Deployment Frequency vs. Success Rate: Key Differences

Deployment frequency measures how often code is pushed to production, while success rate tracks the percentage of deployments that work without needing fixes. Both are essential for effective DevOps but focus on different aspects: speed versus reliability. High-performing teams excel in balancing these metrics, deploying changes daily with a low failure rate (0–15%). Ignoring one can lead to inefficiencies or delays.

To improve both:

• Use automation in CI/CD pipelines.
• Implement smaller, frequent updates.
• Leverage feature flags for safer rollouts.
• Prioritise testing and monitoring.

Balancing these metrics ensures faster delivery and stable performance, improving team output and customer satisfaction.

What Is Deployment Frequency

Definition and Why It Matters

Deployment frequency refers to how often a team successfully deploys code to production [2]. It’s a key DORA metric that reflects a team’s ability to adapt quickly to customer needs and market demands [2][3].

This metric carries real weight in the business world. Top-performing DevOps teams deploy code 46 times more often than their lower-performing counterparts [5]. Tech giants like Amazon and Airbnb push this even further, deploying over 125,000 times each day [6]. Such rapid deployment cycles allow these companies to iterate quickly and maintain competitive advantages.

Industry leaders often highlight the importance of deployment frequency. Saketh BSV, co-founder of Perpule and an angel investor, puts it succinctly:

One of the best indicators to identify high-performing engineering teams is deploying frequency to production. Optimising to be able to deploy daily is extremely powerful and can be a moat for many startups. [6]

Frequent deployments come with major benefits. Teams can deliver features and fixes faster, while shorter feedback loops provide real-time insights to refine products [4][9]. Regular deployments also help identify and address issues earlier in development, reducing the risk of significant failures [10].

That said, not every industry can adopt a high deployment frequency. Some sectors, like electric reliability, demand a slower, more cautious approach. Adriana Fiorante, Marketing Director at Volta Insite, explains:

We're a bit different from your normal software company, and our deployment rate is slower, but for good reasons. The application (electric reliability) in our company is more complicated, so deployments need more thought than you might see with a typical software company. [6]

How to Track and Improve Deployment Frequency

To leverage deployment frequency effectively, you first need to measure it. This involves counting the number of successful production deployments over a set time period [2]. The formula is simple:

Number of change deployments ÷ time = Deployments per unit of time [4].

DORA benchmarks help categorise performance levels:

Currently, around 19% of teams achieve Elite performance, while 22% fall into the High category [8]. Instead of fixating on exact numbers, grouping results into these broader categories often provides better context [4].

Automation is a game-changer for tracking and improving deployment frequency. Platforms like GitLab offer DORA metrics APIs for advanced tracking, while tools like Jira, GitHub, and Jenkins can monitor build activity over time [7][8].

To improve deployment frequency, organisations can adopt several strategies:

• Implement CI/CD pipelines to automate the build, test, and deployment processes.
• Break down large releases into smaller, less complex updates to minimise risks.
• Use feature flags to deploy code without immediately releasing new features.
• Automate testing to catch issues early.
• Adopt trunk-based development for frequent, smaller code merges.
• Utilise infrastructure as code (IaC) to streamline deployment processes [2].

As Sandeep Parikh, a DevRel Engineer, points out:

If you're automating deployment operations, it means you're speeding up your ability to deploy software regularly. And if we can get the automation part right, it can help teams ship fewer broken services. [6]

What Is Success Rate

Definition and Why It Matters

In the world of DevOps, success rate is a key metric that evaluates deployment reliability through the change failure rate (CFR) - the percentage of deployments that need immediate fixes after going live [1]. By understanding your CFR, you can accurately gauge performance and identify areas for improvement.

A low CFR not only allows teams to focus on delivering new features without constantly putting out fires, but it also ensures system stability and builds user confidence [11]. Tracking success rate is equally important for confirming that new code releases meet security standards [1].

Top-performing teams, often referred to as elite teams, maintain a CFR of 0–15%. In contrast, less effective teams can experience rates between 46–60%. Elite teams also restore services in under an hour, while others may take up to a week [12][1].

How to Measure and Improve Success Rate

To calculate your CFR, divide the number of failed deployments by the total number of deployments over a specific period, then multiply by 100 [11].

The DORA framework provides benchmarks to categorise performance levels:

Real-world examples highlight how focusing on success rate can lead to substantial gains. For instance, a Fortune 500 financial services company boosted deployment frequency by 400% and cut production defects by 28% in just 18 months [15]. Similarly, a fintech firm slashed its mean time to recovery from 6 hours to just 20 minutes by adopting practices like distributed tracing, automated canary deployments, chaos engineering, and automated runbooks [15].

To improve your success rate and tackle the root causes of deployment failures, consider these strategies:

• Enhanced Testing Practices: Incorporate automated unit, integration, and end-to-end tests throughout your CI/CD pipeline to catch issues before they reach production [16].
• Infrastructure as Code (IaC): Automate infrastructure management to eliminate misconfigurations and ensure consistent environments, reducing human error [16].
• Advanced Deployment Strategies: Use methods like canary, blue-green, and rolling deployments to test changes with a smaller user group before a full rollout, minimising the risk of failure [16].
• Feature Flags: Separate deployments from releases, enabling safer testing and phased rollouts. If problems arise, features can be turned off without rolling back the entire deployment [16].
• Smaller, Frequent Changes: Deploying smaller updates makes testing more manageable and failures less likely. These smaller changes are easier to track and fix quickly if issues occur [16].

For example, a mid-size SaaS company reduced its mean time to restore from 45 minutes to under 18 minutes and cut customer-impacting incidents by 37% by implementing automated deployment pipelines with built-in monitoring and rollback features [15].

Main Differences Between Deployment Frequency and Success Rate

Side-by-Side Comparison

Deployment frequency and success rate are two key metrics in DevOps, each addressing a different aspect of software delivery. Understanding how they differ can help teams decide where to focus their improvement efforts.

The core distinction lies in their focus. As Forsgren, Humble, and Gene explain:

We settled on deployment frequency as a proxy for batch size since it is easy to measure and typically has low variability. By 'deployment,' we mean a software deployment to production or an app store. [17]

While deployment frequency highlights how quickly a team can deliver changes, success rate ensures those changes operate as intended. Focusing solely on one metric can lead to imbalances that undermine overall performance.

Risks of Focusing on Just One Metric

Concentrating only on deployment frequency without ensuring quality can lead to failures and accumulating technical debt [18]. For instance, products delayed by six months can earn 33% less profit over five years [18]. Developers may spend an average of 13.5 hours per week fixing issues instead of building new features, draining productivity [18].

On the other hand, prioritising success rate while ignoring deployment frequency can result in overly cautious workflows. Lengthy approval processes and extensive manual testing slow down innovation, causing missed opportunities. Developers may also struggle to get timely feedback, which can reduce their sense of ownership and create a culture that avoids risk. This reluctance to experiment can stifle progress [18].

Microsoft's research highlights the importance of balance:

frequent small deployments are preferable to infrequent large deployments [18]

Small, incremental changes are easier to address when problems arise [18]. This approach allows teams to maintain both speed and stability, avoiding the pitfalls of focusing too heavily on either metric.

Additionally, 86% of respondents agree that quickly moving new software into production is vital for their company [6]. However, speed must be paired with reliability to sustain a competitive edge. Teams that strike this balance report significant benefits, with DevOps practices improving teamwork quality by 75.6% [18].

Ultimately, balancing deployment frequency and success rate is essential for achieving both rapid delivery and dependable quality.

How to Balance Deployment Frequency and Success Rate

Practical Balancing Methods

Finding the right balance between deployment frequency and success rate means adopting strategies that promote both speed and reliability. Instead of seeing these goals as competing, it's about integrating practices that naturally support both. These methods work seamlessly within existing CI/CD processes to maintain stability without slowing things down.

Start with smaller, incremental changes to minimise risks while keeping deployment cycles fast. Breaking large features into smaller, manageable parts makes it easier to test, deploy, and roll back if necessary [13][22]. This approach helps teams deploy frequently without jeopardising system stability.

Automate testing throughout your CI/CD pipeline to catch problems early. Automated testing reduces human error and ensures consistent quality [7]. This safety net allows for more frequent deployments without increasing the likelihood of failures.

Use feature flags to separate code deployment from feature activation, adding an extra layer of control [2].

In fact, automating CI/CD pipelines has been shown to increase deployment frequency by 50% while cutting change failure rates by 60% [21].

Set up comprehensive monitoring and observability to identify and address issues quickly. Effective monitoring means faster detection and resolution of problems [2], which supports both speed and stability.

Research highlights the importance of DORA metrics as a baseline for setting goals and tracking progress [13]. By aggregating data on changes, incidents, and deployments, teams can measure their performance against these metrics [13].

Adopt trunk-based development to keep your codebase deployable with frequent, small, and low-risk changes [2].

By implementing these practices, organisations can create a DevOps environment that supports both high deployment speed and strong system reliability.

How Hokstad Consulting Can Help

Hokstad Consulting specialises in helping UK businesses balance deployment frequency and success rate through tailored DevOps transformation and cloud optimisation services. Their strategies ensure organisations can achieve both speed and stability without compromise, all while delivering measurable cost savings.

Their DevOps Transformation Services focus on setting up automated CI/CD pipelines that remove manual bottlenecks and reduce errors [19]. Clients have reported deployment speeds improving by up to 75% and errors dropping by 90% [19].

With Cloud Cost Engineering, Hokstad Consulting helps businesses optimise infrastructure costs without sacrificing performance. Through techniques like right-sizing, automation, and smart resource allocation, companies can save over £50,000 annually [19]. These savings can then be reinvested in tools for better monitoring and testing, further supporting frequent and reliable deployments.

By leveraging Infrastructure as Code and advanced monitoring tools, Hokstad Consulting ensures infrastructure changes follow strict processes, reducing deployment failures and enabling more consistent updates [19].

Their customised approach recognises that every organisation is different. Hokstad Consulting works closely with clients to set DORA metric targets that reflect their specific industry, scale, and risk tolerance [20]. This ensures that improvements align with business goals and customer expectations.

To ensure long-term success, Hokstad Consulting offers an ongoing support model. This includes performance optimisation, security audits, and continuous monitoring, helping businesses maintain their improvements over time instead of seeing them as one-off gains.

For UK organisations looking to improve their DevOps performance, Hokstad Consulting provides flexible engagement options, including a No Savings, No Fee model for cost optimisation projects. This removes upfront financial risks, allowing businesses to invest in balancing their deployment metrics with confidence.

With their combination of technical expertise, cost-saving strategies, and ongoing support, Hokstad Consulting is a trusted partner for organisations aiming to achieve top-tier DevOps performance while keeping infrastructure costs in check.

DORA Metrics Defined

Conclusion

Striking the right balance between deployment frequency and success rate is crucial for achieving strong DevOps performance. Jesse Sumrak from LaunchDarkly puts it well:

Deployment frequency isn't just about speed - it's about creating a sustainable, low-risk approach to software delivery [2].

This equilibrium allows organisations to deliver value swiftly while ensuring the stability that customers rely on. Data supports this approach, showing that high-performing teams maintain change failure rates within the 0–15% range [1], proving that speed and reliability can go hand in hand.

While deployment speed is undeniably important for business success, it must be paired with dependable delivery processes. DORA metrics provide a solid framework for teams to achieve this balance. By tracking deployment frequency alongside change failure rates and recovery times, teams gain a comprehensive view of their DevOps performance, focusing on both speed and quality rather than prioritising one over the other [13].

As noted earlier, high-performing teams not only release updates faster but also recover from issues in under an hour [1]. Comparatively, lower-performing teams may take up to a week to resolve failures [1]. This resilience is built on practices like automated testing, feature flags, robust monitoring, and smaller, incremental deployments. These methods naturally encourage frequent releases without sacrificing system stability.

For UK businesses, adopting streamlined CI/CD practices and rigorous monitoring can provide a competitive edge in today’s fast-paced digital environment. With 75% of tech leaders identifying deployment frequency as a key measure of DevOps success [23], achieving this balance is more critical than ever.

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