Telemetry and Privacy in Vibe Coding Tools: What Data Leaves Your Repo

When you type a simple prompt like "Build a login API with JWT auth," your AI coding tool doesn’t just spit out code. It watches. It records. And sometimes, it sends that information out of your machine-without you even realizing it. This isn’t sci-fi. It’s what’s happening right now in tools like Claude Code, an AI-powered coding assistant from Anthropic that generates code from natural language prompts, Cursor, and Cline. The real question isn’t whether telemetry is happening-it’s what is leaving your repo, and who gets to see it.

What Exactly Is Telemetry in Vibe Coding?

Telemetry isn’t just logging. It’s the automated collection of metrics, logs, and traces as you code. Metrics track things like how many tokens you used, how long a code generation took, or how often the AI failed. Logs capture events-like when you asked for a database query or when the AI retried a failed response. Traces follow the full path of a request: from your prompt, through the AI model, into your editor, and back again. These aren’t optional extras. They’re built into every major vibe coding tool today.

Most of these tools use OpenTelemetry, an open standard for collecting and exporting telemetry data across platforms as the backbone. That means whether you’re using Replit, Cursor, or Cline, the data format is the same. It’s like everyone speaking the same language-but not everyone agrees on what to say.

What Gets Sent Out? The Three Types of Data

• Metric data is numbers: token count, latency, memory use. These help teams monitor cost and performance. Nothing personal here-just system behavior.
• Logs are event records. This is where things get risky. Logs might include your exact prompt: "Generate a function that connects to our internal API at https://api.internal.company.com/v2/auth". That’s not just code. That’s your company’s secret endpoint.
• Traces show the full journey. They track how your prompt led to a code change, which file was modified, what dependencies were pulled in, and even which version of the AI model was used. This creates a digital fingerprint of your workflow.

OpenTelemetry handles all three. But here’s the catch: what gets included in logs and traces depends entirely on the tool’s default settings.

Privacy Defaults Vary Wildly

Not all tools treat your data the same. Take Claude Code, a vibe coding tool designed with privacy as a core principle. By default, it redacts your prompts. Even if you turn on telemetry, your exact words-"How do I fix the auth token expiry?"-never leave your machine. You have to explicitly enable prompt logging with environment variables like CLAUDE_CODE_ENABLE_TELEMETRY=1 and CLAUDE_CODE_LOG_PROMPTS=false. It’s opt-in, not opt-out. Anthropic built this because they know developers don’t want their internal API keys, database schemas, or business logic floating around.

Compare that to Gemini, Google’s AI coding assistant that logs prompts by default. Its setting GEMINI_TELEMETRY_LOG_PROMPTS=true is turned on out of the box. If you don’t go into the config and flip it to false, your prompts-full of sensitive context-are being sent to Google’s servers. No warning. No opt-in. Just default behavior.

And then there’s Codium Codex, a tool that exports logs and traces but not metrics. To get metrics, you have to parse logs manually. That’s a workaround, not a feature. It means teams using Codex might be blind to usage spikes or cost overruns because the data isn’t structured for easy analysis.

Where Does the Data Go?

Telemetry doesn’t just vanish into the air. It’s sent to an endpoint. That endpoint could be:

• A local server on your machine (like http://localhost:4318)
• Your company’s internal observability stack (VictoriaMetrics, Grafana Loki)
• A cloud service like VictoriaMetrics Cloud or Sentry

Most enterprise teams use a local collector first. The telemetry data flows from your editor to a tool running on your network, then gets filtered and forwarded to the cloud. This way, sensitive data never leaves the firewall unless you say so. But if you’re using a tool that sends data directly to the vendor’s cloud-like some versions of Cursor or Replit-you’re trusting them not to store or analyze your prompts.

And here’s the twist: some tools now use Model Context Protocol (MCP), a system that lets AI agents pull live telemetry data back from observability platforms to improve code generation. So after your AI writes code, it might query Sentry to see how the last version performed. That means your execution logs-errors, latency, failures-flow back to the AI provider. You’re not just sending data out. You’re feeding it back into the model.

What’s at Risk?

Imagine this: you use an AI tool to generate a data pipeline in Tinybird. You type: "Create a materialized view that aggregates satellite images by hour and alerts on z-score anomalies." The AI builds it. But if telemetry logs your prompt, someone at the vendor could see:

• Your data source: satellite imagery
• Your analytical method: z-score on 10-hour windows
• Your infrastructure pattern: materialized views + anomaly endpoints

That’s not just code. That’s your competitive edge. If a competitor uses the same tool and the vendor leaks or sells aggregated data, they could reverse-engineer your entire system.

Even worse: if your prompt includes credentials-"Connect to DB at db.prod.company.com:5432 with user admin and password X7#k9mP"-and that gets logged, you’ve just handed your database to a third party.

How to Protect Yourself

You can’t just ignore telemetry. But you don’t have to accept defaults. Here’s what to do:

1. Check your tool’s default settings. Does it log prompts? If yes, disable it. In Gemini, set GEMINI_TELEMETRY_LOG_PROMPTS=false. In Claude Code, leave it off unless you need it.
2. Use local collectors. Set up a local OpenTelemetry collector on your network. Route all telemetry there first. Then decide what, if anything, gets sent externally.
3. Use environment variables. Don’t rely on UI toggles. Configure via OTEL_EXPORTER_OTLP_ENDPOINT and OTEL_LOGS_EXPORTER. That way, your settings stay consistent across machines.
4. Never include secrets in prompts. Even if telemetry is off, AI tools can memorize your prompts. Never paste passwords, keys, or internal URLs into chat.
5. Use prompts to instrument your code. Instead of relying on tool telemetry, ask the AI: "Add OpenTelemetry spans to this handler, log errors with context, and emit a counter for API calls." You control what gets tracked.

The Bigger Picture: Telemetry Is Now Part of the Code

The future of vibe coding isn’t just about writing code faster. It’s about observability, the practice of understanding system behavior through metrics, logs, and traces being baked into every line. Tools like Sentry’s MCP server are already letting AI agents query your production logs to fix bugs before you even notice them. That’s powerful. But it’s also a new attack surface.

What’s happening now is a shift from "AI writes code" to "AI writes code that writes telemetry." And that telemetry, if not controlled, becomes a shadow record of your entire development process-your logic, your secrets, your mistakes.

You’re not just a developer anymore. You’re a data steward. And if you don’t manage what leaves your repo, someone else will.