OTel-native APM that traces every request, maps every dependency, and surfaces the cause across thousands of attributes — all on the same data plane as your logs, metrics and RUM sessions.
Use the OpenTelemetry collector you already run. Datadog, Elastic, New Relic, Jaeger and Zipkin agents work too — nothing to rip and replace.
Every trace lands in your data lake — unsampled, unfiltered, and never leaving your environment.
Traces join to logs, metrics and RUM in one query. No cross-product context switching.
Connect existing observability infrastructure — any agent format, not just OTel. Kloudfuse normalizes every trace format into a single schema, so investigation works the same regardless of where the data came from.
# point your existing collector at Kloudfuse — that's it exporters: otlp: endpoint: "kfuse-ingest.your-vpc.internal:4317" service: pipelines: traces: receivers: [otlp, datadog, zipkin, jaeger] exporters: [otlp] # ✓ traces detected · services mapped · nothing replaced
RED metrics in real time. SLO and Apdex scoring per service. Anomaly and outlier detection for auto-monitoring. Service maps that show every upstream and downstream dependency, with one click to drill.
Query spans with TraceQL via embedded Grafana, or investigate with flame graphs and waterfall views in the native UI. Supplemental panels show logs, metrics, and stacktraces alongside each span — no context switching between tools.
Multiple ML models score every series continuously and converge on what matters. Anomalies surface as a heatmap engineers can pivot in seconds, with forecasting for trends and seasonality.
Predictable pricing tied to platform deployment, not host count or trace volume. Capture every trace, every span — analyze any sample, any time.
No. Kloudfuse is OTel-native — use the OpenTelemetry collector your team already runs. Existing Datadog, New Relic, Splunk, Sumo and Elastic APM agents also work without code changes. Bring Datadog, Elastic, Jaeger, Zipkin or OpenTracing agents too — there's nothing to rip and replace.
Inside your VPC, on your cloud account, under your keys. Spans, errors and dependencies are stored in the customer-owned data plane and queried locally — production telemetry never leaves your environment.
Predictable pricing on one axis: terabytes ingested — not per-host, per-trace or per-engineer. Capture 100% of traces without a sampling tax or the per-host model most APM vendors charge on.
K-Lens is Kloudfuse's anomaly-detection engine. It runs SARIMA, Prophet, DBScan and seasonal decomposition together, scoring every series continuously and converging on what matters. Anomalies surface as a pivotable heatmap across thousands of attributes — and all inference runs in your VPC, never a vendor API.
Yes. Traces, logs, metrics, RUM and LLM telemetry all run on the same data plane and join in a single query. You're not stitching together multiple vendor products — it's one investigation model across every signal.
Distributed tracing is the underlying telemetry — spans capturing each step of a request across services. APM is the application-aware layer built on top: service maps, RED metrics, error tracking, SLO scoring, and K-Lens root cause analysis. In Kloudfuse, both live on the same page and the same data plane.
Connect your OTel collector, see your first traces, and explore the service map. No proprietary agents required.
Read the guideWhy observability shouldn't force a trade-off between security, governance, and control — and how that shaped the architecture behind APM, tracing, and root cause analysis.
Read the blogArchitecture, pricing, data residency, agent compatibility. A side-by-side breakdown for teams evaluating both.
See the comparisonThirty minutes on your telemetry. The cause, before the call ends.
