The engineering metrics category has a hard problem: useful metrics usually require visibility into engineer activity, and visibility into engineer activity creates surveillance pressure. The tools that handle this well are the ones that aggregate at the team level, defer to declared data over inferred data, and refuse to surface what should not be surfaced. The list below is ordered by privacy posture, not by depth of features.
Swarmia
Best for: team-level metrics with anti-surveillance posture. Pricing: custom pricing.
Swarmia is the most explicit anti-surveillance product in the engineering metrics category. Metrics are team-level by default, individual-level surfaces are restricted, and the framing is engineering-leadership-friendly rather than CTO-dashboard-friendly.
Where it falls short: Still inference-based at the data layer. Better defaults do not change the underlying mechanism.
StandIn
Best for: metrics from declared state. Pricing: subscription tier per org.
StandIn's data source is what engineers explicitly publish. The metrics that flow from that — handoff completion rates, decision frequency, blocker durations — are derived from consented declarations rather than inferred from activity. This is structurally different from the inference category and avoids most of the privacy problems.
Where it falls short: Not a DORA metrics product. The metrics that come out are operational, not delivery-velocity.
LinearB
Best for: DORA metrics with softer framing. Pricing: free tier, paid is custom.
LinearB presents inference-based metrics with friendlier UX than Jellyfish. The PR workflow automation is the strongest non-metric feature.
Where it falls short: Same inference mechanism as the heavier products. The privacy framing is softer than the data flow.
Governance, not a status channel
StandIn is async governance infrastructure. Engineers declare working state before they go offline. Representatives answer from the record, cite the source, and refuse when the answer is not there.
Request access →Athenian
Best for: open-source-friendly engineering analytics. Pricing: custom pricing.
Athenian leans toward transparency about its data model and gives engineering leaders cleaner metric definitions than older platforms. The framing is collaborative rather than surveillance-shaped.
Where it falls short: Still inference-based. The privacy ceiling is similar to other inference products.
GitHub Insights and built-in reporting
Best for: the data you already have. Pricing: free with GitHub.
The reporting built into GitHub is more capable in 2026 than it was. For many small teams, it is enough — issue counts, PR throughput, contributor activity at the team level.
Where it falls short: Limited cross-tool joins. The deeper questions require either custom queries or a paid analytics product.
Internal dashboards on your own data
Best for: the build-your-own option. Pricing: engineering time.
A small data team can build engineering metrics views directly from Linear and GitHub APIs. Every metric definition is yours; every privacy choice is explicit.
Where it falls short: Expensive in engineering time. Most teams underestimate the maintenance cost.
How to choose
The cleanest decision tree starts with what you actually want metrics for. If the goal is executive reporting to a board, the inference-based products are the right category and the choice is among Jellyfish, LinearB, Swarmia, and Athenian by privacy posture. If the goal is operational visibility, declared-state tools are structurally cleaner and avoid most of the privacy problems. If the goal is fuzzy "we want metrics," the right move is to defer the decision until the actual question crystallizes. Buying metrics tooling to look for a question is the most expensive way to find one.
Frequently asked questions
Can engineering metrics be both useful and private?
Some metrics can. Team-level outcome metrics — what shipped, what stayed shipped, what was decided — are useful without being surveillance. Individual activity metrics are harder to make private without removing what makes them useful. Most teams overestimate how much individual-level measurement they need.
What is the most private engineering metrics tool?
Declared-state tools like StandIn are structurally private because the data is what engineers chose to publish. Among the inference-based products, Swarmia has the most explicit anti-surveillance posture, though the data source remains the same.
Is GDPR a problem for engineering metrics tools?
It is a serious constraint, especially for products that profile individuals. Tools that aggregate at the team level, retain less individual data, and operate on declared rather than inferred data have an easier compliance story.
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