Weave vs Swarmia: which engineering intelligence platform is right for your team?
Both Weave and Swarmia help engineering leaders measure productivity and AI impact. The difference is depth. Weave reads the work itself, using LLMs and machine learning on every PR, review, and deploy. Swarmia builds flow metrics from Git and issue-tracker data, paired with surveys. Here's the full breakdown.
The short version
Weave measures human and AI contributions directly from code, across every PR, review, and deploy, normalized into a single output unit calibrated to expert benchmarks. Swarmia builds team-level flow metrics from Git and issue-tracker data, paired with developer surveys.
Teams typically choose Weave when they want objective, code-level measurement of engineering output and AI ROI, including fair per-engineer visibility. Teams typically choose Swarmia when team-level flow metrics, working agreements, and Slack feedback loops are the priority.
At a glance
How Weave and Swarmia compare
Compare
Weave
Swarmia (swarmia.com)
Core approach
LLMs + domain-specific ML read the code itself
Flow metrics from Git and issue-tracker data, plus surveys
AI vs. human attribution
Built in: every contribution attributed at the code level
AI adoption and cost tracking with delivery-metric splits
Measurement unit
Normalized units of work, calibrated to expert benchmarks, not line counts
DORA, cycle time, and investment balance
Individual visibility
Per-engineer output, benchmarked fairly on calibrated units
Team-level by design; limited individual measurement
Agent observability
Purpose-built observability for autonomous coding agents
Not a core focus
Pricing
Free Starter; $50/engineer Pro; custom Enterprise
Free under 10 devs; Lite €20 and Standard €39 per dev/mo; custom Enterprise
AI agent for insights
Wooly: ask anything about your engineering org
Swarmia AI and MCP server
Finance reporting
Dev FinOps built on measured output
Software capitalization reports
Compliance
SOC 2 Type II, GDPR, HIPAA, SSO/OIDC, SCIM
SOC 2 Type 2, twice-yearly security audits, SSO
Scale
500+ organizations incl. Fortune 100; 2M+ PRs analyzed; 20,000+ engineers
What is Weave?
Weave is the engineering intelligence platform for the AI era. It uses LLMs and domain-specific machine learning to understand engineering work at the source: the code itself. It normalizes that work into a single unit of output, calibrated to expert benchmarks rather than line counts or story points. Because Weave reads the work directly, it can tell you things flow metrics can't: how much of your output was written by AI versus humans, which AI tools are actually helping your team ship faster, and whether AI is affecting code quality and review load. Weave is trusted by 500+ organizations, from seed-stage startups to Fortune 100 companies.
What is Swarmia?
Swarmia (swarmia.com) is an engineering intelligence platform that builds team-level flow metrics from Git and issue-tracker data: DORA, cycle time, investment balance, and PR flow, paired with developer experience surveys and Slack-based working agreements. It also tracks AI tool adoption and cost, and offers software capitalization reporting. Swarmia is a well-designed, self-serve product used by teams like Miro, Bolt, and Trustpilot, and was named a Leader in the 2026 Gartner Magic Quadrant. The question is whether flow metrics built on Git metadata can still describe engineering work now that AI writes a growing share of the code.
The key difference
Measuring the work vs. measuring the flow
Swarmia's approach
Swarmia measures how work flows: PR cycle times, DORA metrics, and investment balance, built from Git and issue-tracker metadata and rounded out with surveys. That helps teams improve their process. But flow metrics stop at the surface of the work: they can't tell you what the code accomplished, how good it was, or whether a human or an AI agent wrote it.
Weave's approach
Weave analyzes every PR, review, and deploy as it happens. LLMs and domain-specific ML, tuned to your codebase, evaluate the actual engineering work, attribute it to humans or AI, and normalize it into a benchmarked unit of output. Flow tells you how smoothly work moved; Weave tells you how much real work was done, and who or what did it.
If your team ships with Cursor, Claude Code, Copilot, or autonomous agents, PRs are getting faster and more numerous everywhere. The question that matters now is what all that motion actually produced, and that's a question only code-level measurement can answer.
Why Weave
Why teams choose Weave over Swarmia
Output, not just flow
Cycle time and DORA describe how smoothly work moves. Weave measures what came out: normalized units of real engineering work, calibrated to expert benchmarks, comparable across teams and time.
Attribution, not just adoption
Swarmia tracks which AI tools are used and what they cost. Weave attributes the code itself, per tool and per contribution, so you know what AI actually produced, not just that engineers are using it.
Fair individual visibility
Swarmia is team-level by design, partly because raw Git metrics are unfair to individuals. Weave's calibrated output units solve the fairness problem directly, so you can see per-engineer output without measuring the wrong things.
Agent-ready by design
Autonomous agents don't answer surveys or follow working agreements. Weave's agent observability measures their work the same way it measures human work, so your metrics don't go dark as agents scale.
Ask your org anything
Wooly, Weave's AI engineering agent, answers questions about your engineering org in plain language, from "which team got the most out of Claude Code last quarter?" to "where is AI adding review burden?"
Enterprise-grade from the start
SOC 2 Type II certified, GDPR and HIPAA compliant, SSO/OIDC, SCIM provisioning, and regular third-party audits, with GitHub Enterprise support on Enterprise plans.
When Swarmia might be the better fit
We'd rather you pick the right tool than just pick us. If your organization wants team-level flow improvement with a firm philosophy against individual measurement, Swarmia's working agreements, Slack feedback loops, and flow metrics are polished and well liked by developers. Its Lite tier is also an affordable entry point for GitHub-only insights. But flow metrics alone shouldn't decide it: if you need to know what your engineers and AI agents actually produced, with objective, code-level attribution you can defend in front of a board, that's what Weave was built for.
Customer Story
"Our goal is to ship the highest quality product as fast as possible for our customers. We use Weave to get an objective measurement and keep ourselves honest about how we're doing."
Raunak Chowdhuri
Founder & CTO, Reducto
+19%
Increase in measured engineering output
60 days
From install to first executive report
Frequently asked questions
What's the main difference between Weave and Swarmia?
Weave measures engineering work directly from code using LLMs and machine learning, attributing every contribution to humans or AI and normalizing output against expert benchmarks. Swarmia builds team-level flow metrics from Git and issue-tracker data, paired with developer surveys. Weave measures what was produced; Swarmia measures how smoothly it flowed.
Is Weave or Swarmia better for measuring AI coding tools like Cursor, Claude Code, and Copilot?
Weave is purpose-built for AI measurement: it attributes code to specific AI tools, quantifies AI's impact on velocity, quality, and review throughput, and provides observability for autonomous coding agents. Swarmia tracks AI adoption and cost with delivery-metric splits, which tells you AI is being used and what it costs, but not what it produced at the code level.
How much do Weave and Swarmia cost?
Both publish transparent pricing. Weave has a free Starter plan, Pro at $50 per engineer per month, and custom Enterprise pricing. Swarmia is free for teams under 10 developers, then €20 (Lite) or €39 (Standard) per developer per month, with custom Enterprise plans. The difference is what you get for it: flow metrics from metadata versus code-level output measurement and AI attribution.
Can Weave measure individual engineers fairly?
Yes, and this is a core difference. Swarmia deliberately avoids individual metrics because raw Git data (commit counts, lines of code) is misleading about individuals. Weave solves the underlying problem instead: its output units are normalized and calibrated to expert benchmarks, so per-engineer visibility reflects real contribution rather than activity volume.
Does Weave replace Swarmia's working agreements and Slack feedback loops?
No. Working agreements and Slack nudges are process tooling, and Weave doesn't aim to replace them. Weave focuses on measurement: output, quality, reviews, and AI attribution, surfaced through dashboards, reports, and the Wooly AI agent.
Is Weave enterprise-ready?
Yes. Weave is SOC 2 Type II certified, GDPR and HIPAA compliant, and supports SSO (OIDC) and SCIM provisioning, with regular third-party audits. It's used by Fortune 100 companies and supports GitHub Enterprise.
Can I switch from Swarmia to Weave?
Yes. Weave connects directly to your existing tools, including GitHub and your project management stack, and calibrates automatically to your codebase. Most teams see their first data the same day. Book a demo and we'll map your current Swarmia reporting to Weave equivalents.
Flow tells you work moved. Weave tells you what got built.
Weave normalizes engineering work into a single unit, calibrated to expert benchmarks. Code-level AI attribution, fair per-engineer visibility, live before the end of the day.
The engineering intelligence platform for the AI era.
Trusted by engineering teams from seed stage to Fortune 500