Code-Level
Performance Monitoring
Go beyond response times. See the exact function calls, database queries, and external service calls behind every slow request — with zero-configuration AI analysis that surfaces fixes automatically.
Slowest Transactions (p95)
POST /api/checkout
120 rpm
p95: 891ms
avg: 342ms
GET /api/products
840 rpm
p95: 243ms
avg: 88ms
GET /api/orders/:id
360 rpm
p95: 132ms
avg: 54ms
POST /api/users/login
210 rpm
p95: 98ms
avg: 43ms
GET /api/search
540 rpm
p95: 580ms
avg: 210ms
Flame Graph — POST /api/checkout (p95: 891ms)
Slowest Database Queries
SELECT * FROM carts WHERE user_id = $1
INSERT INTO audit_log VALUES ($1, $2, $3, …)
Visibility Down to the Function Call
TigerOps APM gives you more than averages and p99s — it shows you exactly why your app is slow and where to fix it.
Code-Level Tracing
Automatic instrumentation captures every function call, HTTP request, and async operation — no manual spans required.
Database Query Analysis
Identify slow queries, N+1 problems, and missing indexes automatically. View normalized queries with execution plans.
External Service Monitoring
Track latency and error rates for every HTTP client, gRPC call, and third-party API your application depends on.
Deployment Tracking
Automatic deployment markers with before/after performance comparison. See performance regressions the moment code ships.
Error Analytics
Every unhandled exception captured with full stack trace, request context, and user impact data — grouped by root cause.
Performance Profiling
Continuous profiling captures CPU and memory flamegraphs in production with less than 1% overhead.
Auto-Instrumented Languages & Frameworks
Drop-in integrations with the tools your team already uses.
Frequently Asked Questions
How does TigerOps APM identify N+1 query problems?
The APM agent tracks every database call made within a transaction and groups calls with identical normalised queries. When the same query pattern executes more than a configurable threshold of times within a single request — typically 5 or more — it is flagged as a probable N+1 and highlighted in the flame graph with the total time spent and call count.
Which languages and frameworks are supported?
TigerOps APM auto-instruments Node.js, Python, Java, Go, Ruby, .NET, and PHP. Popular frameworks including Express, Fastify, Django, Flask, FastAPI, Spring Boot, Rails, Laravel, ASP.NET Core, Gin, and Echo are instrumented automatically. The full list of 200+ supported libraries is available in the documentation.
How does deployment tracking work?
You send a deployment event to the TigerOps API when you ship code — this can be automated from your CI/CD pipeline. TigerOps draws a vertical marker on all charts at the deployment time and calculates before/after performance comparisons for every transaction, error rate, and Apdex score, so regressions are immediately visible.
What is the agent overhead in production?
The TigerOps APM agent adds less than 1% CPU overhead and under 50 MB of additional memory under typical production load. Traces are exported asynchronously on a dedicated background thread so the agent never blocks request handling. The agent has been validated in production environments handling tens of thousands of requests per second.
Can I see slow database queries with their execution plans?
Yes. For PostgreSQL and MySQL, TigerOps can optionally capture EXPLAIN plans for slow queries automatically when they exceed a configurable duration threshold. The execution plan is stored alongside the query sample in the slow query list, so you can diagnose missing indexes and planner issues without running EXPLAIN manually in a separate database client.
Find the Code That's Slowing You Down
Instrument your app in minutes. Get code-level visibility, deployment tracking, and AI-suggested fixes from day one.
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