High Availability vs Fault Tolerance: A Comparative Guide

Recovery approach

Systems are never 100% reliable by nature. This isn't a feature of systems per se but an extension of the accumulated effects of running on infrastructure that can and will fail, as well as the impact of change as new features are introduced. For example, a new feature might increase the load on a dependency in an unanticipated way, leading to an outage of that system. Different types of system faults (network partitions, node crashes, data corruption) require fundamentally different recovery strategies, which is why high availability and fault tolerance take such divergent approaches. This means that planning for recovery is an important part of improving availability (HA and FT).

The way recovery is approached differs significantly between high availability and fault tolerance implementations.

High availability recovery

When implementing highly available architectures, recovery centers on detecting failures quickly and shifting workloads to functioning components. Think of it as having understudies ready to take over when the main actor falls ill: The show continues, but there might be a brief pause while the understudy gets on stage.

Highly available systems employ health checks to monitor component status constantly. When a component is degraded or fails, traffic gets redirected away from it through load balancers while automation works to replace or restart the failed component. During this transition, users might experience a brief interruption—perhaps a few seconds of unavailability or a canceled request that needs to be retried.

This same mechanism also protects against bad deployments by allowing teams to roll out changes serially and roll back individual nodes before all customers are impacted.

Recovery process showing a brief service interruption during failover

Fault tolerance recovery

Fault tolerance takes availability further: Rather than switching to standby components after active ones fail, fault-tolerant systems operate with multiple active components running simultaneously that can take over immediately when failures occur. This is like having multiple actors simultaneously playing the same role—if one exits the stage unexpectedly, the others seamlessly continue the performance without the audience noticing.

In fault-tolerant systems, redundant components operate in parallel, often with complex consensus mechanisms ensuring all active components maintain a consistent state. When one component fails, the others continue providing the service without interruption. For stateful systems like databases, technologies like CockroachDB and Google Spanner implement complex protocols that maintain data consistency even when nodes fail or network partitions occur.

The key difference is in user experience. Highly available systems might experience brief but noticeable interruptions during recovery, while fault-tolerant systems maintain service availability during failure and recovery.

Continuous operation is maintained throughout component failure and recovery

Implementation complexity

With the increasing popularity of the cloud-native landscape, it is arguably becoming easier to implement high availability and, by extension, fault tolerance in systems. More off-the-shelf offerings and cloud providers are making this easier every day. But it is still difficult, especially from an operational standpoint, to manage highly available and fault-tolerant systems.

Maintaining high availability means that when your infrastructure evolves, your applications have to evolve with it. Consider how Kubernetes has changed container orchestration. What started as simple container deployments using Docker now involves complex service meshes, custom resource definitions, operators, and bespoke monitoring solutions. As these platforms evolve, availability implementations must adapt accordingly.

High availability implementation

Implementing “vanilla” high availability has become more accessible through modern tools, but it still requires careful design:

• Load balancing and service discovery: Cloud providers offer managed load balancers like AWS ELB or GCP Load Balancing that distribute traffic and detect unhealthy instances. While easier to set up than traditional hardware solutions, they still require proper configuration and monitoring.
• Health checking: Implementing effective health checks means going beyond simple ping/pong responses. Meaningful health checks verify that a service can perform its core functions. For a payment API, this might mean checking database connectivity, dependency availability, and basic transaction processing.
• Monitoring and alerting: Modern observability platforms like Datadog, New Relic, or Prometheus make monitoring easier, but engineering teams still need to determine what to monitor and how to set appropriate thresholds.

An e-commerce platform implementing high availability might deploy its checkout service across three availability zones in AWS, using an application load balancer with health checks that determine service state. When instances fail, auto-scaling groups automatically replace them while the load balancer redirects traffic. A relevant SLO might be “99.95% of checkout attempts complete successfully” to ensure acceptable levels of availability as the company’s infrastructure evolves.

Fault tolerance implementation

Fault tolerance requires significantly more engineering effort, including the following:

• Consensus protocols: Implementing distributed state management through protocols like Raft or Paxos is complex. While databases like CockroachDB or etcd implement these protocols, engineering teams still need to understand their trade-offs and operational characteristics, and they come at a high cost. 
• Cross-region replication: Setting up active-active deployments across regions involves addressing network latency, data consistency challenges, and complex failure scenarios. Engineers must carefully design for network partition tolerance and data consistency.
• Comprehensive failure testing: Fault-tolerant systems require extensive testing through chaos engineering practices. Netflix's Chaos Monkey, for example, randomly terminates instances to verify that services continue operating correctly during failures.

A payment provider implementing fault tolerance for transaction processing might use CockroachDB deployed across three AWS regions. It would need to carefully tune consensus settings, implement appropriate retry mechanisms, and run regular chaos tests to ensure that transactions remain consistent even during severe infrastructure failures. SLOs would include strict objectives like “99.999% of transactions maintain atomicity, consistency, isolation, and durability (ACID) properties during regional failures.” (Atomicity means that transactions complete entirely or not at all, consistency means that transactions maintain valid data states, isolation means that concurrent transactions don’t interfere, and durability means that completed transactions persist even during failures.)

It's important to note that even fault-tolerant implementations have limitations. Fault-tolerant systems are engineered to handle specific classes of failures without service disruption (like single-region outages), but they might still have failure scenarios they cannot handle (like the simultaneous failure of multiple regions). The difference is that fault tolerance masks certain failures completely, while traditional approaches require brief recovery periods.

The operational complexity of maintaining these systems highlights why SLOs are so crucial. They provide objective measures to verify that availability implementations work as intended, even as the underlying technologies evolve.

End user experience

The user experience differs significantly between high availability and fault tolerance approaches, and this directly affects how businesses should implement availability for different components.

High availability user experience

In high-availability systems, users may experience brief interruptions during failover events. For a payment provider's API gateway implemented with high availability, these interruptions might manifest as:

• Occasional connection timeouts requiring the client to retry
• Brief periods (typically seconds) where the API returns 5xx errors
• Sporadic latency spikes during instance failures and replacements

When customers attempt to make a payment during a failover event, they might see a spinning wheel in the UI followed by an error message suggesting that they try again. Mobile applications using the payment API would need to implement retry logic to handle these occasional failures gracefully. While these interruptions are minimized, they are an accepted trade-off in high-availability systems.

This user experience is typically acceptable for non-critical operations like viewing account history or updating profile information. The brief interruptions are noticeable but don't fundamentally break the user's trust in the system when handled properly, as long as they’re not too frequent.

Fault tolerance user experience

Fault-tolerant systems provide a fundamentally different user experience during failover and recovery. In a payment provider's transaction processing system implemented with fault tolerance:

• Users experience no perceptible interruptions during component failures.
• Transactions continue processing even during regional outages.
• Operations maintain correctness guarantees regardless of underlying infrastructure issues.

When a customer submits a payment transaction during a significant failure (like an entire availability zone going offline), the transaction still completes normally. The customer sees the same confirmation message they would during normal operations, with perhaps only a slight increase in latency that falls within acceptable bounds.

It is important to note that these guarantees are only true for classes of failures that the system is designed to handle, not every possible failure scenario. The key difference is that for covered failure modes, the user experience remains consistent without requiring retries or showing error messages.

The operations team might be responding to alerts and actively mitigating the infrastructure issues, but customers remain completely unaware that anything unusual is happening. This seamless experience is critical for sensitive operations like payment processing, where any interruption could lead to lost revenue and damaged trust.

Aligning user experience with business needs

The choice between high availability and fault tolerance should be driven by the business impact of different user experiences. For the payment provider:

• Reporting dashboards and analytics tools can use high availability, as occasional brief outages are acceptable.
• User account management might implement high availability with appropriate error handling.
• Payment processing and transaction systems require fault tolerance to maintain customer trust and meet regulatory requirements.

By directly measuring the user experience through SLOs rather than focusing on internal system metrics, organizations can make data-driven decisions about where to invest in the more complex and expensive fault-tolerant architectures. Both approaches contribute to the overall goal of maximizing uptime, but fault tolerance techniques provide a different user experience for critical system components, where brief interruptions would have a significant business impact.

SLO alignment

Quantifying reliability and availability through SLOs is where the real work begins. It's not just about saying “we want highly available systems” and then taking arbitrary steps to implement them. SLO alignment requires making sure your targets match the chosen high availability implementation strategy. There needs to be precise, measurable targets that allow for making data-driven decisions about scaling and improvements. 

The alignment challenge involves ensuring your chosen availability implementation strategy upholds the guarantees of your SLOs and error budgets. When a basic high-availability system repeatedly fails to meet its SLO targets—often due to frequent failover interruptions that rapidly deplete the error budget—this indicates misalignment between the availability strategy and business requirements. The system may need an upgrade to a more sophisticated implementation approach that can reliably support its SLO commitments. 

On the other hand, implementing expensive fault tolerance for a component that easily meets its SLO targets with basic active-passive redundancy represents a misaligned investment.  Effective monitoring of the error budget consumption pattern becomes important for deciding when to invest in which availability strategy..

Why traditional metrics fall short

Traditional metrics like mean time to recovery (MTTR) and other recovery time metrics have significant limitations in modern, complex systems:

• They're reactive, only measuring after failures have occurred.
• They can be skewed by outliers, with one major outage disproportionately affecting the overall metric.
• They don't account for degraded states where a system is technically operational but performing poorly.
• They focus on system internals rather than user experience.

As Nobl9 points out, “MTTR metrics are often woefully inaccurate” because variables like ticket volume, definition of start/stop times, and data quality issues significantly impact measurement. If you respond to hundreds of small, non-user-impacting tickets quickly but have one outage that takes a day to resolve, your MTTR looks great even though users experienced a significant disruption.

SLOs for high availability and fault tolerance

High availability and fault tolerance require different types of SLOs.

How Nobl9 helps with SLO management

Setting up and tracking SLOs involves numerous technical details that affect service availability. This process requires first identifying the right SLIs (the underlying metrics) and then defining SLO targets based on those indicators. Tools like Nobl9 help streamline this process.

Nobl9 enables organizations to prioritize SLIs that directly impact customer experience without requiring changes to existing monitoring infrastructure, then use those SLIs to create meaningful SLOs. The platform integrates with widely used monitoring systems like Datadog, CloudWatch, and Splunk to consolidate telemetry data, transforming it into actionable SLI data that feeds into SLO calculations.

Once SLOs are established using these SLIs, Nobl9’s Service Health Dashboard provides visual indicators of service health through color coding, with services turning yellow when SLOs have error budgets below 20% and red when they exceed error budgets. 

Color-coded visualization of service health based on error budget consumption (source)

The SLO dashboard provides more visual info about an SLO target, like the current burn rate—how fast you are depleting your allowed error budget—and the remaining error budget. It also features the SLI metrics that feed the SLO at various percentiles. This intuitive interface allows teams to identify problematic services quickly.

Detailed view showing error budget consumption, burn rates, and supporting SLIs for a service (source)

For organizations managing high-availability and fault-tolerant systems, Nobl9's hierarchical organization of services by project facilitates drilling down from high-level views to specific SLOs. This helps identify which components might need to transition from high availability to fault tolerance based on their error budget consumption patterns.

Service overview showing multiple SLOs with different targets and error budget statuses (source)

The platform's Replay feature allows testing SLO settings against historical data, enabling teams to evaluate different availability goals without waiting for new data to accumulate. This accelerates establishing appropriate SLOs, particularly for organizations just beginning their journey.

Platforms like Nobl9 help connect technical availability metrics to business outcomes by making SLOs accessible and actionable. This powers more informed decisions about availability, including when to implement high availability versus the costly fault tolerance approach for different system components.

Last thoughts

High availability and fault tolerance both aim to optimize the availability of systems but take distinct approaches, each with its own implementation requirements, costs, and performance characteristics. Throughout this article, we've seen how high availability focuses on quick recovery from failures, while fault tolerance aims to prevent service disruptions entirely.

The cloud-native landscape has made implementing both approaches more accessible, but choosing between them remains a critical decision that impacts both operational complexity and budget. Rather than applying uniform reliability across all services, modern availability engineering uses SLOs to make data-driven decisions about where each approach makes sense.

Remember that achieving high availability is a journey, not a destination. As your business evolves, so will your needs. By measuring the right metrics through SLOs and tracking error budget consumption with tools like Nobl9, organizations can identify which components truly need fault tolerance and which can operate effectively with simpler high availability implementations.