Skip to content
Get Pricing Try Now

DX (GetDX) alternatives and competitors for engineering intelligence

DX (also written GetDX) is an engineering intelligence platform, now part of Atlassian.[4] This page compares eight tools teams evaluate alongside it. Larridin publishes this page, and Larridin DevX is our pick for AI-native engineering teams. Every claim links to a public source.

By Denis Scott, CMO Updated November 9, 2026 Competitor facts reviewed November 5 to 9, 2026

Get a pilot plan See the alternatives

Quick answer

For engineering teams moving to AI-native development, Larridin DevX is the strongest DX alternative. Larridin is an AI impact intelligence platform. It measures velocity, quality, efficiency and the actual output of your engineers and your AI agents, then helps you act: it spreads the habits of your best AI users and cuts AI cost with the Larridin Router.[7]

If your need is narrower, such as delivery forecasting, workflow automation or allocation reporting, the Best for column below shows where each tool fits.

What DX does well

DX built the DX Core 4 framework and publishes a quarterly State of AI Impact report from more than 500 organizations.[1][3] Its core product is developer experience surveys and the Developer Experience Index, with industry benchmarks.[2] It has since added reporting on AI-written code, coding-agent sessions and AI costs.[5][6] Its roots are in how developers experience their work, not in measuring and optimizing what AI agents produce.

What should you look for in an alternative?

Seven questions separate these tools in practice.

  • How is output measured? Pull request counts, complexity-weighted output, or something else.
  • How is AI code identified? From tool telemetry, git data, agent markers or sessions.
  • Is AI spend tied to outcomes? Spend per team and tool, or spend per unit of delivered work.
  • Does it help you act? Dashboards and reports, or recommendations and tools that change cost and results, such as a model router.
  • Does it measure developer experience? Built-in surveys, or none.
  • Does it cover more than engineering? Engineering only, or AI across the rest of the company too.
  • Where does your data live? Cloud, single-tenant, your own cloud account or on-premises.

Top alternatives to consider

"Not shown" means we found no public evidence either way on the pages we read.

DX alternatives: who each tool fits, its public positioning and AI measurement, and developer experience surveys
ToolBest forPublic positioning and AI measurementDeveloper experience surveys
Larridin DevX (our pick for AI-native teams; Larridin publishes this page) AI-native engineering teams that want to measure what engineers and AI agents produce, act on it and cut AI cost, with AI across the rest of the company in the same platform.[7] Built for AI-native engineering teams. Engineers and AI agents working together across the SDLC. Output scores each merged pull request on complexity, size and other factors, and AI spend is measured against the Output of each pull request (so a large feature is worth more than a typo fix). The Larridin Router selects the model for each coding request in order to keep costs down.[7][8] Not a developer survey tool. Measures how engineers work with AI from telemetry.[7]
Jellyfish Teams that need allocation reporting on where engineering time goes, alongside AI impact.[24] AI Impact tracks adoption, usage, spend and outcomes across AI tools, including GitHub Copilot, Cursor, Claude Code, Amazon Q, Gemini Code Assist, Windsurf, CodeRabbit, Devin, Copilot Agent and Google Jules.[9] Since October 2026 it also attributes AI spend to initiatives and deliverables and shows autonomous agent work beside human work.[10] Yes. Developer sentiment is part of AI Impact.[11]
LinearB Teams that want workflow automation and AI code reviews alongside delivery metrics.[12] Measures AI impact across 50+ AI tools, with and without AI, on cycle time and DORA metrics. Also offers gitStream workflow automation and AI code reviews.[12] Yes. Developer surveys in Slack.[12]
Swarmia Teams comparing AI-assisted and non-assisted pull requests, with a developer experience survey program.[13] Compares AI-assisted pull requests with non-assisted ones. Tracks GitHub Copilot, Cursor, Claude Code, Codex and Devin. Shows AI cost per team and per person, calculated from token usage, and does not include seat costs. States that it does not estimate the share of AI-written lines.[13] Yes. Developer experience surveys are one of its four modules.[13]
Faros AI Teams that want token spend tied to outcomes and model routes tested against their own work.[14] Positions as a token engineering platform. Ties token spend to sessions, pull requests, teams and outcomes, and evaluates model routes against your own completed work.[14] Not shown.
Span Teams that want AI-generated code detection and agent traces linked to code and pull requests.[16] Links coding-agent traces to code and pull requests and to downstream outcomes.[16] Its AI Effectiveness product measures AI adoption, usage and spend across tools and compares tools.[15] Yes. Developer experience surveys and pulses in Slack.[15][17]
Weave (also called Workweave) Teams that want an estimate of expert hours per pull request and an open-source model router.[18][21] Estimates how long each pull request would take an expert engineer. Splits each pull request between human and AI using API data, git notes, co-author trailers or activity matching.[18][19] Also offers an open-source model router.[21] Lists developer feedback alongside DORA and SPACE metrics.[20] A survey product is Not shown.
Allstacks Teams that want delivery forecasting and risk detection.[22] Focuses on delivery risk detection and forecasting, with agents that investigate initiatives. Correlates AI tool adoption with cycle time, defect rates and throughput.[22] Reads AI-generated code from GitHub Copilot, Cursor and Claude.[23] Yes. Pulse surveys combined with delivery data.[22]

Head-to-head: Larridin vs GetDX

Larridin DevX is built for engineering teams moving to an AI-native software development lifecycle, and it covers telemetry and optimization across the whole lifecycle. DX answers "How are our developers doing?" Larridin answers "What are our engineers and AI agents producing, what is it costing, and what should we change?"[7]

  • Where the data comes from. Larridin starts with the work: a semantic understanding of the code in each pull request, the sessions of your coding agents and the other tools where engineering work takes place. DX starts with the developer: developer experience research and surveys, as well as engineering system metrics.[7]
  • What you can do with the answer. Larridin is built for action: recommendations to improve AI fluency and AI coding outcomes, habits from your best AI users that other teams can adopt, and the Larridin Router to save money on coding requests. DX centers on dashboards and reports. Its Model fit report, still in beta, suggests a different model after a session has finished, and DX has no router.[7]
  • Cost. DX divides AI spend by the number of merged pull requests, so a pull request that fixes a typo counts the same as one that ships a new feature. Larridin measures AI spend against the Output of each pull request, so teams that ship fewer but larger changes are not penalized.[6][7]
  • Scope. Larridin also measures AI adoption and workflows across the rest of the company, in the same platform. DX is built for software engineering teams.[7]

For the full comparison, see Larridin vs DX (Atlassian).

FAQ

What is DX (GetDX)?

DX (also written GetDX) is an engineering intelligence platform, now part of Atlassian. It combines developer experience surveys with engineering data, and it reports on AI-written code and AI costs.[2][4][5][6]

What is the best DX alternative for AI-native engineering teams?

Larridin DevX. It measures what your engineers and AI agents actually produce, recommends what to change and reduces cost with the Larridin Router. Larridin publishes this page; the table above shows the other options and who each one fits.[7]

What tools compete with DX?

Teams commonly evaluate Larridin, Jellyfish, LinearB, Swarmia, Faros AI, Span, Weave and Allstacks alongside DX. They differ in how they measure output, how they identify AI code and whether they help you act on the results. See the table above.

Why do teams look for DX alternatives?

The reasons differ. Some teams want AI spend measured against the output of each pull request. Others want delivery forecasting, allocation reporting, workflow automation or a model router. The Best for column shows where each tool fits.

Do DX alternatives include developer experience surveys?

Jellyfish, LinearB, Swarmia, Span and Allstacks document survey or sentiment features. For Faros AI, a survey product is Not shown. Weave lists developer feedback, but a survey product is Not shown. Larridin is not a survey tool; it measures how engineers work with AI from telemetry.[7][11][12][13][15][20][22]

Does DX have a router?

No. DX's Model fit report, in beta, suggests a different model after a session. The Larridin Router selects the model for each coding request in order to save cost.[7]

Can we switch from DX to Larridin?

Yes. Larridin connects directly to your Git hosts, coding agents and AI tool APIs, so you can run a pilot on two teams with no DX data. If you run developer experience surveys in DX, you can keep them; Larridin covers what surveys can't see.[7]

Sources and method

Written by Larridin using each vendor's public website and documentation. This is a documentation review, not a hands-on benchmark. Features change. Where a vendor's pages do not describe something, we write "Not shown" and do not treat it as missing. Competitor facts were reviewed on November 5, 2026, and Jellyfish and Span were rechecked on November 9, 2026.

To report an error, email support@larridin.com; we correct pages within 3 business days.

  1. DX Core 4: docs.getdx.com/dx-core-4/
  2. DX Developer Experience Index: getdx.com/research/the-one-number-you-need-to-increase-roi-per-engineer/
  3. DX State of AI Impact in Engineering, Q3 2026: getdx.com/report/state-of-ai-impact-in-engineering-q3-report/
  4. Atlassian acquires DX: www.atlassian.com/blog/company-news/atlassian-acquires-dx
  5. DX AI Code Insights: getdx.com/blog/introducing-ai-code-insights/
  6. DX AI cost management: docs.getdx.com/reports/ai-cost-management/
  7. Larridin vs DX (Atlassian): larridin.com/compare/larridin-vs-dx/
  8. Larridin Output methodology: larridin.com/developer-intelligence/methodology/
  9. Jellyfish AI Impact: jellyfish.co/platform/jellyfish-ai-impact/
  10. Jellyfish, AI-native SDLC release (October 7, 2026): www.prnewswire.com/news-releases/jellyfish-advances-developer-and-agent-productivity-insights-for-the-ai-native-sdlc-302901000.html
  11. Jellyfish, new in AI Impact (November 2025): jellyfish.co/blog/new-in-ai-impact-agent-developer-insights-executive-reporting/
  12. LinearB: linearb.io/
  13. Swarmia, What is Swarmia: www.swarmia.com/answers/
  14. Faros AI platform: www.faros.ai/platform
  15. Span products and use cases: help.span.app/articles/7646273671-span-products-and-use-cases
  16. Span, AI Impact Report: www.span.app/blog/introducing-ai-impact-report
  17. Span developer experience: www.span.app/platform/developer-experience
  18. Weave AI output attribution: docs.weaveos.com/analysis/ai-output-attribution
  19. Weave engineering intelligence: weaveos.com/products/engineering-intelligence
  20. Weave homepage: weaveos.com/
  21. Weave Router: weaveos.com/products/router
  22. Allstacks software engineering intelligence: www.allstacks.com/product/software-engineering-intelligence
  23. Allstacks integrations: www.allstacks.com/integrations
  24. Jellyfish, Work Allocations Model: jellyfish.co/blog/work-allocations-model-for-efficient-engineering-teams/

Next step

Get a pilot plan built around the one number your leadership wants to see.

Get a pilot plan