← Back to Blog

Cazon + Sentry: Why We're Not Competing

December 15, 2024 • Chris Quinn

Summary

Sentry captures errors for humans to view in dashboards. Cazon enriches those same errors with intelligence for AI tools to query via API. Different output, different customer, different job. We integrate with Sentry through webhooks—they capture, we enrich, AI tools consume. This post explains why we're complementary, not competitive.

The Common Question

"Wait, isn't this just another error monitoring tool?" That's the most common question we get. The short answer: no. Sentry captures errors for humans to view in dashboards. Cazon enriches those same errors with intelligence for AI tools to query via API. Different output, different customer, different job.

We actually integrate with Sentry. Their webhook sends us errors, we enrich them with pattern matches and team history, and AI tools consume that intelligence through our MCP server or REST API. Sentry still does what it does best (capture and display), we add what AI needs (structure and context).

What Sentry Does

Sentry is production error monitoring. When your app throws an error in production, Sentry:

  1. Captures the error - Stack trace, breadcrumbs, user context, environment
  2. Aggregates duplicates - Groups identical errors using fingerprinting
  3. Shows dashboards - Charts, trends, release tracking for human operators
  4. Sends alerts - Email/Slack notifications when error rates spike
  5. Enables investigation - Drill down into individual occurrences, see user sessions

Sentry is built for DevOps teams who need to know "what's breaking in production right now?" It's a human interface to production errors.

What Cazon Does

Cazon is error intelligence infrastructure. When we receive an error (from Sentry or our SDK), we:

  1. Match patterns - Check 353 known signatures for root cause identification
  2. Add context - Pull team history, similar past errors, resolution data
  3. Score impact - Rank by severity, frequency, affected users (when ML is enabled)
  4. Expose via API - REST endpoints and MCP server for AI tool consumption
  5. Provide in-editor tooling - VS Code diagnostics, quick fixes, enriched suggestions

Cazon is built for AI coding assistants that need to know "which error should I help debug and how?" It's a data layer that makes errors queryable and actionable for tools like Claude, Copilot, and Cursor.

Why We Integrate

Here's the workflow for a Sentry customer using Cazon:

Step 1: Error Occurs

// Production code throws error
const user = await getUserById(req.session.userId);
// TypeError: Cannot read property 'userId' of undefined

Step 2: Sentry Captures

  • Sentry SDK captures the error
  • Sends to Sentry.io
  • Appears in your Sentry dashboard with full context

Step 3: Sentry Notifies Cazon

  • Sentry webhook fires (configured in Sentry settings)
  • Sends error payload to https://cazon.dev/api/webhooks/sentry
  • We receive: stack trace, fingerprint, user context, breadcrumbs

Step 4: Cazon Enriches

  • Pattern matcher finds: "Null Deference After Auth Check" (95% confidence)
  • Adds suggestion: "Check if session.user exists before accessing userId"
  • Checks team history: 7 similar errors in last 30 days
  • Stores enriched error in our database

Step 5: AI Tool Queries

  • Developer opens Claude Desktop
  • Types: cazon triage-errors
  • MCP server returns enriched list sorted by impact
  • Claude sees: pattern match, suggested fix, team history, code location

Step 6: Developer Sees Both

  • In Sentry: Full production context, user sessions, release tracking
  • In Claude: Pattern match, suggested fix, similar errors, code actions

Sentry tells you it happened. Cazon tells AI how to fix it.

What Cazon Adds

If you're already using Sentry, here's what the Cazon integration provides:

1. Pattern Recognition

Sentry groups errors by fingerprint (same stack trace = same issue). Cazon matches errors against 353 known patterns to identify root causes across different stack traces.

Example: These are different errors in Sentry (different stack traces), but the same pattern in Cazon:

// Error 1
TypeError: Cannot read property 'userId' of undefined
  at createPost (api/posts.ts:28)

// Error 2  
TypeError: Cannot read property 'email' of undefined
  at sendNotification (api/notify.ts:45)

// Error 3
TypeError: Cannot read property 'role' of undefined
  at checkPermissions (api/auth.ts:67)

Pattern: "Null Deference After Auth Check" - All three are accessing session.user properties without null checks. Sentry sees 3 issues. Cazon sees 1 systemic problem.

2. AI-Optimized Output

Sentry's API returns data optimized for dashboards (HTML, charts, pagination). Cazon's API returns data optimized for AI tools (structured JSON, ranked lists, embedded suggestions).

Compare:

Sentry API Response (simplified)

{
  "id": "abc123",
  "title": "TypeError: Cannot read property 'userId' of undefined",
  "culprit": "api/posts.ts in createPost",
  "level": "error",
  "firstSeen": "2024-12-15T10:30:00Z",
  "lastSeen": "2024-12-15T14:22:00Z",
  "count": 47,
  "userCount": 12,
  "permalink": "https://sentry.io/issues/abc123/"
}

Cazon API Response

{
  "id": "err_xyz789",
  "message": "TypeError: Cannot read property 'userId' of undefined",
  "location": {"file": "api/posts.ts", "line": 28, "function": "createPost"},
  "pattern": {
    "name": "Null Deference After Auth Check",
    "confidence": 0.95,
    "category": "TypeError",
    "suggestion": "Add null check: if (!session?.user) return res.status(401).json({error: 'Unauthorized'})"
  },
  "teamHistory": {
    "similar": 7,
    "lastOccurrence": "2024-12-10T09:15:00Z",
    "avgTimeToResolve": 1800
  },
  "impact": {
    "score": 0.87,
    "severity": "high",
    "frequency": 47,
    "affectedUsers": 12
  }
}

The Cazon response includes everything an AI needs to provide actionable help: the pattern, the fix, the historical context, and the prioritization data.

3. MCP Server Access

Sentry has a web UI and REST API. Cazon adds an MCP server that Claude Desktop can query directly.

With Sentry alone:

Developer: "What errors happened today?"
Claude: "I don't have access to your error monitoring. Please check Sentry."

With Cazon + Sentry integration:

Developer: "What errors happened today?"
Claude: [queries MCP server]
"You have 14 errors today. The highest impact is a Null Deference 
After Auth Check in api/posts.ts (47 occurrences, 12 affected users).
Suggested fix: Add null check for session.user before accessing userId..."

Same underlying data (from Sentry), but now AI can query and reason about it.

4. In-Editor Integration

Sentry lives in a browser tab. Cazon lives in VS Code with diagnostics, quick fixes, and chat participants.

When you're debugging:

  • Sentry: Switch to browser, search for error, read stack trace, copy-paste code location
  • Cazon: See red squiggle in editor, click lightbulb for quick fix, or ask @cazon in chat

Both have value. Sentry for production investigation, Cazon for active development.

Not Competitors, Different Layers

Think of error tooling as a stack:

┌─────────────────────────────────┐
│   AI Coding Assistants          │ ← Copilot, Claude, Cursor
│   (code generation & debugging) │
├─────────────────────────────────┤
│   Error Intelligence (Cazon)    │ ← Enrichment & AI exposure
│   (patterns, context, API)      │
├─────────────────────────────────┤
│   Error Monitoring (Sentry)     │ ← Capture & display
│   (capture, aggregate, alert)   │
├─────────────────────────────────┤
│   Production Applications        │ ← Your app code
└─────────────────────────────────┘

Sentry sits between your app and human operators. Cazon sits between Sentry (or any error source) and AI tools. We're not competing for the same layer.

This is similar to how feature flag platforms work with analytics tools:

  • Analytics (like Amplitude) tracks events and shows charts to humans
  • Feature Flags (like LaunchDarkly) uses those same events to make automated decisions

Both valuable. Both necessary. Different jobs.

When You Need Both

Use Sentry when you need:

  • Production error capture at scale
  • Dashboard visibility for DevOps/SRE teams
  • Alerting and on-call incident response
  • Release tracking and deployment monitoring
  • User session replay and breadcrumbs

Add Cazon when you want:

  • AI tools to query and reason about errors
  • Pattern matching across different stack traces
  • In-editor error intelligence (VS Code)
  • Team-wide error history and context
  • API-first access for tool integrations

Most teams will use both. Sentry for production monitoring, Cazon for development acceleration.

Integration Setup

If you're already using Sentry, adding Cazon takes 2 minutes:

Step 1: Create Cazon Account

  • Sign up at cazon.dev
  • Get your webhook URL: https://cazon.dev/api/webhooks/sentry?key=YOUR_KEY

Step 2: Configure Sentry Webhook

  • Go to Sentry.io → Settings → Integrations → WebHooks
  • Add webhook URL
  • Select events: error.created, error.updated

Step 3: Install Cazon Tools

  • Install VS Code extension: Search "Cazon" in extensions
  • Install MCP server: npm install -g @cazon/mcp-server
  • Configure Claude Desktop with MCP server

That's it. Sentry continues capturing, Cazon starts enriching, AI tools get access.

What's Next

Tomorrow (Day 11): We'll wrap up the series with our vision for error intelligence infrastructure and why this layer will become essential as AI coding assistants evolve from code generation to full-stack debugging partners.

Part of our launch series on error intelligence infrastructure for AI coding assistants.

← Back to all posts

Ready to try Cazon?

Give your AI coding assistant production error intelligence

Get Started Free