Plain verdict
Choose Clavinci when the decision needs exact session and cache economics, actual and API-equivalent cost, durable survived-code ROI, replayable attribution and disputes, cross-pillar AI quality, explicit content exclusions, engineer-visible access audit, and collection-health evidence. Choose Swarmia when engineering intelligence must include developer surveys, benchmarks, team agreements, Slack or Teams feedback loops, retrospectives, initiatives, investment balance, capitalization, AI-assisted analysis, and published self-service pricing.
Choose Clavinci when
- You need local session, token-class, cache, model, tool-event, actual-cost, and API-equivalent-cost evidence across multiple assistants.
- Cost must reach delivered issues, story points, CI, and merged code that remains in production after 30 days.
- Attribution must be replayable and explainable, with a formal engineer dispute and admin-resolution path.
- AI and manual cohorts must be compared across sprint spillover, review, DORA, incidents, re-rolls, acceptance, prompt complexity, churn, and tool choice.
- The system must exclude defined work content and make sensitive individual reads visible to the engineer.
- Administrators need collector coverage, freshness, errors, rate limits, crashes, and integration-health evidence before trusting a result.
Choose Swarmia when
- Developer experience surveys, open-ended feedback, benchmarks, and survey retrospectives are required.
- Teams need working agreements, personal nudges, daily digests, review reminders, issue summaries, and Slack or Microsoft Teams feedback loops.
- Initiatives, investment balance, work logs, focus analysis, and software capitalization belong in the platform.
- You want Swarmia AI for conversational analysis, reports, retrospective preparation, onboarding analysis, and performance-review context.
- You prefer a mature managed platform with broad git and issue-tracker support, public pricing, and self-service trial options.
01 / evidence
Decision ledger
01
Evidence graph and decision scope
ClavinciClavinci joins AI sessions, git, Jira, pull requests, CI, deployments, and incidents in a versioned graph for cost, delivery, quality, and access decisions.
SwarmiaSwarmia combines AI adoption and cost, engineering productivity, DORA, code and issue metrics, initiatives, business outcomes, DevEx, capitalization, and continuous improvement.
Decision implicationClavinci provides a specific trace from assistant input to durable outcome. Swarmia provides a wider team and organization improvement system.
02
AI tool and session telemetry
ClavinciNine local collector implementations cover Claude Code, Codex, Cursor, Gemini, Kiro CLI and IDE, OpenCode, OpenClaw, and Every Code with sessions, token classes, caches, models, tools, and compaction.
SwarmiaSwarmia publishes AI adoption, activity, and cost for Claude Code, Cursor, and GitHub Copilot. Its AI product page also advertises Codex coverage. Tool-specific activity includes actions, lines changed, sessions, commits, pull requests, users, and models where each vendor integration exposes them.
Decision implicationSwarmia brings direct vendor and system integrations with useful adoption detail. Clavinci normalizes deeper local session economics across a broader supported assistant set.
03
Cost and ROI
ClavinciClavinci calculates actual and API-equivalent cost, dark sessions and tokens, tokens per outcome, cost per issue or story point, AI-attributed CI cost, and cost per merged line that survives 30 days.
SwarmiaSwarmia breaks AI spend down by person, tool, team, and department, identifies unused licenses, and compares adoption with productivity trends and business outcomes.
Decision implicationSwarmia is strong for license and organizational spend management. Clavinci goes further on cache-aware session economics and durable cost per delivered unit.
04
Attribution and disputes
ClavinciClavinci attributes sessions through time, file overlap, explicit session trailers, and Jira keys, supports proportional multi-session links, replays versioned logic, and accepts engineer disputes.
SwarmiaSwarmia connects AI usage and activity to pull requests, code, issues, teams, productivity, and investment categories through its integrations and organizational model.
Decision implicationSwarmia provides broad delivery context. Clavinci makes the session-to-work join explicitly replayable, contestable, and correctable.
05
DORA and code survival
ClavinciClavinci combines DORA with AI lead time, AI change failure, rework, rollback and hotfix evidence, and 30-day survival of attributed code.
SwarmiaSwarmia provides DORA, deployment frequency, lead time, change failure, MTTR, automatic rollback and hotfix detection, code metrics, and industry benchmarks.
Decision implicationSwarmia has mature DORA and benchmark coverage. Clavinci adds cost-linked 30-day survival for AI-attributed code.
06
Jira and sprint flow
ClavinciClavinci compares AI and manual velocity, spillover, cycle count and time, time in status, aging WIP, stale issues, story-point economics, epics, and issue attribution.
SwarmiaSwarmia connects Jira and Linear to issues, sprints, scope creep, flow efficiency, initiatives, work logs, investment categories, focus, and cross-team delivery.
Decision implicationSwarmia goes further on issue-tracker breadth, initiatives, and investment views. Clavinci goes further on AI-attributed sprint cost and multi-signal issue provenance.
07
PR and review system
ClavinciClavinci measures PR size, first-review time, merge after approval, iterations, self-merge, reviewer turnaround, review load, cross-team review, and stale PRs by AI cohort.
SwarmiaSwarmia provides PR flow, review metrics, work logs, team ownership, review reminders, working agreements, daily digests, and personal or team nudges.
Decision implicationClavinci ties review health to AI cost and incidents. Swarmia is stronger at turning review metrics into team habits and workflow reminders.
08
Stability and incidents
ClavinciClavinci joins hotfix rate, incidents, MTBI, on-call page rate, reopen rate, MTTR, and change failure through PagerDuty, Opsgenie, Incident.io, or file adapters.
SwarmiaSwarmia provides deployment health, change failure, MTTR, rollback, revert, and hotfix detection with configurable deployment sources and team attribution.
Decision implicationSwarmia is strong on DORA failure detection. Clavinci adds incident-system and on-call evidence plus AI-attributed stability cohorts.
09
AI quality signals
ClavinciClavinci combines re-roll rate, tool-task fit, native and inferred acceptance, prompt complexity, churn, compaction, rework, incidents, and 30-day survival.
SwarmiaSwarmia provides tool-specific acceptance and activity where available, AI versus productivity comparisons, code and quality metrics, developer survey context, and AI-generated analysis.
Decision implicationSwarmia joins quantitative and qualitative improvement signals. Clavinci supplies a fixed cross-assistant session-quality taxonomy tied to durable outcomes.
10
Adoption and tool choice
ClavinciClavinci compares tool and model mix, mix shift, concurrent use, adoption curves, session duration, cost, quality, and attributed outcomes across supported assistants.
SwarmiaSwarmia tracks enabled and active users, tool adoption, activity, spend, unused licenses, team and person breakdowns, productivity correlations, and AI survey benchmarks.
Decision implicationSwarmia goes further on license management and survey context. Clavinci adds deeper session, cost, dispute, and survival analysis.
11
Engineer experience
ClavinciEngineers see personal sessions, spend, outcomes, sprint and DORA context, quality, dark work, export, deletion, disputes, and an audit of who accessed their data.
SwarmiaSwarmia gives contributors work visibility, notifications, survey participation, working-agreement feedback, work logs, team metrics, and AI-assisted insights.
Decision implicationSwarmia is stronger for collaborative improvement loops. Clavinci is stronger for personal AI evidence, correction rights, and access accountability.
12
Manager and executive views
ClavinciClavinci provides direct-manager ROI, delivery, review, stability, quality, and adoption views plus privacy-floored team aggregates for skip-level leaders.
SwarmiaSwarmia provides team and organization metrics, business outcomes, investment balance, initiatives, capitalization, contributor views, benchmarks, and Swarmia AI analysis.
Decision implicationSwarmia has a broader business-outcomes and team-improvement surface. Clavinci binds leadership visibility to reporting-line governance.
13
Governance and access
ClavinciDirect-report reads are engineer-visible. Skip-level drill-down requires a reason, peers cannot inspect individuals, aggregate views have privacy floors, and anti-metrics block rankings and automated people decisions.
SwarmiaSwarmia provides organization admin, editor, viewer, and team-admin roles, while published permissions show that many product objects are visible across roles and individual AI cost or activity can be broken down by person.
Decision implicationSwarmia provides clear role administration. Clavinci goes further on per-read accountability and structural workforce-governance rules.
14
Content boundary
ClavinciClavinci excludes prompts, responses, source, review comments, issue descriptions and comments, CI logs, environment variables, and secrets by schema and test.
SwarmiaSwarmia states that it does not read or store source code, but it stores filenames and change sizes and can ingest issue data, tool analytics, and open-ended survey feedback according to each integration.
Decision implicationBoth have meaningful data-minimization positions. Clavinci publishes a wider field-level exclusion contract and does not collect qualitative survey content.
15
Service operations
ClavinciClavinci exposes uptime, collector and sync coverage, freshness, silent engineers, parser errors, rate limits, crashes, latency, Jira health, and notification delivery.
SwarmiaSwarmia provides an established managed service, SOC 2 Type 2 assurance, twice-yearly security audits, integration guidance, customer support, and production infrastructure in Google Cloud with stored data in Frankfurt.
Decision implicationClavinci exposes evidence completeness inside the product. Swarmia brings a mature service, documented security posture, and customer program.
16
Planning and automation
ClavinciClavinci sends stale-PR, aging-WIP, dark-session, and hotfix triggers through in-app, Slack, email, or webhooks. It does not provide surveys, initiatives, capitalization, or team agreements.
SwarmiaSwarmia provides working agreements, review reminders, digests, issue summaries, retrospectives, initiatives, investment balance, capitalization, Swarmia AI, and Slack or Teams improvement loops.
Decision implicationSwarmia is the stronger continuous-improvement and planning system. Clavinci focuses alerts on attributed engineering risk and cost.
17
Deployment model
ClavinciClavinci is a commercial company-operated hosted service with local collectors and engineer-held history. Customers do not deploy or maintain the application stack.
SwarmiaSwarmia is a commercial managed service hosted on Google Cloud, with proxy options for protected GitLab environments and no customer-operated Swarmia application established in the reviewed material.
Decision implicationBoth are vendor-operated products. Compare residency, integration permissions, proxy scope, retention, deletion, and service responsibility.
02 / custody
Deployment and data custody
Clavinci operates the hosted application, API, database, upgrades, backups, and service health. Local collectors retain full-fidelity engineer history and send only the structured event contract. Customers control connector authorization, project scope, identity, retention requirements, and permitted workforce use. Swarmia publishes Google Cloud operation, Frankfurt data storage, SOC 2 Type 2, source-code non-storage, configurable repository access, and a proxy for protected GitLab environments. Buyers should compare exact connector permissions, filenames and metadata retained, survey content, AI-tool analytics, residency, subprocessors, deletion, and support access.
03 / attribution
AI telemetry and outcome attribution
Swarmia now has meaningful AI data: licenses, active users, individual and team cost, activity, sessions, lines changed, actions accepted or rejected, commits, pull requests, and models where vendor APIs expose them. It also compares adoption with engineering outcomes. Clavinci goes deeper on normalized local session and cache economics across nine collectors, actual and API-equivalent cost, multi-signal attribution, disputes, incidents, prompt complexity, re-rolls, tool-task fit, compaction, and 30-day survival.
04 / access
Privacy, access, and individual visibility
Swarmia states that it does not read or store source code and publishes clear security and role documentation. Its product intentionally includes filenames, issue data, individual AI cost and activity, contributor analysis, and open-ended survey feedback where enabled. Clavinci uses a stricter field-level exclusion contract and adds engineer-visible audit for individual reads, reasoned skip-level drill-down, privacy floors, peer restrictions, and query-layer anti-metrics. Buyers should choose the governance model that matches the program, not assume one generic privacy label covers both.
05 / competitive advantage
Where each product goes further
Where Clavinci goes further
- Clavinci normalizes local session, token-class, cache, model, tool-event, actual-cost, and API-equivalent-cost evidence across nine collector implementations.
- Clavinci provides versioned multi-signal attribution, proportional session links, replay, engineer disputes, admin resolution, and cost per merged line that survives 30 days.
- Clavinci joins incidents, on-call load, re-rolls, tool-task fit, prompt complexity, churn, and compaction to AI-attributed delivery cohorts.
- Clavinci provides a wider explicit content exclusion contract plus engineer-visible read auditing, reasoned skip-level access, privacy floors, peer restrictions, and anti-metric refusals.
- Clavinci exposes collector coverage, sync freshness, silent engineers, parser errors, rate limits, crashes, latency, and integration health.
Where Swarmia goes further
- Swarmia provides research-backed developer surveys, open-ended feedback, benchmarks, survey analysis, and retrospective workflows that Clavinci does not offer.
- Swarmia provides working agreements, review reminders, personal nudges, daily digests, issue summaries, and Slack or Teams feedback loops that Clavinci does not offer.
- Swarmia provides initiatives, investment balance, work logs, focus analysis, and software-capitalization reporting that Clavinci does not offer.
- Swarmia AI provides conversational analysis, custom reports, retrospective preparation, onboarding reports, and performance-review context.
- Swarmia has broader git and issue-tracker support, industry benchmarks, documented security assurance, public pricing, and self-service purchasing paths.
06 / buyer questions
Questions to take into evaluation
- 01
Can the product trace one local assistant session through tokens, cost, commit, issue, pull request, CI, deployment, incident, and 30-day survival?
- 02
Which AI tools provide sessions, token classes, caches, models, actions, and costs directly, and which fields are absent or inferred?
- 03
Can attribution be replayed after rules change, and can an engineer dispute and follow correction of a wrong link?
- 04
Can we compare AI and manual work across sprint spillover, review turnaround, change failure, incidents, rework, and survived code?
- 05
Which source, filenames, issue fields, comments, survey responses, tool analytics, and logs enter or remain in the product?
- 06
Who can see person-level cost, activity, and delivery metrics, and can the engineer inspect every sensitive read?
- 07
Do we require developer surveys, benchmarks, retrospectives, working agreements, digests, review reminders, and team nudges?
- 08
Do we require initiatives, investment balance, work logs, capitalization, Swarmia AI, or issue-tracker breadth beyond Jira?
- 09
Can administrators verify collection coverage, data freshness, silent users, parser failures, rate limits, and integration health?
- 10
Which residency, assurance, support, pricing, and commercial-maturity requirements apply?