What is highly available infrastructure and why is it needed?

Any digital service, whether it's an online store, SaaS platform, API, media portal, or corporate system, will sooner or later run into some kind of problem. Equipment breaks down, networks fail, software updates come out with bugs. And let's not even get started on the human factor.

That's why there's a simple and straightforward rule in infrastructure engineering: it's not a question of whether a failure will occur, but when it will occur. And this is where High Availability Infrastructure comes to the rescue of us engineers.

High Availability Infrastructure (or simply HA) is an approach to designing IT systems in which the failure of individual components does not lead to service downtime. If one element fails, the system automatically switches to backup resources and circuits, so that the user often does not even notice the problem.

Ten to fifteen years ago, this type of architecture was considered the preserve of large corporations and banks, as it required significant financial investment and entire departments of highly qualified specialists. Today, the cost and availability of high-availability solutions has decreased significantly, as has the overall entry threshold. Therefore, such solutions are now used almost everywhere, from SaaS services and large marketplaces to ordinary web applications and startups.

In this article, let's take a look at what high availability is, how it is achieved in practice, and what technologies underlie modern fault-tolerant infrastructure.

 

 

 

 

What does high availability mean?

High availability (HA) is the ability of a system to remain operational even when individual infrastructure components fail. Simply put, the service continues to function even when one of the servers, network devices, or software modules stops working.

The idea of high availability is based on a simple principle: in any complex system, failures are inevitable, so the infrastructure must be designed in such a way that such failures do not lead to the shutdown of the entire service. This is achieved through resource redundancy, automatic failover, data replication, and continuous system monitoring.

In engineering practice, availability is usually measured as the percentage of time during which the service remains available to users. This indicator is often specified in a service level agreement (SLA), and the higher the availability percentage, the less downtime is allowed during the year.

Even a small difference in percentage points can mean a significant difference in actual service downtime. For example, moving from 99.9% to 99.99% reduces the allowable downtime by almost ten times.

Below are typical availability levels used in online services and cloud platform infrastructure.

 

Table - Typical availability levels (SLA)

Availability	Downtime per year	Typical usage
99%	about 3.6 days	small websites and internal services
99.9%	about 8.7 hours	SaaS applications and corporate systems
99.99%	about 52 minutes	large web projects and online platforms
99.999%	about 5 minutes	financial systems and mission-critical services

 

The 99.999% metric is often referred to as “five nines availability”. This level of availability can only be achieved with a carefully designed architecture that includes server redundancy, distributed databases, fault-tolerant networks, and automatic system recovery mechanisms.

The SLA level for all servers from 3v-Hosting is close to 99.99%.

 

 

 

 

What is a Single Point of Failure (SPOF)?

When talking about high availability, it is impossible to ignore another key concept, such as Single Point of Failure.

A Single Point of Failure (SPOF) is any component of the infrastructure whose failure leads to the shutdown of the entire service. Simply put, it is an element of the system on which the operation of the application critically depends and for which there is no backup replacement.

Such points of failure are much more common than they might seem at first glance. This is especially true for small projects or infrastructures that were originally built without taking fault tolerance into account.

 

Let's look at some typical examples of single points of failure. In practice, SPOFs can be found at different levels of the infrastructure:

• a single application server on which the entire website or API runs;
• a single database where all service data is stored;
• a single load balancer through which all incoming traffic passes;
• a single network switch or router;
• a single DNS server responsible for domain name resolution;
• a single data storage (storage server or file system);
• etc.

 

Even if the infrastructure itself seems reliable, the presence of even one such point can negate all efforts to ensure stability.

For example, if a web application is hosted on a single server, any hardware failure, whether it be SSD failure, processor overheating, or a problem with the power supply, will immediately result in the site becoming completely unavailable. Users will simply stop receiving responses from the service.

Therefore, one of the main tasks in designing a fault-tolerant infrastructure is to detect and eliminate all possible SPOFs. To do this, critical components are always duplicated, multiple application servers, a database cluster, backup network devices, and distributed storage systems are used.

It is the elimination of single points of failure that forms the basis of High Availability architecture, allowing the system to continue operating even when individual elements fail.

 

 

 

 

Causes of infrastructure failures

Even the most reliable IT system consists of a large number of components, a bunch of servers, network infrastructure, operating systems, databases, and applications. Each of these elements plays its own role, and any of them can potentially become a source of problems.

It is important to understand that failures are not an exception, but a normal part of the life of any infrastructure. Even when using high-quality equipment and proven software, it is simply impossible to completely eliminate the risk of failures. That is why modern architectures are built with the expectation that failures will occur sooner or later.

In practice, most infrastructure incidents can be divided into several basic categories.

 

Hardware failures

Physical equipment wears out over time, and this is one of the most common causes of infrastructure problems. Even in data centers with redundant power and cooling, equipment can fail.

The most common hardware problems include:

• failure of SSD or HDD;
• failure of RAM modules;
• overheating of processors or other components;
• power supply malfunctions;
• failures or degradation of RAID arrays.

It turns out that even the most trivial disk failure can lead to system unavailability if the infrastructure does not provide for redundancy.

 

Network problems

Sometimes the source of the failure is not on the server itself, but in the network infrastructure. Since modern services are heavily dependent on network connections, problems at this level can quickly lead to application unavailability.

Typical network incidents may include:

• network channel overload;
• router or switch failures;
• network device configuration errors;
• BGP routing problems;
• load balancer malfunctions.

 

Software errors

Even if the hardware infrastructure is working flawlessly, the software layer of the system can also cause incidents.

In this context, common problems include:

• failed application updates;
• errors in new software versions;
• incorrect service configurations;
• memory leaks and performance degradation;
• process or service freezes.

 

Sometimes, a single, seemingly insignificant error in a configuration file can cause the entire system to crash.

But that's not all. Even if you've planned everything, reserved all devices, made a bunch of backups, and parallelized all processes, no one has canceled the notorious human facuptor.

 

Human factor

According to statistics from DevOps teams and large cloud platforms, a significant portion of incidents are caused by human error. This could be a failed deployment, incorrect infrastructure configuration, or accidental deletion of critical data. A single simple typo in the configuration file is enough to cause your Terraform infrastructure to be assembled incorrectly.

Other examples of such situations include:

• an error in server configuration;
• incorrect deployment of a new version of an application;
• incorrect modification of network rules;
• accidental deletion of data or services;
• etc.

 

That is why modern DevOps practices pay great attention to automation, change control, and monitoring systems.

 

As a result, the approach to building fault-tolerant systems is changing over time, and instead of trying to completely eliminate failures, engineers are designing infrastructure in such a way that it can continue to operate even if problems arise.

 

 

 

 

Basic principles of high-availability architecture

Fault-tolerant infrastructure does not arise on its own - it is the result of a well-thought-out architecture. To ensure that the system can continue to operate even when problems arise, engineers use a number of fundamental principles on which modern high-availability platforms are built.

And although specific technologies may vary from project to project, most HA infrastructures rely on several key approaches.

 

Eliminating single points of failure

The first and most important rule of high-availability architecture, which we discussed above, is that the system should not have components whose failure would lead to a complete shutdown of the service. Let's repeat this again, as it bears repeating.

Every critical element of the infrastructure must have a backup or alternative source of service. This applies to application servers as well as network devices, databases, and data storage systems.

In practice, this means using:

• multiple application servers;
• backup load balancers;
• database clusters;
• distributed storage systems.

 

For example, if a web application runs on multiple nodes, when one server fails, the others continue to process user requests. The load balancer automatically excludes the unavailable server from the pool and redirects traffic to the working nodes. This way, the infrastructure remains operational even in the event of a partial failure.

 

Automatic switching (Failover)

The presence of backup components alone is not enough. It is important that the system can automatically switch to backup resources without requiring intervention by engineers. This process is called failover.

Failover allows the backup component to automatically take over the functions of the failed node. In a well-designed system, this happens very quickly, in a matter of seconds, so that users do not notice any problems.

Failover mechanisms can operate at different levels of the infrastructure:

• the load balancer redirects traffic to other servers;
• a database cluster automatically selects a new primary node;
• a container orchestrator restarts the application on another node;
• storage systems switch to backup disk nodes.

The main goal of failover is to minimize service downtime in the event of a failure.

 

Data replication

Also, to ensure high availability, it is not enough to duplicate only computing resources, because data must also exist in multiple copies. The replication mechanism allows you to store information on several nodes at once, which protects the system from data loss and ensures the continuity of services.

In practice, various replication models are used:

• master–replica (primary–replica) - when the main server synchronizes data with backup nodes;
• multi-master architecture - when multiple servers can accept records simultaneously;
• distributed storage systems - when data is automatically distributed among multiple nodes.

 

 

Monitoring and automatic recovery

Even the best-designed system needs constant monitoring. Without monitoring, it is impossible to quickly detect a problem and launch recovery mechanisms. Therefore, high-availability systems always include observability tools that track the status of the infrastructure in real time.

Typically, monitoring collects information about:

• the status of servers and individual virtual machines;
• CPU, memory, and disk usage on the server;
• service and application availability;
• network latency and channel bandwidth;
• database status;
• etc.

 

There are a great many monitoring tools, but the most popular and well-established ones are currently considered to be Prometheus, Grafana, Zabbix, and Alertmanager.

These systems allow you to automatically detect anomalies, send notifications to engineers, and in some cases, launch automatic recovery processes.

Thanks to monitoring, the team can learn about a problem before users notice it, which is one of the key factors for the stable operation of modern online services.

 

 

 

 

Typical tools for building HA infrastructure

Let's summarize everything we've already discussed. High availability is achieved not by a single technology, but by a combination of tools that work at different levels of the infrastructure. Each of them is responsible for its own part of the system, whether it's traffic distribution, server redundancy, data replication, file storage, or service status monitoring.

In modern DevOps practice, there is a whole set of proven solutions that allow you to build sufficiently fault-tolerant systems. Below are the most common tools currently used to implement this idea.

 

Table - High Availability Tools

Infrastructure layer	Purpose	Popular tools
Load balancing	Distributing incoming traffic between multiple application servers	HAProxy, Nginx, Traefik, Envoy
Failover and Virtual IP	Automatic traffic switch to a backup node in case of failure	Keepalived, Pacemaker, Corosync
Database clustering	Data replication and automatic primary node failover	Patroni, Galera Cluster, PostgreSQL Streaming Replication, MySQL Group Replication
Container orchestration	Automatic container management and service restart	Kubernetes, Docker Swarm, Nomad
Data storage	Distributed file storage and data loss protection	Ceph, GlusterFS, MinIO
Monitoring and alerts	Infrastructure monitoring and problem detection	Prometheus, Grafana, Zabbix, Alertmanager
Service discovery	Automatic detection of services in a distributed system	Consul, etcd, ZooKeeper
Logging and observability	Log collection and analysis for troubleshooting	ELK Stack (Elasticsearch, Logstash, Kibana), Loki

 

Each of these tools serves a specific purpose in a high-availability architecture. For example, load balancers distribute user traffic across multiple servers, database replication systems ensure data integrity, and container orchestrators automatically restart services in the event of failures.

In real-world infrastructures, these technologies are usually used in combination, and it is this combination of tools that allows you to create systems that can continue to operate even when individual servers, network devices, or software components fail.

 

 

 

 

The price of high availability

Like any engineering solution, high availability comes at a price. A fault-tolerant infrastructure requires more resources, a more complex architecture, and additional management tools. And, of course, we must not forget the expertise of the personnel who build and maintain this system. Therefore, the main trade-off of the HA approach is the increased cost of infrastructure and its maintenance.

Reserving the necessary components increases both the cost of the infrastructure and the complexity of its operation. Engineers have to manage a more complex system, monitor data synchronization, test failure scenarios, and maintain automatic switching mechanisms.

However, when evaluating costs, it is important to consider not only the cost of the infrastructure, but also the cost of service downtime.

For large Internet projects, even a short period of unavailability can have serious consequences, when one hour of downtime can lead to the loss of users and customers, a decline in trust in the service, a halt in sales or transactions, and even reputational risks for the company. For e-commerce projects or SaaS services, such incidents can directly translate into financial losses.

That is why many companies view high availability not as an additional expense, but as an investment in business stability and user experience quality. A fault-tolerant infrastructure allows the service to operate predictably, reduces the risk of incidents, and provides the reliability that modern users expect.

Based on this, it becomes clear that not every service needs high availability. For many projects, short-term downtime is quite acceptable, especially if it does not lead to financial losses or critical disruptions in user operations. For example, personal blogs, small corporate websites, landing pages, internal test environments, startup MVPs, or educational projects can often run on a single server without complex fault-tolerant architecture. In such cases, it is much more important to reduce the cost of infrastructure and simplify its maintenance than to invest in complex high availability mechanisms.

 

 

 

Frequently Asked Questions (FAQ)

 

What is High Availability?

High Availability is an architectural approach in which a service continues to operate even when individual system components fail. This is achieved through resource redundancy, data replication, and automatic failover to backup nodes.

 

How does High Availability differ from Fault Tolerance?

High Availability allows for a short-term switch to backup resources.

Fault Tolerance assumes continuous operation without any interruption, even at the millisecond level.

 

Is it possible to build HA on a VPS?

Yes. A fault-tolerant infrastructure can be built on a VPS using load balancers, database replication, and container orchestrators.

 

How many servers are needed for HA?

The minimum high availability architecture usually starts with three servers to provide redundancy and automatic failover.

 

Does high availability affect performance?

Generally, no. By distributing the load across multiple nodes, the system can handle more requests.

 

Can HA be added to an existing project?

Yes, but this often requires reworking the infrastructure - adding load balancers, data replication, and monitoring.

 

Is high availability always necessary?

No. For small websites, blogs, or test projects, a complex HA architecture may be excessive.

 

 

 

 

Conclusions

Highly available infrastructure is an architectural approach that allows services to remain available even when individual system components fail. Instead of trying to completely eliminate failures, engineers design infrastructure in such a way that it can survive problems without stopping the service.

The basis of such systems is the elimination of single points of failure, redundancy of key components, data replication, and automatic switching to backup nodes. Monitoring systems also play an important role, allowing incidents to be quickly detected and responded to.

Today, there is a wide range of technologies available for building fault-tolerant solutions, such as load balancers, clustered databases, container orchestrators, distributed storage systems, and observation and monitoring tools. By using them in combination, you can create an infrastructure that can withstand hardware failures, network problems, and software errors.

At the same time, it is important to remember that high availability increases the complexity and cost of the infrastructure. Therefore, the architecture should always be designed with the project tasks, load level, and possible downtime risks in mind.

For services where stability directly affects business, such as online stores, SaaS platforms, APIs, and financial systems, high availability becomes an almost mandatory element of the infrastructure. It allows you to maintain stable service operation, minimize downtime, and provide users with a reliable digital experience.