Automatic SQL capture and analysis via eBPF — see exactly which queries slow down your services, identify missing indexes, and detect N+1 patterns
From automatic SQL capture to query plan analysis — deep database visibility from eBPF
See the actual SQL your ORM generates and how it performs. Identify where eager loading, query batching, or raw SQL would improve performance.
Automatically detect N+1 patterns across your entire application. See which endpoints generate hundreds of queries per request.
See live connection pool utilization based on actual usage data. Stop guessing and start configuring pools based on real traffic patterns.
Compare query performance before and after schema migrations. Historical data actually improves the queries they target.
Trace timeout errors to the specific slow queries that cause them. See the full chain from HTTP request to database query.
Understand query growth trends and predict when you will need to scale your database. Plan upgrades based on data, not guesswork.
Replace the patchwork of monitoring, logging, tracing, and incident management tools with a single AI-powered platform.
Every new service means another agent to install, configure, and keep updated. Miss one, and it's a blind spot until something breaks.
Attach at the kernel level once, and every service — new or old — is covered instantly. No agents, no per-service config.
To see actual query traffic, you're wrapping database calls, adding middleware, or routing through a proxy — and hoping nothing breaks.
Queries are captured as they hit the wire — no proxy, no wrapper, no touching a single line of application code.
N+1 queries slip through code review constantly. You usually find them the hard way — during a slow page load in production.
Every endpoint is watched continuously. The moment a pattern looks like N+1, you're flagged — before users notice the slowdown.
Someone wrote a script two years ago to check pool usage. It's brittle, nobody trusts it fully, and it doesn't tell you what size to use instead.
Pool usage is tracked live, and ApexData tells you exactly how to size it based on real traffic — not guesswork.
You only check for missing indexes once a query is already slow — and only if someone thinks to run EXPLAIN on it in the first place.
Every query is analyzed automatically, and when an index would help, you get the exact CREATE INDEX statement to run.
Most monitoring gives you a snapshot of right now. Figuring out when a query got slow — or what deploy caused it — is guesswork.
Query performance is tracked over time and automatically lined up against deploys, so you can pinpoint exactly what changed.
If a service wasn't set up with the right library or agent, it's invisible — including the shadow services nobody remembers exist.
eBPF sees traffic at the kernel level, so any service touching a database is covered automatically — instrumented or not.
Every agent you run is competing for CPU and memory with your actual application — a tax you pay on every single pod, all the time.
Running in kernel space means monitoring costs almost nothing — no competing for resources, no performance tax on your services.
eBPF captures queries at the kernel level — before and after execution, no proxies, no code changes. See every query from every service.
Our AI automatically detects N+1 patterns, identifies missing indexes, and recommends specific optimizations — no manual profiling needed.
No SDK, no agent, no sidecar proxy. No code changes required. Zero performance overhead on your application. Works on every service.
Every finding includes specific recommendations — CREATE INDEX statements, query rewrites, connection pool sizing based on actual traffic data.
Database Insights
Capture
Profiling
N+1
Connections
Plans
Trends
ORM
N+1
Pool
Migration
Timeout
Capacity
No more tab-switching
Traditional Stack
ApexData
Zero config
Why ApexData
