Larridin vs Span: Engineering Output, Coding Agents and AI Spend Compared

Why teams choose Larridin Developer Intelligence

• We measure the work. A lot of the data we derive is from a semantic analysis of what actually got done. Engineering Output scores each merged PR on complexity, size and quality and splits the work out as human, AI-assisted and agent authored.^[7][12]
• We act on cost. The Larridin Router scores each coding request and picks the model according to your cost and quality priorities, with budgets per engineer and per team.^[11]
• Agent effectiveness with the evidence behind it. Each coding agent session is scored on 6 dimensions, with citations to what actually happened in the session. The practices of your best AI users become the guide for the team.^[8][9]
• Agent cost and where the agents are spending their time. Sessions, work patterns, token usage and cost all sit in one view from feature work through to bug fixing, research and testing.^[8]
• Repositories ready for agents. Agent Readiness checks out each repository and gives you the shortest path to fixing what’s holding agents back.^[10]

What Span does well

Span captures the coding agent traces and links them to tickets, pull requests and production outcomes. It then runs the evaluations to assess how the engineers are guiding the agents and ranks the fixes for each repository’s agent configuration. It also provides allocation of salary and token spend to projects, DevFinOps records, developer experience surveys and Src (the AI agent that answers questions in Slack). Where it stops is in measuring how much work each change delivered and acting on the cost of each request.

The key difference: what was built, and what it cost

Scoring the work not just the flow. Span is about how work moves: velocity, cycle time, DORA and throughput.^[3] Larridin’s Engineering Output scores the amount of work delivered by each merged PR on complexity, size and quality and measures AI spend against that where the coding agent transcripts are linked.^[12]

Acting on cost rather than just tracing it. Span shows you the cost of each agent session and can recommend fixes to the agent’s environment.^[1][2] The Larridin Router acts on every request: it picks the model under your cost and quality criteria, applies per engineer and per team budgets and then compares the routed work to the pinned work on output, quality and cost.^[7][11] A model router is not shown for Span.

A simple example. Two PRs merge on the same day, same cycle time. The flow metrics treat them the same. One is a config change, the other is a new billing feature with tests. Larridin’s Engineering Output makes the difference and shows the AI spend for each.

Larridin vs Span at a glance

Similar features, different names

What data Larridin collects

Larridin Developer Intelligence reads Git metadata, coding agent session data and usage from AI tool APIs. It does not record keystrokes or take screenshots. Our SOC 2 Type 2 and SOC 3 reports, external penetration test and data flow diagram are all available via the Larridin Trust Center.^[15]

Recommendations by role

CTO

Larridin Developer Intelligence to see how much engineers and AI agents actually build rather than just how fast work moves and to drive the move to AI-native development.

VP of Engineering

Larridin Developer Intelligence to identify the agent habits of your best engineers and then turn those into team practice.

AI platform / developer experience lead

Larridin Developer Intelligence to score agent sessions with evidence, fix what’s holding the repositories back and route each request to the correct model.

CFO

Larridin to look at the AI coding spend against how much was actually built, cut the cost with the Router and have a single view of AI spend across the whole company.

Security-constrained teams

Larridin Developer Intelligence (hybrid or on-premises deployment), SOC 2 Type 2 and SOC 3 reports and a published data flow diagram.

Moving from Span

Larridin Developer Intelligence doesn’t need Span or its data to get going. It just connects to your Git hosts, your coding agents and your AI tool APIs. If you’re already running Span then run a pilot on two teams, compare what each product is showing you and what Larridin is recommending and then make your decision with the real data in front of you.

Questions to test in a pilot

1. Compare two PRs with the same cycle time. Pick a small change and a large feature that both merged in the same week. See which product shows how much work each one actually delivered.
2. Open the same agent session in both. Look at the scores, the evidence behind them and the guidance each product gives the engineer.
3. Ask what each product does in terms of cost. Find out whether it simply reports the cost of a session or whether it also has the capability to determine which model will handle the next request.
4. Ask what the change would be. After 30 days ask each vendor what they would recommend you change and what that would save.

Span alternatives: how Larridin and other tools compare

Teams evaluating Span often also compare DX, Jellyfish, LinearB, Swarmia and Weave. Each tool has its own focus, so see our side-by-side pages for Larridin vs DX, Larridin vs Jellyfish, Larridin vs Swarmia and Larridin vs Weave.

Some teams keep an existing engineering metrics tool for measures such as cycle time and add Larridin for complexity-scored output, cost per outcome and agent effectiveness. The pilot questions above work with any of these tools.

Proof: Larridin research

For measured findings across Larridin customers, see the AI Impact in Engineering Report, Q3 2026. It is Larridin’s own research and not a comparison with Span.^[16]

Not just engineering

Larridin is not limited to engineering. The same platform measures AI adoption, fluency, impact and spend across Finance, Support, Marketing, Sales and Legal, so leadership gets one view of what AI costs and what it produces across the whole enterprise.^[6][17]