What is our primary use case?
My main use case for
LinearB is measuring all key engineering metrics.
LinearB is designed with a user-friendly, very user-first approach and an intuitive interface that ensures every feature is easy to navigate. From day one, I have effortlessly harnessed the platform's power, streamlining my workflow without any hassle.
LinearB provides a dedicated success manager, someone who is not just there to answer my questions but to deeply understand my unique needs. The success manager works closely with me to tailor the platform's functionality to my project, ensuring I get the most out of every feature.
One of the most frustrating aspects of the previous solution we were using was the uncertainty surrounding data accuracy. With LinearB, I can put those worries to rest. The data is accurate and not processed by any proprietary machine learning model that makes backtracking data impossible. Also, the solution just works as designed, which is not that obvious. With data coming from multiple streams and a platform, it is essential that there are no disconnections or lost data. Moreover, the vendor is constantly adding new features and offering early access to them. Finally, their support is super responsive and competent.
LinearB has helped my team measure engineering performance by tracking metrics such as cycle time, deployment frequency, pull request turnaround time, and delivery efficiency. For example, we recently monitored our development workflow to identify delays in code reviews and hand-offs between developers, which helped us spot bottlenecks, improve collaboration, and deliver releases more consistently.
How has it helped my organization?
LinearB has positively impacted my organization by improving engineering visibility, team collaboration, and delivery efficiency. I have seen better tracking of development progress, faster identification of workflow bottlenecks, and improved pull request turnaround times. It has also helped reduce delays, make more informed decisions, and keep releases more predictable. Overall, it has improved team productivity and made our development process more organized and efficient.
I can provide a specific example, as I have seen our development efficiency improve by reducing delays in code reviews and identifying workflow bottlenecks earlier. For example, my team has seen roughly a 30% improvement in pull request turnaround time and better coordination between developers.
What is most valuable?
The best features of LinearB are its engineering metrics dashboard, cycle time tracking, deployment frequency monitoring, pull request analytics, and workflow automation.
These features stand out because they give us clear visibility into team performance, help identify bottlenecks early, improve code review turnaround, and make development processes more efficient. I particularly appreciate how it turns engineering data into actionable insights that help the team make better decisions and deliver projects faster.
The user interface is very intuitive and user-friendly, allowing us to navigate through all the features very easily.
Regarding LinearB's AI capabilities, I think its governance and security are important strengths because they help organizations such as mine maintain better control over engineering data. I appreciate the focus on data privacy, access controls, and transparency overall.
I find that the AI capabilities are generally accurate and reliable, providing useful insights into engineering performance and workflow efficiency.
What needs improvement?
I believe LinearB could be improved by offering more flexible dashboard customization, deeper integration with third-party development tools, and more detailed reporting.
I want to see more flexibility to customize dashboards based on team roles, project priorities, and individual preferences. Also, better integrations with tools such as Jira, GitHub, and Slack would make workflows much smoother. For reporting, more detailed insights into cycle time, deployment frequency, code review delays, and individual team performance would help us identify bottlenecks, track progress, and make better decisions.
For how long have I used the solution?
I have been using LinearB for around five years, although I am not sure of the exact duration.
What do I think about the stability of the solution?
LinearB has been stable and very reliable in my experience. I have not encountered major performance disruptions or availability issues, and it consistently provides the engineering metrics and workflow insights I need.
What do I think about the scalability of the solution?
I have been able to scale LinearB well, and it can support my organization's growing engineering needs. As my team and development workflows expand, it continues to provide useful performance metrics, reporting, and workflow visibility without major difficulties.
How are customer service and support?
My experience with the support has been very positive because the support has been responsive, professional, and helpful. Whenever I have questions or need assistance, the team provides clear guidance and works to resolve issues promptly.
I would rate the customer support a nine out of ten.
Which solution did I use previously and why did I switch?
Previously, I relied more on manual tracking spreadsheets and basic reporting through my existing development tools. I moved to LinearB because I needed better visibility into engineering performance, more accurate delivery metrics, and automated workflow insights.
How was the initial setup?
Deploying LinearB in my environment was relatively easy and straightforward. The setup process was smooth, and connecting it with our existing development tools did not require significant technical effort.
My experience with the configuration process was fair and straightforward. I never encountered any hiccups along the way. Setting up dashboards, connecting repositories, and adjusting metrics to match our engineering workflows was relatively easy.
LinearB integrates very seamlessly with our AWS environment and fits into our existing development workflows.
What was our ROI?
I have not really needed fewer employees, but overall, there has been a positive return on investment from LinearB, especially by improving code review turnaround times and identifying workflow bottlenecks. My team has become more productive, with around a 30% improvement in pull request turnaround time, saving several hours of development time, specifically four to five hours each week. It has also reduced manual tracking and improved delivery consistency, making the investments worthwhile.
What's my experience with pricing, setup cost, and licensing?
The procurement process for LinearB was smooth and straightforward because the agents were very professional. They provided the pricing information and licensing options, which were clear enough to support my evaluation, and communication during the process was helpful.
The metering and billing experience with LinearB was generally smooth and manageable. The pricing structure is reasonably clear, making it easier to understand and plan my expenses.
My experience with pricing, setup cost, and licensing was generally reasonable and manageable. The setup did not require significant additional investment, and the licensing structure was straightforward to understand.
Which other solutions did I evaluate?
Before choosing LinearB, I evaluated other options such as
GitLab and
Jellyfish, specifically
GitLab's value stream analytics.
What other advice do I have?
On a scale of 1 to 5, I would rate LinearB a five out of five.
I chose five out of five because LinearB provides excellent visibility into our engineering performance, helps us identify workflow bottlenecks, and improves team productivity.
I would recommend LinearB to engineering teams looking to improve their productivity, delivery efficiency, and workflow visibility. My advice is to take time to configure the dashboards, connect your development tools, and identify the key metrics that matter most to your team. Make use of its automation and reporting features to identify bottlenecks and track improvements.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
