Directory Observability & MonitoringAPM & Distributed TracingSpring Cloud Sleuth

Spring Cloud Sleuth

About Spring Cloud Sleuth

Spring Cloud Sleuth is a distributed tracing solution designed specifically for Spring Boot applications, enabling enterprises to gain deep observability into their microservices architecture. It automatically instruments common ingress and egress points within Spring applications, allowing trace and span IDs to be propagated and logged consistently. This facilitates troubleshooting and performance monitoring by correlating logs and tracing requests across distributed systems.

Targeted at enterprises running complex, distributed Spring-based applications, Spring Cloud Sleuth simplifies the implementation of tracing without requiring extensive manual configuration. It integrates seamlessly with Zipkin-compatible tracing systems for centralized trace collection and visualization. By providing auto-configuration and native support for Spring Boot, it reduces the operational overhead of managing distributed tracing and enhances the ability of CIOs and technology leaders to monitor application health and diagnose issues effectively.

How to evaluate APM & Distributed Tracing

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Spring Cloud Sleuth. It comes from our Observability & APM Platforms buyer guide.

22%

Telemetry Coverage & Correlation

Unified metrics, traces, logs, real-user monitoring, synthetics, and continuous profiling; automatic correlation across signals; service maps and topology; Kubernetes, container, serverless, and cloud-provider auto-discovery; breadth and quality of the integration catalog

22%

Cost Control, Cardinality & Data Governance

Pricing-unit transparency and overage terms; high-cardinality handling without runaway cost; ingest-time filtering, sampling, aggregation, and tiered retention; a native or compatible telemetry pipeline; per-team budgets, quotas, and chargeback visibility so spend is governable, not just observable

20%

Distributed Tracing & APM Depth

End-to-end trace correlation across services and async boundaries, tail-based and adaptive sampling, code-level and method-level profiling, error tracking and exception grouping, database and dependency visibility, and frictionless OpenTelemetry trace ingestion alongside any native agent

16%

AIOps, Anomaly Detection & Root Cause

Quality of automated anomaly detection and alert noise reduction, causal versus correlative root-cause analysis, forecasting and capacity signals, and emerging agentic remediation — judged on accuracy during a real incident, not on the marketing name of the AI engine

12%

OpenTelemetry & Openness

Depth of native OTel support (traces, metrics, logs) versus a proprietary agent; whether collection is genuinely vendor-neutral or quietly locks you in; query-language portability; export and data-egress freedom; and how much re-instrumentation a future migration would actually require

Scale, Reliability & Operability

Proven performance at your peak event and series volumes, multi-region availability and platform SLA, data-retention options, RBAC and SSO, and — if self-hosted — the operational burden of running and upgrading the backend at high cardinality

Other APM & Distributed Tracing Vendors

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Airbrake

Alibaba Cloud Application Real-Time Service

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Quick Facts

spring.io/projects/spring-cloud-sleuth

CategoryObservability & Monitoring

SubcategoryAPM & Distributed Tracing

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