Glue vs Swimm

By Arjun Mehta, Principal Engineer at Glue

LinearB has become one of the most popular engineering team productivity tools for measuring and improving development velocity. If you are comparing it to Glue, the distinction is fundamental: LinearB measures how fast your team works. Glue helps your team understand what they are working on.

I have tracked engineering metrics obsessively across three organizations. Metrics matter. But metrics without context are just numbers, and context is what most engineering metrics platforms lack.

LinearB was founded in 2019 to bring data-driven management to software engineering. It connects to Git and CI/CD pipelines, measures DORA metrics (deployment frequency, lead time, change failure rate, mean time to recovery), and provides engineering leaders with visibility into team productivity. It competes with Jellyfish, Pluralsight Flow, and similar platforms.

Glue was built to solve a different problem: giving teams visibility into what the codebase contains, not how fast the team is working. LinearB measures the speed and efficiency of the development process. Glue provides the understanding of the system that informs what enters that process.

The distinction matters because speed without direction is just velocity. An engineering team can have excellent DORA metrics while building features on top of a codebase they do not fully understand. LinearB would show you green dashboards. Glue would show you the hidden dependencies and technical debt that make those green dashboards misleading.

LinearB provides engineering productivity measurement with genuine depth.

DORA metrics. LinearB tracks the four DORA metrics (deployment frequency, lead time for changes, change failure rate, mean time to recovery) that have become the industry standard for engineering performance. These metrics provide an evidence-based framework for improvement.

Cycle time analysis. LinearB breaks down the development lifecycle into stages: coding, review, merge, deploy. This granularity helps identify where bottlenecks exist. If PR review is the slowest stage, you know where to focus improvement efforts.

Team benchmarking. LinearB provides benchmarks against industry data so you can assess whether your team's metrics are strong, average, or weak relative to peers. This external reference point is useful for setting realistic improvement goals.

Workflow automation. LinearB can automate PR routing, review assignments, and status notifications. These automations reduce the manual overhead that slows development workflows.

Investment tracking. LinearB categorizes engineering work into categories like new features, maintenance, and tech debt. This gives engineering leadership visibility into where capacity is going, which helps in budget and resource conversations.

LinearB tells you how fast code moves through the pipeline. Glue tells you what the code is, how it connects, and what working with it actually involves.

Understanding vs. measurement. LinearB measures the development process. Glue understands the codebase itself. When an engineering leader needs to explain why a module takes three times longer to modify than another, LinearB shows the cycle time difference. Glue explains the why: tangled dependencies, concentrated knowledge, missing tests, accumulated debt.

Product team access. LinearB's audience is engineering leadership. Product managers rarely interact with it because the metrics are engineering-centric. Glue is built for the cross-functional team: PMs who need to understand what a feature involves, EMs who need to visualize technical debt, and CTOs who need feature inventories. For engineering leaders, having both measurement and understanding provides a more complete picture.

Estimation context. LinearB can tell you historical velocity, but it cannot tell you what a specific future project involves at the code level. Glue provides the codebase context that makes estimation more accurate: which modules will be affected, what dependencies exist, and where code health metrics indicate potential complications.

Feature-level intelligence. LinearB operates at the PR and deployment level. Glue operates at the feature and module level. When a PM asks "what would adding a referral program involve?", LinearB has no answer. Glue can identify the affected files, map the dependencies, and surface the technical debt in the relevant area.

Choose LinearB when your primary need is measuring and improving engineering process efficiency. If you want to track DORA metrics, identify pipeline bottlenecks, benchmark against industry standards, and automate development workflows, LinearB provides the measurement infrastructure.

LinearB is particularly valuable for engineering organizations that need to justify headcount or demonstrate productivity improvements to leadership. The ability to show that cycle time dropped by 30% or deployment frequency doubled provides the data-backed narrative that budget conversations require. The benchmarking feature adds external context: "Our lead time is in the top quartile compared to similar-sized teams" is a powerful statement in a board presentation.

For teams practicing continuous improvement, LinearB's historical trend data shows whether process changes are working. If you introduced a new code review policy and want to measure its impact on review cycle time, LinearB tracks that automatically.

LinearB is the right choice when the goal is process optimization and the audience is engineering leadership.

Choose Glue when your primary need is understanding the codebase and bridging the product-engineering visibility gap. If PMs cannot see what the system contains, if estimates lack codebase context, if roadmaps slip because of hidden complexity, Glue addresses the understanding problem.

Glue is the right choice when the goal is informed decision-making across the product and engineering organization.

Absolutely. LinearB measures the efficiency of the development process. Glue provides the codebase intelligence that informs what goes into that process. Together, they cover both the "how fast" and the "what and why" dimensions of engineering work.

An engineering organization using both would track delivery metrics with LinearB while using Glue to provide codebase context for planning conversations. LinearB shows you the speed. Glue shows you the terrain.

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Is Glue an engineering metrics tool?

No. Glue is an AI codebase intelligence platform, not an engineering metrics platform. It does not track cycle time, deployment frequency, PR throughput, or DORA metrics. It provides understanding of the codebase itself: what features exist, how modules connect, where technical debt lives, and what a proposed change involves. For engineering metrics, tools like LinearB, Jellyfish, and Pluralsight Flow are purpose-built.

Can LinearB help with estimation accuracy?

LinearB can provide historical velocity data and cycle time benchmarks, which offer a reference point for estimation. But it cannot tell you what a specific project involves at the code level: which modules are affected, what dependencies exist, or where complexity hides. For estimation accuracy, you need both process metrics (LinearB) and codebase intelligence (Glue). The combination provides the historical context and the system-specific context that together produce better estimates.

What engineering team productivity tools do product teams need?

Product teams benefit from three categories of engineering visibility: (1) project management tools (Jira, Linear) for tracking execution, (2) engineering metrics tools (LinearB, Jellyfish) for understanding delivery velocity, and (3) codebase intelligence tools (Glue) for understanding the system being built. Most organizations have the first two but lack the third, which is why product teams often feel disconnected from engineering reality. Adding codebase intelligence closes the loop.