Tracing Service ingests distributed traces from applications and services into a centralized platform for storage, search, visualization, and root-cause analysis.
An e-commerce platform is split into multiple services for checkout, payment, inventory, and shipping. Users report occasional slow checkout requests, but the failing service is unclear.
Tracing Service captures the full request path across all participating services. Engineers can inspect a single trace in Grafana, identify the slow span, and determine whether the delay came from the application, a database call, or an external dependency.
A SaaS provider exposes public APIs and needs to understand why some endpoints exceed latency targets during busy periods.
Tracing Service records spans for inbound requests and downstream calls. By comparing span durations, teams can separate network latency from application processing time and pinpoint the most expensive operations.
A business workflow fails intermittently, but logs from individual services do not clearly show where the failure starts.
Tracing Service links related spans into a single trace so teams can follow the exact execution path of a failed request. Error status, events, and span attributes help isolate the failing component, triggering step, and root cause.
A platform team needs better visibility into how internal services interact with databases, message queues, and third-party APIs.
Tracing Service shows server, client, producer, consumer, and internal spans in one flow. This helps teams understand service dependencies, spot slow external calls, and analyze retry patterns, timeout events, or queue delays.
A development team deploys frequent application changes and wants to verify that a new release does not introduce hidden latency or failure patterns.
Tracing Service allows the team to compare traces before and after deployment. Changes in span duration, error rate, or dependency behavior can be detected early before users are broadly affected.
An organization wants to standardize observability across different languages and runtimes without building separate tracing stacks per team.
Tracing Service supports OpenTelemetry trace ingestion over OTLP/HTTP and OTLP/gRPC. Teams can instrument services with OpenTelemetry SDKs and export traces to one managed backend using consistent authentication and access patterns.
A team runs generative AI and LLM workflows and needs to understand where latency and operating cost accumulate across internal processing steps, without adding a separate AI observability tool.
Tracing Service uses the OpenTelemetry semantic conventions for generative AI (GenAI) to instrument each interaction and processing chain. Token consumption, prompts, and step costs are recorded as span attributes, the execution path is visualized in Grafana, and AI performance correlates directly with infrastructure signals in the same interface.
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