users enter a search query, their request is processed by multiple systems: one to parse the query, others to search different portions of the index, and still others to rank and assemble the results.

This distribution isn’t necessary in the sense that a single (theoretical) supercomputer could perform all these functions. Rather, Google distributes its system to achieve specific quality attributes: scalability to handle the query volume, fault tolerance to maintain service despite hardware failures, and performance to deliver results within milliseconds.

The company famously built its infrastructure using thousands of commodity servers rather than a few high-end machines, making an explicit design choice to distribute processing horizontally. This approach provided cost advantages and simplified scaling as demand grew.

Akamai operates over 340,000 servers in more than 135 countries. When users visit websites served through Akamai’s CDN, they’re transparently redirected to nearby servers that host copies of the requested content.

This distribution isn’t necessary for the content to be accessible—a single central server could theoretically serve all users. However, by distributing content based on geographic proximity to users, CDNs reduce latency, improve bandwidth utilization, and enhance the user experience. The system makes sophisticated decisions about which content to replicate where, based on usage patterns and performance metrics.

Netflix employs a similar strategy, placing content caching servers within internet service providers’ networks. This distributed approach allows them to deliver high-definition video streams to millions of concurrent users without overwhelming internet backbones—a sufficient distribution to achieve their performance requirements.

Decentralized systems must manage the inherent complexity of distribution without the option of simplifying through centralization. This often requires sophisticated consensus mechanisms, complex security models, and careful handling of partial failures.

The Google Spanner database illustrates how challenging this can be. To maintain consistent data across globally distributed data centers, Spanner relies on GPS receivers and atomic clocks to provide precise time synchronization. This extraordinary complexity is necessary because the system must maintain consistency across administrative domains while supporting global distribution.

Distributed systems can often leverage centralized control planes even while distributing data and processing. Kubernetes, the container orchestration system, uses a centralized control plane to manage distributed container workloads, simplifying many aspects of system management.

Decentralized systems typically incur performance penalties for their necessary distribution. Bitcoin processes only a few transactions per second compared to Visa’s tens of thousands, because its decentralized consensus mechanism prioritizes trustlessness over throughput.

Distributed systems can often achieve performance improvements through their distribution. Hadoop distributes data processing across computer clusters to enable analysis of datasets too large for single machines, turning distribution into a performance advantage rather than a liability.

Decentralized systems face unique challenges in evolution. Changes to the Bitcoin protocol, for example, require broad consensus among a diverse community of miners, developers, and users. This governance by consensus makes rapid evolution difficult but protects the system’s decentralized nature.

Distributed systems that remain under unified administrative control can evolve more rapidly. Amazon can update its distributed retail platform without seeking consensus beyond its own engineering teams, allowing for more agile responses to changing requirements.