Infrasage uses temporal embeddings and advanced AI to detect anomalies before they become outages, diagnose root causes in seconds, and automatically remediate — at any scale.
Integrates with your existing stack
Microservices, multi-cloud, containers — the complexity has exploded. Your on-call engineers can’t keep up, and it’s costing you.
Thousands of alerts flood your channels. Critical signals get lost in the noise, and real incidents slip through.
Engineers spend 45+ minutes per incident hunting across dashboards, logs, and traces to find the actual root cause.
The same issues recur week after week. The same manual steps, the same runbooks. Institutional knowledge is trapped in people’s heads, not in systems.
Mean time to recovery stretches to hours. Every minute of downtime costs revenue, customer trust, and your team’s morale.
A complete closed-loop AIOps pipeline. Ingest any signal, detect anomalies before impact, diagnose root cause in seconds, and remediate automatically.
A single unified pipeline that ingests every signal — metrics, logs, traces, events, SLOs, and profiles — with intelligent cardinality control. Every component is horizontally scalable. Add nodes, not complexity.
Forget static thresholds. Infrasage builds multi-dimensional vector representations of how each service actually behaves — capturing latency, resource patterns, topology context, and time-of-day cycles to detect anomalies no dashboard ever could.
Advanced LLM intelligence analyzes every incident with full context — similar historical incidents via vector search, high-cardinality trace exemplars, service topology, and human post-mortem resolutions. Seconds to root cause, not hours.
Define runbooks that automatically remediate known issues — with human-in-the-loop approval gates, Slack notifications, rollback safety, and complete audit trails.
Go beyond reactive monitoring. Predict anomalies 15-60 minutes before they happen, distinguish causation from correlation, and classify root causes automatically.
Built for production from day one. Multi-tenant isolation, granular RBAC, circuit breakers, graceful degradation, and complete observability of the platform itself.
Four specialized microservices — each independently scalable — connected via high-throughput streaming, backed by columnar analytics storage. Scale any layer independently, from startup to Fortune 500.
Drop into your existing infrastructure in minutes. 9 platform integrations out of the box, with an extensible plugin system for anything custom.
Every layer is independently scalable. Start small, grow to thousands of services — the architecture handles it.
One command deploys the entire platform — storage, streaming, monitoring, dashboards, and all Infrasage services — on any Kubernetes cluster.
# Any Kubernetes cluster — 20 min to production
$ git clone https://github.com/sushant-115/infrasage.git
$ cd infrasage
$ ./quickstart.sh
# ✅ K3S cluster deployed
# ✅ ClickHouse + Redpanda running
# ✅ Prometheus + Grafana configured
# ✅ Ingestion Gateway (auto-scaling)
# ✅ AIops Engine + Watchdog active
# ✅ Retention policies applied
# ✅ Ready to receive telemetry!ClickHouse, Redpanda, Prometheus, Grafana — all preconfigured
Non-root containers, RBAC, read-only filesystems, resource limits
Kubernetes HPA on CPU, memory, and custom buffer metrics
Configurable TTL-based cleanup for raw data, aggregations, and audit logs
15+ Grafana panels: throughput, anomalies, RCA, automation, DLQ