Middleware Observability Platform

OpsAI watches applications, infrastructure, databases, containers, logs, and LLM apps at the same time, then correlates what it finds across those signals. The point is the step after alerting: it points to a likely root cause and proposes a fix, and it can open a pull request with the change. Error detection itself costs nothing, and root cause analysis is billed by token, so you can keep the alerts on even when you don't want the AI step on every incident.

The APM module records spans in real time and follows a request from an API endpoint down into the databases it touches. Continuous profiling shows which function is burning CPU, which answers a different question than a slow trace alone: the trace says where the time went, the profile says which code spent it. Traffic maps and dependency views help when you need to explain an outage to someone outside engineering.

Logs come in from any JSON source, so you don't have to reshape your output before sending it. You can search by attribute, fuzzy match, or regular expression, and pause and resume live view while a deploy is running. QueryGenie turns a plain English request into a query, which helps people who don't write structured queries every day. Sensitive data masking and custom attribute extraction happen before storage, which matters if your logs carry customer records.

Auto instrumentation covers hosts, Kubernetes clusters, Linux nodes, and cloud services without a script per machine. You see cluster health, CRDs, and network policies in one view, along with ECS level detail for containerized workloads. Correlating a CPU spike with the application latency it caused is the workflow the product was designed around, and it's the part most teams notice first.

Real user monitoring records what actual visitors experience in web and mobile apps, then ties a slow page load to the backend trace that caused it. Session replay lets you watch the session next to the network data instead of asking a user to describe what they saw. Privacy controls anonymize fields and limit what gets captured, which keeps the feature usable in products that hold personal data.

Synthetic monitoring and browser testing share this module. Synthetic checks run HTTP, UDP, DNS, and WebSocket requests from multiple locations on a schedule, with full request and response detail. Browser tests script a user journey, such as signing in and checking out, and run it across browsers and device profiles. Together they cover the gap that server side monitoring can't see: whether the service is reachable and usable from the outside.

Database monitoring digs into query level detail across MySQL, PostgreSQL, MongoDB, Cassandra, and Aurora, tracking CPU, memory, disk, and throughput alongside the queries themselves. The LLM module tracks latency, token usage, cost, response quality, and errors for prompts, agents, and multi step workflows, then connects an expensive or failing AI call to the trace and session that produced it.