TigerOps
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See exactly what your code does in production

Stop guessing. Get code-level traces, AI-suggested fixes, and deployment confidence so you can find and fix production issues in minutes — not hours.

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Supports

Node.jsPythonJavaGoRuby.NETPHPRust
trace: checkout-service → POST /orders
✓POST /orders12ms
✓validateCart()2ms
✓chargePayment()3ms
✗db.insertOrder()2847ms
orders.ts:142
→ Missing index on orders.user_id
◈ AI Fix Suggestion(confidence 94%)
CREATE INDEX CONCURRENTLY
idx_orders_user_id
ON orders (user_id);
< 5 min
Average time to identify root cause
90%
Faster root cause analysis
15 min
Average onboarding time
0 config
Required for auto-instrumentation

Sound familiar?

Production debugging shouldn't be a half-day adventure. These are the friction points developers tell us kill their productivity.

🐌

Slow Debugging Cycles

"Works on my machine" bugs that take hours to reproduce locally. Without production traces, you're guessing at root causes from logs that rarely tell the whole story.

👻

Production Blind Spots

You deploy, then pray. Without real production visibility, issues discovered by users feel like surprises — and the debugging process starts from scratch every time.

🤹

Context Switching Hell

Switching between your IDE, log aggregator, APM tool, and error tracker to piece together what happened. Each context switch costs you flow state and time.

🎯

Unclear Deployment Impact

After a release, it's not always clear if a metric change was caused by your deployment or something else. Attribution is manual, slow, and often wrong.

Built for Developers

The visibility you need. The context you deserve.

⬡

Code-Level Traces

See exactly which line of code is causing latency. Distributed traces pinpoint slow DB queries, N+1 problems, and external API bottlenecks with file and line references.

◈

AI-Suggested Fixes

When an error occurs, the AI agent analyzes the stack trace, correlates with similar past incidents, and suggests a specific fix — often with a code snippet.

◎

Real-Time Error Tracking

Every exception, unhandled promise rejection, and HTTP error captured and grouped intelligently. See first occurrence, affected users, and a full stack trace instantly.

▦

Deployment Tracking

Every deploy is annotated across every metric and trace. See exactly which deploy changed your error rate, latency, or throughput — with one click.

From bug report to fix — in minutes

The debugging workflow developers actually want.

1

Error in production

Error Captured

User reports a 500 error on checkout. TigerOps has already captured the full stack trace, user session, and distributed trace.

2

Open in TigerOps

Code-Level Trace

Click the Slack notification. See the exact line of code, the DB query that timed out, and the 3 upstream services involved.

3

AI diagnosis

AI Analysis

AI correlates with 3 similar incidents from last month. Root cause: missing database index on the orders table. Suggested fix attached.

4

Fix and deploy

Resolved

Apply the suggested migration, deploy. TigerOps auto-correlates the new deployment with error rate — confirms the fix worked in 90 seconds.

“

I used to spend half a day debugging production issues. With TigerOps, I open the error, see the exact line of code that caused it, and the AI has usually already suggested the fix. It changed how I think about shipping.

JC
Jamie C.
Senior Backend Engineer, B2B SaaS

Debug faster. Ship with confidence.

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