Whether you’re consolidating tool sprawl, wrestling with ingest costs, or moving to OpenTelemetry, the search for New Relic alternatives is rarely about “good vs. bad.” It’s about fit. Different platforms prioritize different data models, query engines, and pricing levers—and those differences matter once you’re pushing billions of spans and logs per day. This guide is written for technical evaluators who want a neutral, in-depth comparison and a practical path to lower cost without sacrificing visibility.
What New Relic Does Well, and Why Teams Explore Alternatives
New Relic delivers full-stack APM, infrastructure monitoring, logs, RUM, and synthetics atop a unified telemetry store and NRQL query model. Many teams love the integrated experience, quick time-to-value, and rich dashboards. Others explore alternatives when they need: (1) tighter control over cardinality and ingest volume, (2) specific strengths like high-fidelity distributed tracing, or (3) alignment with an OpenTelemetry-first strategy to avoid lock-in. That’s not a knock on New Relic; it’s simply acknowledging that organizations have different priorities and constraints.
Top 10 New Relic Alternatives at a Glance
- Datadog • Full-stack monitoring & security with deep integrations; excels in breadth.
- Dynatrace • AI-assisted root cause and automatic discovery; strong for large, dynamic estates.
- Grafana Cloud • OSS-friendly visualizations plus managed Prometheus/Loki/Tempo.
- Elastic Observability • Powerful search on logs/metrics/APM with flexible self-host or cloud.
- Splunk Observability Cloud • Real-time metrics (SignalFx heritage) with streaming analytics.
- Honeycomb • Event-based observability designed for high-cardinality debugging.
- Lightstep by ServiceNow • Deep tracing for microservices; OTel-native.
- AppDynamics (Cisco) • APM with business transaction focus; strong enterprise governance.
- Sentry • Developer-centric application monitoring and error tracking.
- SigNoz (open source) • OTel-native, self-hosted alternative with manageable TCO at scale.
(We’ll unpack each choice below—what it’s best for, what to watch, and where it shines relative to New Relic.)
New Relic Alternatives to Know About
Datadog
Best for teams prioritizing breadth and a single pane of glass across infra, APM, logs, and security. Datadog’s integration catalog is enormous and its dashboards are polished. The trade-off is cost predictability at scale: cardinality, custom metrics, and uncurated logs can grow spend quickly. If you’re OTel-forward, Datadog’s support is strong, but be mindful of where sampling and enrichment happen so you’re not shipping unfiltered noise.
Dynatrace
Best for automated topology and AI-assisted triage in big, fast-moving environments. Dynatrace automatically maps dependencies and uses its Davis AI to propose root causes. You’ll get value if you need consistent baselining and enterprise guardrails. The flip side is a steeper learning curve and a “batteries included” approach that can feel prescriptive if you want fully bespoke pipelines.
Grafana Cloud
Best for teams committed to the Prometheus/Loki/Tempo stack with beautiful dashboards. Grafana Cloud embraces open systems and makes it easier to run OSS without the toil. It’s flexible and cost-effective when you curate telemetry upstream. Expect more do-it-yourself thinking on data modeling and alert strategy versus an all-in-one APM.
Elastic Observability
Best when powerful search on heterogeneous data is the priority. Elastic’s strength is schema-on-read versatility and scalable log analytics. APM and metrics have matured, and you can run it self-hosted or on Elastic Cloud. You’ll need discipline on index lifecycle management and cardinality to avoid performance and cost surprises.
Splunk Observability Cloud
Best for streaming metrics and low-latency SLOs at enterprise scale. The SignalFx lineage shows in its real-time analytics and alerting. It pairs well with teams who require tight SLO windows and rapid time-to-detect. Bring a plan for log volume control and consistency across Splunk’s broader ecosystem.
Honeycomb
Best for high-cardinality, event-first debugging and “what changed?” workflows. Honeycomb encourages rich, wide events that make outlier analysis and trace-to-log pivoting feel effortless. It’s beloved by teams focused on incident resolution speed. It’s less of an infra command center; you’ll often pair it with other tools for infra metrics or SIEM.
Lightstep by ServiceNow
Best for deep distributed tracing and microservice health narratives. Lightstep’s tracing model makes it easier to understand cross-service regressions. If you’re already aligned with ServiceNow, the platform fit is compelling. Plan for onboarding time to get spans and attributes modeled well; the payoff is strong causality.
AppDynamics (Cisco)
Best for transaction-centric APM and exec-friendly health views. AppD builds clear business transaction hierarchies, useful where LOB stakeholders need traceability to revenue. It’s a dependable enterprise pick with robust role-based governance. The agent-heavy model can require more operational care in containerized and serverless worlds.
Sentry
Best for developer velocity and application error monitoring. Sentry shines at surfacing actionable errors, release health, and performance traces that developers fix quickly. It’s not a full infra/log analytics replacement, but as a complement it frequently reduces MTTR and closes the feedback loop from code to production.
SigNoz (Open Source)
Best for teams wanting an OTel-native, self-hosted stack with predictable costs. SigNoz offers tracing, metrics, and logs without vendor lock-in. You own the data plane and can tune it aggressively. You’ll trade some polish and managed convenience for control and budget stability.
How to Select the Best New Relic Alternative
1) Start with your data model, not the dashboard
Do you prefer wide events (e.g., Honeycomb) or time-series metrics first (e.g., Prometheus/Grafana) with traces/logs in support? New Relic’s NRDB abstracts a lot—swapping tools means picking where you’ll invest: high-cardinality events, streaming metrics, or search-centric logs. The “right” answer is the one that matches your debugging style and SLO enforcement reality.
2) Price the shape of your data, not just the sticker
Most platforms price by some mix of hosts/containers, custom metrics, span/log ingest, retention, and cardinality. Model at least three months of real traffic and ask: What happens if log volume doubles during an incident? What’s the marginal cost of a new label on a high-QPS metric? Run that math before you migrate dashboards.
3) Decide where you’ll exercise control: edge, collector, or backend
Cost and clarity come from control points. If you shape data at the edge (SDKs) or in a collector (e.g., OpenTelemetry Collector), you avoid shipping noise. If you rely entirely on backend-side filtering, you’ll pay to store what you later drop. Most high-performing teams put opinionated logic in the collector.
4) Commit to OpenTelemetry, even if you choose a vendor backend
OTel instrumentation future-proofs your stack and reduces re-platforming risk. Favor vendors with first-class OTLP support and clear docs on sampling, attribute filtering, and resource detection. Use semantic conventions consistently or you’ll re-learn the same lesson in your next migration.
5) Build a “golden path” for SLOs and incident response
Tools don’t fix process. Define SLOs, error budgets, and paging policy before you change vendors. Your alternative should make your golden path easier: fast slice-and-dice, clear service maps, and rapid pivoting between traces, metrics, and logs.
Where Sawmills Helps—Whatever Alternative You Choose
- Cost & availability control: Visualize which services, labels, and teams drive ingest and storage. Apply volume caps and escalation workflows to prevent overages without blinding yourself during incidents.
- Optimization you don’t have to babysit: AI identifies redundant logs, noisy attributes, or mis-labeled metrics; you approve a recommendation and it’s enforced in the pipeline.
- Standards & safety: Enforce consistent log formatting, block PII from egress, and manage cardinality spikes so your backend remains available (especially important for on-prem or self-hosted stacks).
- OpenTelemetry-first: Because Sawmills rides the OTel Collector, you keep vendor flexibility. Switch exporters as your needs evolve—no mass reinstrumentation.
- Multi-collector, multi-pipeline control: Centralize config, rollout, and drift detection for collectors across clusters and regions.
Bottom line: You don’t have to rip-and-replace to get relief. Curate telemetry upstream with Sawmills, then “right-size” the backend that fits your preferred workflow.
Telemetry Expert Tips
- Treat telemetry like product: Set SLOs for your data pipeline (ingest latency, drop rates, sampling accuracy). When you ship a new service, include a telemetry review—schema, attributes, and budgets—just like API design. This keeps costs linear with value, not with traffic spikes.
- Curate labels aggressively: A single unbounded label (e.g., user_id) on a high-QPS metric can 10× series counts overnight. Move high-cardinality context into traces or logs where it’s cheaper, and let Sawmills drop or hash sensitive values automatically.
- Sample with intent, not regret: Keep error and key-transaction traces at 100%, then probabilistically sample the rest. Tail sampling in the collector preserves rare or slow requests while trimming the noise. You’ll cut cost without losing the story you need in incidents.
- Route by value: Not all data deserves premium storage. Send compliance-relevant logs to long-term storage, keep hot SLO metrics in a fast backend, and route verbose debug logs to a cheaper sink—Sawmills can automate these decisions as traffic changes.
- Measure what you spend to save: Instrument your pipeline itself—emit metrics on events dropped, attributes removed, and bytes saved. Showing teams the savings from one-click policies builds buy-in and prevents accidental rollbacks that re-inflate bills.
Conclusion: Choose the Workflow, Then the Vendor
There are excellent New Relic alternatives—Datadog for breadth, Dynatrace for automated causality, Grafana/Elastic for open flexibility, Honeycomb for deep debugging, Splunk Obs Cloud for streaming SLOs, and more. The right choice depends on your debugging style, data shape, and governance needs. No matter which platform you prefer, the biggest wins come from optimizing telemetry before you store it.
If you want lower cost and higher signal without a disruptive migration, add Sawmills as your smart telemetry layer. Then pick the backend that best matches your workflows—confident that your data is clean, compliant, and right-sized.
Next step: Want to see how much noise you can safely cut in a week? Schedule a Sawmills demo and bring a day of real traffic—we’ll show you exactly where the waste lives.
