Plain verdict
Choose Clavinci when you need exact session and cache economics, actual and API-equivalent cost, contestable attribution, survived-code ROI, cross-pillar quality, explicit content exclusions, engineer-visible access auditing, and collection-health evidence. Choose Jellyfish when AI measurement must sit inside a broader enterprise R&D system with surveys, benchmarks, portfolio allocation, custom dashboards, executive assistance, capitalization, tax credits, and audit-ready finance reporting.
Choose Clavinci when
- You need structured session, token, cache, model, cost, and tool-event evidence from the local assistants engineers actually use.
- AI ROI must reach cost per issue, story point, CI run, or merged line that remains in production after 30 days.
- Session-to-work attribution must be versioned, replayable, explainable, and open to engineer disputes.
- You need DORA, sprint flow, PR review, incidents, acceptance, re-rolls, prompt complexity, tool-task fit, churn, and compaction in one model.
- The measurement service must structurally exclude work content and expose every sensitive individual read to the engineer.
- Admins need operational proof of collector coverage, data freshness, errors, rate limits, crashes, and integration health.
Choose Jellyfish when
- AI Impact must include a broad vendor-neutral tool catalog, token-spend dashboards, adoption, productivity, quality, and executive reporting in an established enterprise product.
- Developer surveys, DevEx Index, qualitative analysis, team and industry benchmarks, and tailored improvement guidance are required.
- Investment allocation, portfolio visibility, project and product alignment, and planning analysis belong in the same platform.
- R&D tax credits, software capitalization, SOC 1 Type II controls, and audit-ready finance reports are required.
- You want custom dashboards, AI-powered queries, Jellyfish Assistant, packaged research, and an implementation relationship.
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 designed for engineering AI cost, delivery, quality, and trust decisions.
JellyfishJellyfish unifies AI Impact, developer productivity, delivery, investment allocation, DevEx, portfolio, and DevFinOps data for engineering, product, and finance leaders.
Decision implicationClavinci provides a specific evidence chain from assistant session to durable outcome. Jellyfish provides a broader enterprise R&D operating and reporting 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, models, token classes, cache activity, tool events, and compaction.
JellyfishJellyfish publishes support for Copilot, Cursor, Claude Code, Amazon Q, Gemini Code Assist, Windsurf, code-review tools, and agentic systems, with adoption, usage, spend, and automatically detected system signals.
Decision implicationJellyfish has a broad current integration estate. Clavinci makes normalized local session detail and cache economics central to the product model.
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.
JellyfishJellyfish provides AI token spend by tool, team, or initiative and connects spend to throughput, cycle time, code quality, productivity, and cost efficiency.
Decision implicationBoth make AI cost actionable. Clavinci adds explicit API-equivalent costing and durable survived-code unit economics.
04
Attribution and disputes
ClavinciClavinci attributes sessions through time, file overlap, explicit trailers, and Jira matches, supports proportional multi-session links, replays versioned logic, and provides engineer disputes with admin resolution.
JellyfishJellyfish links AI usage and inferred signals to Git, planning, delivery, and quality outcomes, including cases where every AI tool is not directly integrated.
Decision implicationJellyfish reduces integration burden. Clavinci prioritizes a visible, recomputable attribution path with a formal correction workflow.
05
DORA and code survival
ClavinciClavinci reports DORA plus AI lead time, AI change failure, AI rework, rollbacks, hotfixes, and whether attributed code survives 30 days in the main line.
JellyfishJellyfish provides DORA, SPACE, throughput, cycle time, delivery, quality, and AI versus non-AI impact analysis with benchmarks.
Decision implicationJellyfish offers broad mature productivity analysis. Clavinci makes 30-day survival a cost-linked outcome rather than a standalone quality metric.
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 attributed issue outcomes.
JellyfishJellyfish connects planning systems to delivery, allocations, initiatives, project progress, cycle time, and investment categories across the R&D portfolio.
Decision implicationJellyfish is stronger for broad portfolio and investment alignment. Clavinci is deeper on AI-attributed sprint mechanics and issue-level cost.
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 for AI and manual cohorts.
JellyfishJellyfish measures throughput, PR cycle time, review speed, delivery flow, and quality as part of developer productivity and AI impact.
Decision implicationBoth cover review and flow. Clavinci keeps review mechanics joined to the originating sessions, Jira work, incidents, and cost.
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 structured file input.
JellyfishJellyfish integrates delivery and incident signals and connects AI adoption to code quality, DORA, defects, and production outcomes.
Decision implicationJellyfish covers enterprise quality outcomes. Clavinci publishes a specific incident and on-call metric set within the attribution model.
09
AI quality signals
ClavinciClavinci combines re-rolls, tool-task fit, native and inferred acceptance, prompt complexity, churn, compaction, rework, incidents, and 30-day survival.
JellyfishJellyfish provides quality and productivity analysis, AI Workflow Insights, multi-tool comparisons, developer feedback, and guidance on effective AI behaviors and workflows.
Decision implicationJellyfish goes further on program guidance and qualitative context. Clavinci supplies a specific session-level quality taxonomy tied to durable delivery.
10
Adoption and tool choice
ClavinciClavinci compares tool and model mix, mix shift, concurrent use, adoption curves, session duration, cost, quality, and outcomes across supported assistants.
JellyfishJellyfish provides adoption, usage, spend, multi-tool evaluation, enablement insights, license and activity analysis, and vendor-neutral comparison across assistants and agents.
Decision implicationJellyfish offers a broader enterprise adoption program. Clavinci adds detailed session economics and contestable delivery attribution.
11
Engineer experience
ClavinciEngineers see personal sessions, spend, attributed commits and issues, sprint and DORA context, quality, dark work, export, deletion, disputes, and an audit of who accessed them.
JellyfishJellyfish DevEx combines research-backed surveys, system metrics, a DevEx Index, benchmarks, recommendations, and people insights for coaching and improvement.
Decision implicationJellyfish is stronger for sentiment and improvement programs. Clavinci is stronger for personal AI evidence, contestability, and access transparency.
12
Manager and executive views
ClavinciClavinci provides direct-manager evidence across ROI, delivery, review, stability, quality, and adoption plus privacy-floored team aggregates for skip-level leaders.
JellyfishJellyfish provides team, organization, portfolio, product, allocation, AI Impact, finance, and executive views plus Jellyfish Assistant for proactive and conversational analysis.
Decision implicationJellyfish provides a wider leadership and finance surface. Clavinci anchors leader access to an explicit reporting-line policy and audit model.
13
Governance and access
ClavinciEvery direct-report read is logged and engineer-visible. Skip-level drill-down needs a reason, peers cannot inspect individuals, aggregates have privacy floors, and anti-metrics block rankings and automated people decisions.
JellyfishJellyfish publishes enterprise security, least privilege, role-aware experiences, SOC controls, and organization-level analysis. The reviewed public material does not establish the same engineer-visible read audit and anti-metric contract.
Decision implicationJellyfish brings mature enterprise controls. Clavinci adds a particular workforce-governance model with accountability to the measured engineer.
14
Content boundary
ClavinciPrompts, responses, source, comments, issue descriptions, CI logs, environment variables, and secrets are excluded from Clavinci's schema and regression tests.
JellyfishJellyfish uses AI-tool, Git, planning, workflow, survey, and system signals. Its public material does not publish an equivalent field-by-field never-read list.
Decision implicationJellyfish gains more qualitative and workflow context. Clavinci makes data minimization a structural product boundary.
15
Service operations
ClavinciClavinci exposes uptime, sync and collector coverage, freshness, silent engineers, parser failures, rate limits, crashes, latency, Jira health, and notification delivery.
JellyfishJellyfish provides an established AWS-hosted enterprise service, assurance program, integrations, customer relationship, and implementation support.
Decision implicationClavinci makes evidence completeness visible inside the product. Jellyfish brings a more established enterprise service and assurance estate.
16
Planning and automation
ClavinciClavinci sends privacy-aware stale-PR, aging-WIP, dark-session, and hotfix triggers through in-app, Slack, email, or webhook. It does not provide portfolio planning, custom dashboards, or finance automation.
JellyfishJellyfish provides custom dashboards, AI-powered queries, Assistant insights, investment allocation, portfolio analysis, DevEx actions, software capitalization, tax credits, and audit-ready finance workflows.
Decision implicationJellyfish is substantially broader for planning, customization, guidance, and finance. Clavinci focuses action on operational signals from its governed evidence graph.
17
Deployment model
ClavinciClavinci is a commercial company-operated service with local collectors and engineer-held history. Customers do not deploy or maintain the application stack.
JellyfishJellyfish is a commercial hosted platform whose reviewed trust material describes AWS hosting and enterprise security controls.
Decision implicationBoth are vendor-operated products. Compare residency, isolation, subprocessors, deletion, local collection, service responsibility, and commercial terms directly.
02 / custody
Deployment and data custody
Clavinci operates the commercial service, API, database, upgrades, backups, and service health while local collectors keep full-fidelity engineer history and sync only the structured event contract. Customers govern connector authorization, project scope, identity, retention, and permitted workforce use. Jellyfish publishes an AWS-hosted platform and enterprise assurance controls. Buyers should verify residency, tenant isolation, subprocessors, retention, deletion, AI-tool credentials, survey data, and implementation access for both products.
03 / attribution
AI telemetry and outcome attribution
Jellyfish AI Impact covers token spend, adoption, multiple assistants and agents, productivity, quality, delivery, enablement, and workflow insights, including signals derived from Git and planning systems when direct integration is incomplete. Clavinci's difference is the shape of proof: structured local sessions and cache classes, actual and API-equivalent cost, a versioned attribution path to commits and Jira issues, formal disputes, and continuation through PR, CI, deployment, incident, sprint, quality, and 30-day survival evidence.
04 / access
Privacy, access, and individual visibility
Jellyfish publishes enterprise security controls, audited assurances, encryption, least privilege, and role-aware analysis. Clavinci does not claim that this makes Jellyfish insecure. Clavinci goes further in a different direction by excluding defined content classes at the schema boundary, logging each sensitive individual read for the engineer to inspect, requiring reasons for skip-level drill-down, protecting aggregates with privacy floors, and refusing anti-metrics at the query layer.
05 / competitive advantage
Where each product goes further
Where Clavinci goes further
- Clavinci provides versioned multi-signal attribution with proportional session links, replay, engineer disputes, and admin resolution. That exact correction model is not established in Jellyfish's reviewed public material.
- Clavinci distinguishes actual from API-equivalent cost and connects both to dark work, issues, story points, CI, and merged code that survives 30 days.
- Clavinci joins incidents, on-call load, re-rolls, native and inferred acceptance, tool-task fit, prompt complexity, churn, and compaction to the same AI-attributed delivery cohorts.
- Clavinci structurally excludes work content and gives engineers an immutable record of individual access, with privacy floors, reasoned drill-down, and query-layer anti-metrics.
- Clavinci exposes collection and synchronization health so buyers can distinguish a genuine trend from missing or stale evidence.
Where Jellyfish goes further
- Jellyfish provides research-backed DevEx surveys, a DevEx Index, qualitative analysis, team and industry benchmarks, and tailored improvement recommendations that Clavinci does not offer.
- Jellyfish provides investment allocation, portfolio and initiative visibility, custom dashboards, AI-powered queries, and Jellyfish Assistant across engineering and business data.
- Jellyfish DevFinOps provides R&D tax-credit, software-capitalization, allocation, SOC 1 Type II, and audit-ready finance workflows that Clavinci does not offer.
- Jellyfish supports a broader published AI and SDLC integration estate, packaged research, executive reporting, and established enterprise implementation services.
- Jellyfish can derive useful AI impact signals from Git and planning data without requiring direct integration to every AI tool.
06 / buyer questions
Questions to take into evaluation
- 01
Can the platform trace one assistant session through tokens, cost, commit, issue, pull request, CI, deployment, incident, and 30-day survival?
- 02
Which AI tools provide direct session and token data, and which adoption or impact signals are inferred from Git and planning systems?
- 03
How are cache reads and writes priced, and can we compare actual cost with API-equivalent cost for the same work?
- 04
Can attribution be replayed after logic changes, and can an engineer dispute a link and inspect its resolution?
- 05
Can we compare AI and manual work across sprint spillover, review turnaround, change failure, rework, incidents, and code survival?
- 06
Which prompt, source, issue, review, survey, and workflow content is ingested or retained?
- 07
Who can inspect an individual, and can that engineer see each access and the reason for exceptional drill-down?
- 08
Do we require DevEx surveys, benchmarks, allocation, portfolio, capitalization, tax credits, custom dashboards, or Assistant queries?
- 09
Can administrators inspect collector coverage, data freshness, sync failures, rate limits, crashes, and integration health?
- 10
What hosting, residency, assurance, implementation, pricing, and commercial maturity requirements apply?