The Tool Desk
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Start by making the comparison fair
Before comparing charts, align the scope as closely as the data allows. Use the same time range and, where available, the same release, environment, route or transaction, and user segment. Make sure you know whether a view represents browser real-user behavior, server-side transactions, or individual spans: those perspectives are connected, but they are not interchangeable.
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- Time and release: Check that both views cover the same period and deployment. A release change can alter latency or errors even when the route is unchanged.
- Environment and route: Avoid comparing production traffic with staging, or one route with an unrelated transaction.
- Population: If one tool is filtered to a browser, device, or user segment, apply an equivalent filter where possible.
- Collection: Check sampling, extrapolation, trace coverage, SDK versions, and collection settings. Missing or selectively collected spans can make two views appear to disagree.
New Relic documents application performance reports and transaction views for server-side analysis, while its Browser monitoring covers page and route behavior. Sentry’s timeseries query supports filtering event data and calculating aggregates. See New Relic’s application performance reports, its Transactions page, New Relic Browser’s summary, and Sentry’s timeseries query documentation.
Decide whether the problem is reliability, latency, or load
Read failures and traffic beside latency. A fast average does not rule out an error problem, and a slow isolated request may matter less than a modest delay affecting a high-volume route.
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- Reliability: Look at errors or failure rate and the number of affected events or requests.
- Latency: Compare the median with a tail percentile such as p95. If p95 rises while the median stays stable, slower experiences may be worsening for a subset of traffic.
- Load and impact: Consider request or event volume alongside duration. High traffic can make a frequently called transaction a larger contributor to total application time than a slower but rare operation.
Sentry timeseries supports counts, averages, percentiles, failure-rate and failure-count aggregates, events per minute or second, and Web Vitals performance and opportunity scores. New Relic APM transaction views pair throughput with duration and Apdex. Use the aggregate that answers the question at hand, and account for sampling or extrapolation in Sentry results.
Read New Relic signals by what they measure
Apdex is relative to a configured threshold
New Relic Apdex uses a configured response-time threshold, T. Responses at or below T are satisfied; responses above T and up to 4T are tolerating; responses above 4T, or responses returning a server-side error, are frustrated. Because errors count as frustrated regardless of speed, a high error rate can coexist with a quick-looking average response time and a poor Apdex score.
Record the configured T before comparing Apdex across applications or periods; the score is threshold-relative. New Relic also cautions that below 100 requests per minute, there may not be enough data for a stable score. These are product-specific interpretation guidelines, not a universal benchmark. See New Relic’s Apdex documentation.
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Choose the transaction ranking that fits the question
New Relic’s transaction rankings answer different operational questions:
- Most time consuming: Finds transactions contributing the most total application time—the effect of call volume multiplied by duration.
- Slowest average response time: Finds transaction types with the longest average duration.
- Apdex most dissatisfying: Highlights transactions associated with the greatest satisfaction impact.
- Highest throughput: Shows which transactions receive the most requests.
A quick but very frequent operation can therefore deserve attention before a slow, rarely used one. New Relic describes these views in its Transactions page guidance.
Separate browser experience from server response
New Relic Browser covers page loads and route changes, browser throughput, JavaScript-error pages, Core Web Vitals, and a frontend-versus-backend view linked to APM. It can also segment behavior by device or user agent. These browser measures describe the user-facing path; a server transaction duration alone does not describe the entire page experience.
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When browser distributed tracing is available, follow browser activity through AJAX Fetch/XHR and associated backend services to connect frontend, network, and server work. New Relic documents single-origin defaults for this coverage; cross-origin resource sharing must be enabled for cross-origin resources. See New Relic’s browser distributed tracing documentation and its browser monitoring overview.
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Use timeseries aggregates to establish the shape of the issue
Filter Sentry timeseries to the route, transaction, release, or context being investigated. Counts and events per minute or second show volume; failure-rate aggregates show reliability; averages and percentiles describe latency; Web Vitals performance and opportunity scores add browser-oriented context. Compare a percentile such as p95 with the median and event volume rather than treating one average as the whole story. Collection and sampling affect how representative a series is.
The available aggregates are documented in Sentry’s timeseries query reference. Availability of an aggregate does not mean every project has configured or collected the corresponding data.
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Use traces to investigate a spike
A Sentry trace groups related spans and may include related errors. Trace metadata can summarize spans, errors, performance issues, logs, and metrics. Open representative traces from the affected period, find the slow span, and inspect its parent and child spans to understand the order and duration of operations. A chart shows that a value changed; the trace provides context for where time was spent.
Use Sentry’s Retrieve a Trace reference and trace metadata reference for the documented trace data.
Check instrumentation before adding custom measurements
Custom context can make spans more useful, but excessive dedicated spans can add noise. Sentry’s JavaScript span-metrics guidance recommends adding contextual attributes to spans or creating dedicated spans where appropriate, and says the older setMeasurement() approach is deprecated in favor of span attributes. Confirm the guidance for the project’s platform and SDK version before applying it; see Sentry’s JavaScript span-metrics documentation.
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Sentry monitor documentation lists transaction-duration and Web Vitals aggregates, including percentiles, as well as failure-rate and Apdex aggregates. This describes supported capabilities, not proof that a particular project has configured or collected each field. See Sentry’s monitor documentation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Follow a symptom through the stack
- Choose the scope: Fix the time range, route or transaction, environment, release, and user segment. Identify whether the initial symptom is in browser data, server transactions, or spans.
- Classify the symptom: Read failures, volume, and latency together. Decide whether the concern is errors, typical response time, a slow tail, or overall load.
- Prioritize the impact: Use aggregate time or throughput to locate large contributors; use average or percentile latency to locate slow behavior; use errors or dissatisfaction to assess user impact.
- Locate the layer: For browser symptoms, segment by device or browser and inspect route timing and JavaScript errors. Follow New Relic browser traces across network and backend work when available. In Sentry, inspect representative traces and their attached errors.
- Test a cause hypothesis: A slow database or external-service span, a frontend JavaScript error, and a release-specific regression suggest different investigation paths. Verify the hypothesis in trace and surrounding context rather than inferring cause from a dashboard correlation alone.
- Recheck after a change: Use the same scope and compare latency distribution, failures, and volume. A changed average by itself does not establish that user experience improved.
Why matching numbers across vendors can mislead
There is no established universal one-to-one mapping between New Relic and Sentry metric names. A browser timing, server transaction duration, and span duration may describe related work while using different boundaries, units, aggregation, sampling, or instrumentation. Even similarly named percentiles or satisfaction measures should be checked against their definitions and collection settings in the actual project.
When both tools are available, compare the same time range and scope, traffic volume, error or failure rate, median and tail latency, and trace/span coverage. Treat names as clues, then verify what each value counts and how it is collected before drawing a cross-tool conclusion.
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