Applicare distributed tracing follows every request through every service, every database query, and every cloud hop. IntelliTrace identifies the exact line of code responsible for the slowdown — in plain English, attributed to the commit. No sampling. No guesswork. No war rooms.
Modern applications run as dozens or hundreds of services — frontend talking to API gateway, gateway talking to authentication, services talking to services, services talking to databases and cloud APIs. When a user clicks “Pay” and the page hangs, the problem might live in any one of them. Distributed tracing follows the request through every hop, records how long each step took, and surfaces the slowest one. Applicare goes further: IntelliTrace explains why that span is slow — in plain English, attached to the responsible commit — so the next minute of your day isn’t a war room.
The trace you actually need to debug is almost always the rare one — the 99.99th percentile, the regression that hurts a single tenant, the slow request you’ll never reproduce in staging. Throwing it away to save storage cost is throwing away the diagnosis.
Scale without throttling. The trace pipeline ingests enterprise OTLP volume without dropping spans at peak traffic — no degraded fidelity during the incident that matters most.
No head sampling. Legacy APM throws away 75–90% of traces before they hit storage. Applicare keeps every one — tail-based rules decide what to retain long-term, so error and slow traces are always there.
Causal graph completeness. Every span carries its service, host, container, deploy, log line, and commit. IntelliTrace causal inference works only because the graph is whole — not stitched together from samples.
Interactive trace · Every span linked to service, method, commit, and remediation playbook
A user in Mumbai clicks “Pay” on a $148 cart. Response time for /checkout crosses 4 seconds — 3× the baseline for that cohort. Applicare flags the regression before the user abandons.
The trace ID maps the slow request to checkout-svc. The slow span is OrderRepository.findCartLineItems() at 3.6 seconds. Every other span ran within budget.
IntelliTrace recognizes the pattern: an N+1 query loop introduced two days ago in commit 4a7f9d2. ArcIn explains it in plain English — and notes the same pattern caused incident INC-3140 last month.
IntelliTune drafts the fix — a @BatchSize(20) annotation on the entity — opens a PR, and notifies the author. The deploy is auto-rolled back behind your policy gates while the fix awaits review. Zero pages fired. Zero customers churned.
| Legacy APM | OpenTelemetry alone | Applicare | |
|---|---|---|---|
| Sampling | 10–25% head-sampled | Depends on Collector config | Zero head sampling · tail-based by default |
| Root cause | Correlation guesswork | Manual span inspection | Causal inference (IntelliTrace) |
| Attribution | “Slow service X” | Span name + tags | Span → method → commit → author |
| Remediation | Page someone | Page someone | IntelliTune drafts the fix |
| Instrumentation | Proprietary agents | OpenTelemetry SDKs | OpenTelemetry native + auto-instrument |
| Storage | Vendor-locked | You operate it | Managed — or export to your warehouse |
No. Applicare accepts standard OpenTelemetry traces via OTLP/gRPC and OTLP/HTTP. If you’ve already instrumented with OpenTelemetry SDKs, point your Collector at Applicare — data flows immediately. Auto-instrumentation is available for services that haven’t been instrumented yet.
Yes. Applicare is OpenTelemetry-native by design. Your existing Collectors, processors, and exporters continue to work — just add Applicare as a destination. No proprietary agents, no parallel instrumentation, no vendor lock-in.
The ingest pipeline is built for high-throughput tail-based sampling. 100% of error traces and slow traces are retained by default; healthy fast traces can be sampled down via configurable rules so storage cost stays predictable. The rare trace you actually need is always there.
Yes. Lambda functions are first-class trace participants — instrumented via OpenTelemetry Lambda layers or the Applicare SDK. Cold-start latency, downstream service calls, and database hops are stitched into the parent trace automatically.
Yes. Raw spans export to S3, Snowflake, BigQuery, and Databricks. ArcIn queries data natively where you store it — no forced re-ingestion. Long-term retention and ad-hoc analytics happen in your data platform, on your schema.
Trace context propagates across clusters and regions via standard W3C Trace Context headers. A request that begins in your US-east cluster, fans out to a service in EU-west, and finishes at a database in APAC is stitched into one trace — with each hop labeled by its origin cluster.