TigerOps
Use CasePerformance Optimization

AI that finds your performance bottlenecks

Stop waiting for users to complain. TigerOps continuously profiles every service, identifies the highest-impact bottlenecks, and gives you specific, actionable optimization suggestions — with expected improvement estimates.

40%average improvement in p99 latency across TigerOps customers
See performance profilingTigerOps for developers →
p99 Latency Improvementcheckout-service
Baseline2400ms p99
After DB fix640ms p99
After cache280ms p99
After query opt.143ms p99
Total improvement94% faster p99
40%
Improvement in p99 latency
24/7
Continuous profiling
< 1%
Profiling overhead
Real-time
Impact measurement

Every performance dimension covered

AI analyzes performance across every layer of your stack — from database queries to external APIs to cache efficiency.

◎

Database Query Analysis

AI identifies slow queries, missing indexes, N+1 patterns, and over-fetching. Suggestions come with estimated impact and an example fix.

Before
847ms avg query time
After
12ms avg query time
98% faster
⬡

API Endpoint Profiling

Every endpoint is continuously profiled. AI flags endpoints that exceed latency SLOs and breaks down time spent in each code path.

Before
p99: 2,400ms
After
p99: 180ms
92% faster
▦

Cache Effectiveness

Measure cache hit rates, identify hot keys and cold paths, and get AI recommendations for cache strategy improvements.

Before
23% cache hit rate
After
89% cache hit rate
+287% hits
◈

External Service Latency

Track latency for every third-party API call. AI identifies which external dependencies are degrading your response times and by how much.

Before
340ms avg latency
After
45ms avg latency
87% faster
Optimization Workflow

From bottleneck to improvement — continuously

01

Continuous Profiling

TigerOps continuously profiles every service with low-overhead instrumentation. No sampling gaps, no manual profiling sessions.

›Always-on profiling across 100% of requests with < 1% overhead
02

Bottleneck Detection

AI analyzes flame graphs, trace data, and latency distributions to identify the highest-impact bottlenecks — ranked by user impact.

›Top bottleneck: orders.findByUser() — affects 34% of requests, +620ms per call
03

Optimization Suggestions

For each bottleneck, AI provides a specific, actionable suggestion — with code examples, expected improvement, and implementation complexity.

›Add composite index on (user_id, created_at). Estimated improvement: 85%. Effort: low.
04

Implement & Deploy

Apply the suggested optimization and deploy. TigerOps auto-tracks the deployment and begins measuring impact immediately.

›Deploy tagged: perf-fix/orders-index. Tracking latency impact on affected endpoints.
05

Impact Measurement

Before/after comparison is automatically generated. See exactly how much the change improved latency, error rate, and throughput.

›p99 latency: 2,400ms → 340ms (-86%). Throughput: +240 req/s. Error rate: unchanged.
“

TigerOps found a missing composite index that had been silently hurting our checkout p99 for 8 months. We fixed it in an afternoon and latency dropped from 2.4 seconds to 140ms. The AI even predicted the improvement before we deployed.

AR
Alex R.
Tech Lead, E-commerce Platform
40% improvement in p99 latency

Make your application faster

Let AI find the bottlenecks you've been missing and give you the exact steps to fix them.

Start optimizing todayExplore APM →