Overview: Two Different Bets on Observability
New Relic has been around since 2008 — one of the first companies to put application performance monitoring in the cloud. Over the years it expanded from a pure APM tool into a full observability platform covering infrastructure, browser, mobile, synthetics, and log management. In 2020 it made a major pricing pivot: abandoning the old host-based subscription in favor of a consumption model that charges per GB of data ingested plus a per-seat fee for full-platform users. On paper this looked more flexible. In practice, fast-growing engineering teams discovered that the $549/user/month full-platform seat cost quickly became the dominant line item on their invoice — often larger than the data ingest bill itself.
obseria.io is a newer entrant built on a fundamentally different assumption: that OpenTelemetry has won the instrumentation war, and that the future of observability is signal correlation, not siloed dashboards per product category. obseria.io ingests OTLP natively, stores every signal (metrics, traces, logs, profiles) in a unified columnar backend, and uses a machine learning layer to automatically correlate anomalies across signal types into a single incident. There are no proprietary agents, no per-seat fees, and no cardinality limits.
Both platforms can monitor the same production systems. The question is which model makes more sense for your team size, instrumentation strategy, and budget trajectory. This comparison covers the areas that matter most when making that call.
New Relic
Founded 2008
- Mature APM with deep proprietary agents
- Broad product surface: browser, mobile, synthetics
- $549/month per full-platform user
- OTel supported, not OTel-first
- 8-day trace retention on standard plans
obseria.io
OTel-native, no proprietary agents
- OpenTelemetry-first architecture
- Zero per-seat fees — all engineers included
- ML baselines auto-learned, no config needed
- 30-day trace retention on all paid plans
- Unified metrics + traces + logs + profiles
Pricing: Where New Relic Gets Expensive Fast
New Relic's pricing has two independent axes that both grow as your organization scales: data ingest and user seats. You pay $0.35 per GB of data ingested on the standard plan, or $0.50/GB on Data Plus (which adds 90-day log retention, streaming export, and FedRAMP eligibility). On top of that, every engineer who needs to create dashboards, write NRQL queries, set up alerts, or access APM data requires a full-platform user seat at $549/user/month.
The user-seat problem explained
"Read-only" users are free in New Relic, but the access is heavily restricted: they cannot create or edit dashboards, cannot configure alerts, cannot run ad-hoc queries, and have no access to APM detailed views. In practice this means that every on-call engineer, every SRE, every developer who might need to investigate a production incident needs a paid full-platform seat. For a 10-person engineering team, that's $5,490/month before a single gigabyte of data is ingested.
obseria.io has no concept of user tiers. Every person on your account gets full access to every feature — dashboards, alert configuration, trace exploration, log queries, ML-driven incident investigation. You pay only for the volume of telemetry you send. At 500 GB/month ingest, a 10-person team on obseria.io pays roughly $200–600/month total compared to $5,300–5,700/month on New Relic.
Real-world example: 15-person SRE team
A 15-engineer team ingesting 800 GB/month: New Relic = $8,235 + $280 ingest = ~$8,515/month. obseria.io at the same volume = ~$480–960/month. That's over $90,000/year in savings — enough to hire another engineer.Hidden costs to watch for
Beyond seat fees, New Relic charges separately for certain capabilities that obseria.io includes by default. Applied Intelligence (ML-based alert noise reduction and incident correlation) is an add-on in New Relic. Streaming data export to S3 or Azure requires the Data Plus plan. Vulnerability management is a separate product. obseria.io includes all of these in the base plan — no add-ons, no surprise line items at renewal.
OpenTelemetry & Instrumentation Strategy
OpenTelemetry (OTel) is now the de facto standard for instrumentation. All major cloud providers, Kubernetes distributions, and application frameworks ship with OTel support out of the box. The question for any observability vendor is whether they treat OTel as a first-class citizen or as one of several ingest adapters bolted onto a proprietary data model.
New Relic's approach: OTel as an ingest adapter
New Relic accepts OTLP data and has invested meaningfully in OTel compatibility. However, its data model is still built around New Relic's proprietary event and metric taxonomy. When OTel data arrives, it is mapped to New Relic's internal types — and that translation layer introduces semantic gaps. For example, OTel span attributes that don't map to known New Relic fields get stored as generic custom attributes, which can affect how they appear in APM views and NRQL queries. New Relic also continues to actively develop and promote its proprietary language agents (Java, .NET, Python, Ruby, Node.js, Go, PHP) which provide deeper auto-instrumentation than the OTel SDKs — at the cost of tighter vendor lock-in.
obseria.io's approach: OTel-native from day one
obseria.io's entire storage schema is the OTel data model. There is no proprietary layer to translate through. Span attributes, resource attributes, metric exemplars, log body fields — all are stored exactly as they arrive via OTLP and queryable without mapping. This means zero semantic loss and complete portability: if you ever want to switch vendors again, your instrumentation stays the same.
New Relic maps OTLP to its own taxonomy
Some custom attributes lose context after mapping
Already using OTel? Migration takes under 60 minutes.
If your services already emit OTLP data to any backend, switching to obseria.io is a two-line config change in your OTel Collector: swap the exporter endpoint and API key. No code changes. No re-instrumentation. Your dashboards and alerts simply start receiving data in the new format immediately.The lock-in risk of proprietary agents
Every New Relic proprietary agent you deploy is instrumentation that only works with New Relic. The deeper you go — custom attributes, custom events, custom metrics via the New Relic SDK — the harder a future migration becomes. Teams that have used New Relic's APM agents for several years often discover they have tens of thousands of lines of business logic that call New Relic-specific APIs. With obseria.io and OTel SDKs, your instrumentation is vendor-neutral from the start.
APM & Distributed Tracing
Application Performance Monitoring is where New Relic built its reputation. After nearly two decades of development, its APM offering is deep — particularly for teams using New Relic's proprietary language agents. Code-level diagnostics, transaction traces with SQL query analysis, thread profiling, and detailed JVM/CLR metrics are all available. The user experience for navigating from an error to a stack trace is polished and fast.
New Relic APM: strengths and limitations
The strengths are real: New Relic's agents capture more out-of-the-box than most OTel SDKs currently can (though this gap is closing rapidly as OTel matures). The New Relic Lookout view provides a system-wide anomaly overview, and the deployment markers make it easy to correlate performance regressions with code changes.
The limitations are also real. Trace retention defaults to just 8 days on standard plans — meaning if a performance regression is reported by a customer 10 days after it occurred, the traces that would explain it are gone. Adaptive sampling is enabled by default, which means not every trace is captured during high-traffic periods. And span-level anomaly detection — the ability to automatically flag individual spans as anomalous based on historical patterns — requires manual alert configuration rather than being automatic.
obseria.io APM: OTel-native with ML-driven analysis
obseria.io stores traces for 30 days by default on all paid plans, with no sampling on ingested data. Every span is stored. The flamegraph and waterfall views are built around the OTel trace data model, so all standard OTel attributes (HTTP method, DB statement, RPC service) are surfaced automatically without any custom configuration. Service dependency maps are computed in real-time from span relationships.
The key differentiator is span-level anomaly detection: obseria.io's ML engine learns the p50/p95/p99 latency baseline for every unique span signature (service × operation × key attributes) and automatically alerts when a span begins performing outside its normal distribution. This means you get notified about slow database queries or degraded third-party API calls without ever writing an alert rule.
Must configure threshold alerts manually in NR
NR agents provide deeper code-level profiling
Log Management
Both platforms treat logs as first-class observability signals that should be correlated with traces and metrics rather than siloed in a separate search tool. But how they achieve this differs substantially at scale.
New Relic Logs
New Relic Logs indexes log data as part of your general data ingest. The query language is NRQL — the same SQL-like language used across all New Relic products — which makes it familiar if your team already uses New Relic for metrics and APM. Features include live tail, log patterns (automatic clustering of similar log lines), and built-in parsing for common log formats (Apache, nginx, syslog).
The practical limitation is query performance at scale. NRQL executes over an indexed store that performs well for structured data queries but can degrade for high-cardinality full-text search over large time windows. Teams ingesting multi-terabyte log volumes often report that broad full-text queries time out or require narrower time windows to complete. Standard plan retention is 30 days; free accounts get only 8 days.
obseria.io Log Pipeline
obseria.io's log storage is built on a columnar backend optimized for full-text workloads. Queries over terabytes of uncompressed log data return in under 500 ms without requiring pre-indexing or pre-defined field schemas. This matters for incident response: when an outage is happening, you don't want to wait 30 seconds for a grep-style search to complete.
Every log line is automatically enriched with trace context if it was emitted during an instrumented request — meaning you can jump directly from a slow span in the APM view into the exact log lines that fired during that request, without manually correlating by timestamp. Cold-tier S3 archiving extends retention beyond 30 days for compliance or audit use cases.
Performance degrades on broad full-text queries in NR
Requires log forwarder configuration in NR
Alerting & Anomaly Detection
Alert quality — specifically, the ratio of actionable alerts to total alerts — is one of the most important operational metrics for any observability platform. Alert fatigue is a real phenomenon, and it directly affects on-call engineer retention and incident response speed.
New Relic alerting: powerful but manual
New Relic's alerting system is built around NRQL alert conditions: you write a query, define a threshold, and get notified when the threshold is breached. This is flexible and powerful, but requires you to know what to alert on before incidents teach you. Baseline alerting (alerting on deviations from a rolling average) is available, but each baseline condition must be manually configured per signal.
New Relic's Applied Intelligence product adds ML-based alert noise reduction, anomaly detection, and incident correlation — grouping related alerts into unified incidents and suppressing duplicates. The problem is that Applied Intelligence is a premium add-on, not included in the base plan. Teams on standard plans must manage alert noise manually.
obseria.io alerting: ML-first, zero configuration
obseria.io's ML engine observes every metric, trace, and log signal from the moment it arrives and automatically builds a statistical model of normal behavior. Within 24–48 hours of onboarding, baselines are established for every service, every endpoint, and every infrastructure resource — with no alert rules required. You can add threshold alerts on top (obseria.io supports those too), but the anomaly detection layer catches regressions that you wouldn't have thought to write a rule for.
Cross-signal incident correlation is included in every plan: related anomalies across metrics, traces, and logs are automatically grouped into a single incident timeline with a unified root cause analysis view. On-call scheduling and escalation policies are built into the platform — no PagerDuty subscription required for basic on-call rotation, though PagerDuty, OpsGenie, and Slack integrations are all available.
NR requires manually configuring a baseline condition per signal
Requires Applied Intelligence add-on in New Relic
Applied Intelligence add-on required
Alert fatigue is a silent cost
Teams using New Relic without Applied Intelligence average 3–5× more alert notifications per incident than teams with ML-based correlation. Every false alert that wakes an on-call engineer at 3 AM has a real cost — in retention, morale, and response-time quality on the real alerts.Data Portability & Vendor Lock-in
Vendor lock-in in observability has two components: instrumentation lock-in (covered in the OTel section) and data lock-in — the ability to get your historical telemetry data out of the platform if you decide to switch, and in a format that is useful elsewhere.
New Relic data export
New Relic provides data export via two mechanisms: the NerdGraph GraphQL API, which allows querying and exporting data programmatically, and Streaming Data Export to AWS S3 or Azure Blob Storage. The caveat is that Streaming Data Export requires the Data Plus plan ($0.50/GB instead of $0.35/GB) — it's not available on the standard plan. The exported data is in New Relic's internal event format, not standard OTLP, so it cannot be directly replayed into a different observability backend without transformation.
obseria.io data portability
obseria.io stores all telemetry in OTel-native format and provides full raw export at any time, on any plan, at no extra cost. You can configure streaming export to your own S3 bucket, replay data through any OTLP-compatible backend, or query via the REST API. Because the export format is standard OTLP, the data can be imported into any other OTel-compatible platform without transformation. This isn't just a feature — it's an architectural commitment to never holding your data hostage.
Streaming export requires Data Plus plan on NR
NR exports in proprietary event format
How to Migrate from New Relic to obseria.io
Migrating from New Relic to obseria.io takes 1–5 days for most teams — longer if you have deeply custom NRQL dashboards or New Relic proprietary agent instrumentation. Here's the approach we recommend, based on hundreds of team migrations.
Audit your New Relic instrumentation
Export a list of all New Relic agents deployed, custom attributes sent via the New Relic SDK, custom event types, and NRQL dashboards. Flag which services already use OTel SDKs or the OTel exporter for New Relic — those are instant migrations. Agent-instrumented services need OTel SDK replacement (estimate 1–2 hours per service).
Deploy obseria.io's OTel Collector alongside New Relic
Use obseria.io's config generator to produce a Collector configuration for your environment (Kubernetes DaemonSet, Docker Compose, VM binary). Enable the parallel-run mode: the Collector fans out data to both New Relic and obseria.io simultaneously. Your existing dashboards keep working while you validate parity.
Migrate dashboards with the NRQL converter
obseria.io's migration tool accepts New Relic dashboard JSON exports and converts them to native format. NRQL queries are automatically translated (~85% conversion rate). The remaining widgets use a guided editor that suggests equivalent obseria.io query constructs. Most teams complete dashboard migration in half a day.
Recreate alert conditions
Import your New Relic alert policies via the migration tool. obseria.io's ML engine begins learning baselines immediately — within 24–48 hours it produces auto-baselines for every service. You can then deactivate manually-configured threshold alerts that the ML layer has already covered.
Cut over and cancel New Relic
Once you've validated a week of parallel data, flip all Collector outputs exclusively to obseria.io. We help coordinate the New Relic contract cancellation and offer prorated credits for any prepaid period remaining. Most teams see cost savings within the first billing cycle.
Migration support included
obseria.io's onboarding team assists with every step at no extra charge. Book a migration call and we'll walk through your specific stack, instrument your services, and have you live in obseria.io within your first week.Frequently Asked Questions
Is obseria.io a good New Relic alternative?
How much does New Relic cost for a 10-person engineering team?
Can I keep my existing New Relic dashboards when switching to obseria.io?
Does obseria.io support New Relic agents or do I need to re-instrument?
How does obseria.io pricing compare to New Relic at scale?
Is there a free tier or trial for obseria.io?
What happens to my data if I leave obseria.io?
Final Verdict
New Relic is a proven, battle-tested observability platform with nearly two decades of iteration behind it. If your team is small (1–2 engineers who need full access), if you rely heavily on New Relic's proprietary code-level profiling, or if you're deeply embedded in the New Relic ecosystem with no appetite for migration, it may make sense to stay.
For every other scenario — especially for teams that are growing, that have adopted or are adopting OpenTelemetry, or that are seeing their New Relic bill climb due to seat fees — obseria.io offers a materially better unit economics story with a modern architecture that won't require another migration when OTel becomes the universal standard (it already essentially is).
The core question is whether you want to pay for users accessing data that already belongs to you. obseria.io's answer is no. Every engineer on your team should be able to open a dashboard, run a query, and investigate an incident without adding $549/month to the bill.
New Relic is the better fit if…
- Team has 1–2 users who need full platform access
- Deep code-level profiling with proprietary agents is essential
- Browser & mobile monitoring are critical product needs
- You're under 100 GB/month and using the free tier
- Heavy NRQL investment with no migration appetite
obseria.io is the better fit if…
- 3+ engineers need full dashboard and alert access
- You've adopted or are adopting OpenTelemetry
- Per-seat fees are the largest line item on your NR bill
- You want ML anomaly detection without add-ons
- Vendor lock-in and data portability are architectural priorities
See the difference in your own stack.
Start a free 14-day trial. Import your New Relic dashboards, connect your OTel Collector, and run both platforms side-by-side before committing. No credit card. No lock-in.
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