Governance & security11 min read
Governing Claude Code Mods: How They Work
Claude Code mods can rewrite prompts, tool calls and the interface. How mods work, what they can reach, and the managed settings that keep them in check.
Read articleWhy CloudByte AI
The Four Pillars of AI Engineering Efficiency
One dashboard measures where AI actually moves the needle, from the tokens you buy to the way your team ships.
- 01
Project Knowledge
Every prompt, commit, ticket and session summary becomes a searchable knowledge base for the project. New developers get context on day one, decisions stay documented, and no one solves the same problem twice.
Sources indexed7 types
- 02
AI Security
Prompt security catches credentials, secrets and personal data before they leave the developer machine. Shadow AI, plugin and MCP detection show every tool in use, with organization-wide guardrails and a full audit trail for compliance.
Secrets caughtBefore upload
- 03
Measure AI Efficiency
Cost, ROI and tracing for every session, developer and model. See which work AI actually speeds up, where tokens are wasted, and keep spend in line with the value it delivers.
ROI8.3×
- 04
Engineering ROI
Link pull requests, commits and delivery metrics to the AI sessions behind them. Developer and project insight shows what AI really ships, not just how often it is used.
PR cycle time−42%
The problems we solve
Four blind spots in AI-powered engineering
AI coding tools make every developer faster on their own. They leave teams without shared knowledge, security oversight, cost control or a clear view of who is actually using them.
AI learnings stay locked on each developer's laptop
Every developer builds their own skills, CLAUDE.md rules and session context. None of it is shared, so the team keeps re-teaching AI the same project, and it all walks out the door when someone leaves.
- Skills built and reused by one person
- CLAUDE.md files drifting per developer
- Observations lost when the session ends
- Commit context nobody can search
How CloudByte answers it
One searchable knowledge base for every project
Skills
24CLAUDE.md
6 filesObservations
2,186Git commits
2,059Project knowledge basepayments-api · searchable7 sources
Indexed per project
Skills, CLAUDE.md, observations, commits, tickets, pull requests and session summaries
Risky AI activity happens where no one is watching
Developers add plugins and MCP servers on their own, paste keys into prompts and try AI tools security has never reviewed. None of it reaches your existing monitoring.
- Unknown events from unapproved AI tools
- Unauthorized plugins and MCP servers
- Credentials and secrets leaked in prompts
How CloudByte answers it
Every security event flagged, blocked and audited
- Credential leakCloud access key caught in a promptraj · payments-apiBlocked
- Unauthorized pluginUnapproved MCP server with database accessalex · web-appFlagged
- Unknown eventNew AI tool seen on a developer machinemaya · agent-syncReview
Real-time
Caught on the developer machine
Secrets are blocked before upload and admins are alerted as it happens
Token bills arrive with no attribution
Your AI invoice is a single number. You can't tell which developer, project or model burned the money, whose prompts carry oversized context, or which seats barely get used.
- No spend split by developer, project or model
- Prompt burn from oversized context
- Paid seats with low engagement
How CloudByte answers it
Every token attributed to developer, project and model
Prompt burn
avg tokens per prompt
- Raj Mathur412.6K
- Alex Chen63.4K
- Priya Sharma59.7K
- Maya Patel52.4K
Low engagement2 users under 10% of org output
3 min
Attribution delay
Spend a dollar at 9:04 and see it attributed by 9:07
You can't see who's using the $50/seat tool
Vendor reports show completions and suggestions accepted, which are vanity metrics. They never say whether a developer even opened the tool this week.
- Paid seats with zero activity
- No usage split by developer or project
- Vanity metrics instead of real work
How CloudByte answers it
Real adoption data per developer, per project
29 paid seats7 with zero activity24%
From our 30-day pilot
Paid seats that generated zero AI activity
AI Insights
See what your team is actually getting from AI
ROI multiplier, hours saved, session depth — answered live. Plus prompt clustering so you know which kinds of work are pulling the lift.
- Live ROI multiplier and hours-saved estimate
- Team adoption + activity heatmap
- Prompt pattern clustering per developer
- ROI
- 8.3×value / cost
- Hours saved
- 92.9h@ $45/hr
- Depth
- 9.2prompts / session
- Adoption
- 52%13 of 25 developers
Team activity heatmap
Prompts by hour of day, last 4 weeks
Activity peaks on weekdays between 09:00 and 17:00, dips over lunch, and is light on weekends.
Prompt pattern clusters
Share of prompts by kind of work
- Refactoring27%
- Test writing22%
- Debugging19%
- New features17%
- Docs and comments9%
- Code review6%
Spend analytics
Token spend, explained
CloudByte AI turns the raw Anthropic, OpenAI, and Gemini token streams into something your finance team can actually read. Stacked bars by developer, project, or model. Per-seat burn rates. Cache-hit ratios so you know when prompt-caching is paying back.
Live pricing is baked in — spend a dollar at 9:04 AM and see it attributed by 9:07.
| Model | Input | Output |
|---|---|---|
| Opus | $15.00 | $75.00 |
| Sonnet 4.6 | $3.00 | $15.00 |
| Haiku | $0.80 | $4.00 |
Spend by developer
Last 30 days · top 5 · stacked by model
$611.68
Cache-hit ratio 61%
- Opus
- Sonnet 4.6
- Haiku
- Per-seat burn
- $122.34 / seat
- Opus share of spend
- 37%
| Developer | Opus | Sonnet 4.6 | Haiku | Total |
|---|---|---|---|---|
| Raj Mathur | $112.40 | $81.26 | $10.52 | $204.18 |
| Priya Sharma | $64.80 | $98.14 | $9.42 | $172.36 |
| Alex Chen | $31.20 | $69.88 | $7.34 | $108.42 |
| Maya Patel | $18.75 | $57.15 | $6.70 | $82.60 |
| Sam Taylor | $0.00 | $38.90 | $5.22 | $44.12 |
Git tracking
Every commit, summarized by AI
Claude Sonnet 4.6 reads each diff and writes a two-to-three-sentence summary. Filter by author, project, branch, or window. Click any commit to expand the AI summary, the file list, and a syntax-highlighted diff viewer.
Collection runs on the same agent that captures prompts. No GitHub webhooks, no repo setup, no branch protection changes. Diffs stored with a 5-day TTL; metadata and AI summaries live forever.
Works with your stack
- Claude Code
- Cursor
- GitHub Copilot
- GitHub
- GitLab
- Bitbucket
- Linear
- Jira
CommitsAuthor: AllProject: AllBranch: mainLast 7 days
AI summary · Claude Sonnet 4.6
Adds idempotency keys to the payment webhook handler so retried events no longer create duplicate ledger entries. Introduces a processed_events table with a unique constraint and wraps the handler in a transaction. Includes tests for duplicate delivery and out-of-order retries.
- src/webhooks/payments.ts+42 −9
- db/migrations/0042_processed_events.sql+18 −0
- test/webhooks.test.ts+26 −3
src/webhooks/payments.ts
41 export async function handleEvent(event: PaymentEvent) {− await ledger.record(event)42+ const isNew = await db.processedEvents.insertIfAbsent(event.id)43+ if (!isNew) return { status: 'duplicate' }44+45+ await db.transaction(async (tx) => {46+ await ledger.record(event, tx)47+ })48 return { status: 'processed' }49 }AI summary · Claude Sonnet 4.6
Moves dashboard chart data loading to the server so the first render ships with real numbers instead of loading skeletons. Removes two client-side fetch hooks and the loading states they required. No visual changes for users.
- pages/dashboard.tsx+96 −41
- components/charts/spend-chart.tsx+71 −88
- hooks/use-spend.ts+0 −58
- lib/queries/spend.ts+47 −0
pages/dashboard.tsx
−const { data, isLoading } = useSpend(range)−if (isLoading) return <ChartSkeleton />41+export async function getServerSideProps({ query }) {42+ const spend = await getSpend(query.range ?? '30d')43+ return { props: { spend } }44+}45 46 return <SpendChart data={spend} />AI summary · Claude Sonnet 4.6
Replaces a fixed sleep in the sync retry test with fake timers, which removes a race that made the test fail intermittently in CI. Also tightens the backoff assertion so it checks the exact delay sequence.
- test/retry.test.ts+14 −6
test/retry.test.ts
41+vi.useFakeTimers()42 const sync = startSync({ retries: 3 })−await sleep(5000)43+await vi.advanceTimersByTimeAsync(7000)−expect(sync.attempts).toBeGreaterThan(1)44+expect(sync.delays).toEqual([1000, 2000, 4000])
Select a commit to expand its summary, files and diff.
From the blog
All postsThe real data behind AI-assisted development
FAQ
Questions, answered
Everything engineering leaders ask before rolling CloudByte AI out to their team.
CloudByte AI tracks Claude Code, Cursor and GitHub Copilot. Every session, prompt and token burn from all three is captured automatically by the installed agent, so you get one view of AI work whichever tool each developer prefers. Git commits from every repo are tracked at the code-output layer too, so you also see the full picture of what ships.
A 3 KB sync agent runs in the background on each developer machine. It reads the local session data of Claude Code, Cursor and GitHub Copilot (never modifies it), captures git commits from every repo you point it at, and pushes the data to your org every 3 minutes. Developers do not change a single command. The whole install is one curl-to-bash or PowerShell line.
Data is isolated per organization at the database level. Google SSO with domain restriction, magic-link auth, and four-tier RBAC are built in. API keys and BYOK credentials are encrypted at rest with AES-256-GCM. Every API call is scoped on the server — even a super-admin on the wrong subdomain only sees that tenant.
On the Enterprise plan, each organization plugs in its own Anthropic API key. Your AI spend hits your Anthropic account directly — CloudByte never proxies billing. Keys are encrypted at rest with AES-256-GCM and only decrypted per-request. There's also a managed-key tier where we provision Anthropic access for smaller teams that haven't set up their own billing yet.
Claude Code BYOKAcross our 22-developer pilot, the measured multiplier was 1.8x team-wide average, with per-project peaks at 14.3x and troughs (legacy code) at 0.8x. Backend API work saw 2.5–3x, frontend React 1.5–2x, infrastructure/DevOps barely 1x.
No. The AI provider layer is an adapter with a unified generateText() interface, so new Claude models slot in without dashboard or database migrations. We ship model support on Anthropic's release day.
Under 10 minutes from sign-up. The installer completes in 60–90 seconds per developer, an initial sync flushes any backlog, and the dashboard is live immediately.
Yes — on the enterprise plan. The stack is a single Next.js + Express service backed by PostgreSQL, fully containerised and Terraform-deployed. Air-gapped deployments with in-org model endpoints (Bedrock or private gateway) are supported.
Only AI session metadata from Claude Code, Cursor and GitHub Copilot: prompts, tool observations, session summaries, and git commit metadata (SHA, message, diff stats). No source code files are uploaded.
Pricing is a flat monthly price per team, never per seat. Free covers up to 5 developers. Starter is $50 a month for up to 20 developers and Team is $100 a month for up to 50, both billed annually, or $10 a month more on monthly billing, with a 14-day free trial and no credit card required. Enterprise pricing is custom. Startups founded within 2 years with under $5M funding qualify for 90% off the first year.
Join 50+ teams who measure what matters.
Start free — no credit card, no infrastructure. Your team's AI data on a live dashboard in 10 minutes.
- Free for up to 5 developers
- 10-minute setup
- Your Anthropic key, your spend (Enterprise)