Span can be useful when you need to understand AI's impact and costs within engineering. It analyzes agent traces, ties them to pull requests (PRs) and delivery outcomes, attributes AI-assisted code, and tracks AI usage and costs by team, tool, and model.
But engineering-level AI measurement isn't enterprise AI ROI. When leaders need to see AI usage, spend, proficiency, and business impact across finance, HR, sales, operations, and other departments, an engineering-focused platform stops short. For broader use cases, Larridin is the stronger alternative.
This guide compares Span with four alternatives: Larridin, LinearB, GetDX, and Faros AI.
Larridin — Enterprise AI Measurement Across Departments
Larridin is the strongest fit when the AI ROI question goes beyond engineering. Span gives engineering leaders detailed visibility into agent traces, AI-assisted code, spend, delivery, and developer experience. Larridin shows who's using AI, what it costs, how well people and agents are using it, and what value the business gets back across every department. Engineering integrations support developer measurement, while browser and desktop coverage extend visibility into non-engineering work. The platform can track spend across tokens, seat licenses, cloud model calls, and agent activity, then connect those signals to adoption, proficiency, and business outcomes.
Best For
CIOs, CFOs, CHROs, and AI transformation leaders who need enterprise-wide AI ROI across teams, tools, agents, workflows, and outcomes.
Key Limitation
Larridin doesn't replace Span's specialized agent trace analysis, PR-level AI attribution, developer surveys, or engineering cost-capitalization workflows.
LinearB — Engineering Productivity With AI Impact and Workflow Automation
LinearB is a strong Span alternative when engineering leaders need AI impact measurement alongside delivery workflow automation, AI code reviews, DORA metrics, R&D cost capitalization, and developer experience measurement. Its workflow layer can automate PR routing, approvals, policy checks, and AI code reviews.
Best For
CTOs and engineering leaders who need AI impact measurement, delivery workflow automation, DORA metrics, cost capitalization, AI code reviews, and executive ROI reporting.
Key Limitation
LinearB emphasizes engineering productivity and workflow automation. Span is more specialized in connecting agent traces and AI-assisted code to PR-level costs and delivery outcomes.
GetDX — Research-Led Developer Intelligence
GetDX is a strong Span alternative when developer experience measurement, surveys, and research-backed frameworks are the priority alongside AI impact and cost measurement. Its Core 4 approach combines DORA, SPACE, and DevEx with system data and developer feedback.
Best For
Engineering leaders who need research-backed developer experience measurement, AI impact and cost metrics, satisfaction surveys, and productivity benchmarks.
Key Limitation
GetDX is designed for research-led developer intelligence. Span is more specialized in agent trace analysis, PR-level AI attribution, and prompt-to-production observability.
Faros AI — Engineering AI ROI and Software Delivery Intelligence
Faros AI is a strong Span alternative when engineering teams need token intelligence, DORA metrics, AI impact and ROI measurement, investment tracking, and compliance-ready reporting. It connects engineering token spend to productivity, quality, and developer experience. Its credentials include SOC 2 Type II, ISO 27001, GDPR, and CSA STAR.
Best For
Engineering and platform teams, especially in regulated environments, that need DORA metrics, AI spend and ROI measurement, delivery intelligence, and formal security credentials.
Key Limitation
Faros AI emphasizes engineering-wide AI program measurement, delivery intelligence, and governance. Span is more specialized in agent traces and PR-level AI attribution.
| Feature | Larridin | Span |
|---|---|---|
| Enterprise-wide AI tracking | Yes, all departments | Engineering-focused |
| Agent observability | Yes, usage and spend layer | Yes, trace and delivery layer |
| AI-assisted code attribution | Via integrations | Yes, specialized |
| AI spend tracking | Yes, enterprise layer | Yes, engineering layer |
| DORA and delivery metrics | Via integrations | Yes |
| Non-engineering workflow tracking | Browser and desktop coverage | Not a stated focus |
| AI proficiency measurement | Yes | Engineering effectiveness and prompt-quality signals |
| Developer experience surveys | Not developer-specific | Yes |
| Shadow AI discovery | Yes | Not a stated capability |
| CFO and CHRO reporting | Yes | Engineering and finance focused |
In Our Tests… In our enterprise AI audits, engineering data often gave leaders a clear picture of agent activity, AI-assisted code, delivery impact, and coding-tool costs. The gap showed up when leadership asked the same questions outside engineering across sales, marketing, HR, finance, and operations. That data could answer the engineering-effectiveness question, but it couldn't give finance or HR a complete view of AI spend, proficiency, and outcomes across the business.
That is where Larridin pulls ahead. Span can help engineering teams prove and improve AI effectiveness, cost efficiency, and software delivery. Larridin follows AI across departments, tools, users, agents, workflows, spend, proficiency, and outcomes.
Span is strong for engineering AI effectiveness and spend measurement. It connects agent traces to PRs and shipped code, attributes AI-assisted code, tracks costs by team, tool, and model, and compares spend with delivery outcomes. Developer surveys, benchmarks, delivery metrics, and R&D capitalization add broader engineering context.
Span is focused on engineering. It doesn't provide enterprise-wide measurement of AI usage, spend, proficiency, and business impact across departments such as sales, marketing, HR, finance, and operations.
Both are engineering intelligence platforms, but they emphasize different layers. Span focuses on agent traces, AI-assisted code attribution, and linking spend to PRs and delivery outcomes. LinearB combines AI impact measurement with workflow automation, DORA metrics, AI code reviews, and cost capitalization. Choose Span for deeper prompt-to-production observability and LinearB when workflow automation matters more.
Larridin is the best fit when leaders need enterprise-wide AI ROI tracking across technical and non-technical teams. LinearB, GetDX, and Faros AI are stronger for specialized engineering productivity, developer intelligence, and software delivery needs. Among these options, Larridin is built for AI measurement across the full enterprise.
Span is a strong choice when engineering leaders need agent trace analysis, AI-assisted code attribution, AI spend measurement, and prompt-to-production visibility into software delivery.
Choose Larridin when the bigger problem is enterprise AI accountability: what AI costs, who is using it, how well they are using it, and what the business is getting back across every department.