Directory Observability & MonitoringAPM & Distributed TracingFullStory

FullStory

About FullStory

Fullstory provides a platform that captures user behavior data and applies it to analytics, workforce, and data activation products. The platform is described as an Intelligent Digital Experience Platform. It includes Fullstory Analytics, Fullstory Workforce, and Fullstory Anywhere. Fullcapture collects a record of user interactions for use in analysis. StoryAI converts behavioral data into answers to business questions.

Products

Fullstory Analytics: Uncovers digital behaviors to remove friction and improve user experience.
Fullstory Workforce: Improves workflows that shape employees' experience.
Fullstory Anywhere: Activates data to create personalized user experiences.
Fullcapture: Delivers a record of every interaction on web and mobile apps.
StoryAI: Provides answers based on behavioral data rather than assumptions.
Guides and Surveys: Delivers in-product guidance like product tours and collects contextual feedback with NPS and surveys.

Used for

Uncovering where users drop off and the revenue impact
Detecting friction and flagging anomalies
Visualizing how employees use tools in their daily workflows
Sharing troubleshooting details with IT teams to resolve internal support tickets
Integrating behavioral data with a data warehouse or cloud storage

Who it is for

Product, UX, and growth teams
Support, CX, and IT teams
Data teams
Data science and analytics teams building predictive models or using data for GenAI and LLMs

From FullStory's own website, read 2026-10-23. How we build listings.

How to evaluate APM & Distributed Tracing

This is how the CIOPages Research Team evaluates this category. It is not an assessment of FullStory. 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

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Airbrake

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