Middleware OpsAI

What is Middleware OpsAI?

Middleware OpsAI is an observability and incident-response platform for SRE, platform, DevOps, application, and Kubernetes teams that correlates telemetry across applications, infrastructure, databases, containers, logs, RUM, synthetics, and LLMs to find root causes and trigger remediation.

It includes Automated error fixing, Kubernetes debugging and auto-fix, Anomaly detection, and Log pattern analysis, and connects with GitHub, Kubernetes, Docker, AWS, Slack, Grafana, and Prometheus. Plans run Free Forever at $0, Pay As You Go at $0.3 per GB of metrics, logs and traces, and Enterprise at custom pricing.

What does Middleware OpsAI do?

OpsAI watches telemetry across applications, infrastructure, databases, containers, logs, RUM, synthetics, and LLMs, then correlates signals on one timeline to spot anomalies and trace them back to root causes. It combines automatic issue detection, suggested fixes, and automated remediation so teams can move from alert to action without stitching together separate tools.

The same workflow also supports Kubernetes debugging and auto-fix, plus log pattern analysis for faster triage. At scale, Middleware says it serves 4,200+ engineers and connects to 200+ tools and services, with 450+ built-in integrations listed on the integrations page. The platform is built for cloud-native environments and supports public API docs at docs.middleware.io.

Customer proof on the site includes Trademarkia and Corgi, and Middleware says it reduced debugging and resolving time by nearly 90% in one customer story.

Why use Middleware OpsAI?

• OpsAI ties detection, diagnosis, and remediation together, so teams can move from alert to fix without switching tools.
• The unified timeline across logs, metrics, traces, RUM, synthetics, and LLMs reduces the guesswork in cross-signal debugging.
• Automatic PR generation and suggested fixes help teams turn incident response into repeatable workflows.
• The platform's 200+ tool and service integrations make it easier to fit into an existing observability stack.
• Public API docs give engineering teams a path to automate workflows around observability data.
• Customer stories cite nearly 90% less time spent debugging and resolving issues, which signals meaningful operational impact.

Who is Middleware OpsAI for?

• SRE teams who need to detect incidents quickly and shorten time to resolution.
• Platform engineers who want correlated visibility across logs, metrics, traces, and containers.
• DevOps teams who need automated fixes and clearer incident triage across cloud-native systems.
• Application engineers who want log, APM, and RUM signals in one workflow.
• Kubernetes operators who need debugging help and auto-fix for cluster issues.

What are Middleware OpsAI's key features?

What are Middleware OpsAI's use cases?

How does Middleware OpsAI work?

1. Connect your first source through Integrations, starting with logs, metrics, traces, or Kubernetes so OpsAI has live production context to analyze.
2. Route alerts into Third-party alert ingestion and map them into the Unified Experience, so incidents arrive with the surrounding telemetry already attached.
3. Let Anomaly detection and Log pattern analysis surface the most likely failure points, then review the investigation trail inside OpsAI.
4. Trigger Automated error fixing or Kubernetes debugging and auto-fix to apply the recommended remediation, then verify recovery across APM, RUM, and infrastructure views.