New Relic vs Observe: A complete comparison for 2026

Something changed in this comparison on February 2, 2026, and it is worth putting at the very top: Observe is no longer an independent startup. Snowflake closed its acquisition of Observe that day, in a deal reported at roughly a billion dollars and described as the largest in Snowflake's history, folding an observability platform directly into the largest data-cloud vendor in the market. So the first question to ask is not about dashboards or query speed. It is about what kind of company you want to buy observability from, and what that choice means over the next three years.

New Relic is an independent, full-stack observability suite that has spent more than a decade maturing its agents and its NRQL query language, and it bills you on two meters at once, the data you send and the engineers who need full access. It collects telemetry for you, understands your frameworks out of the box, and has been pushing hard into agentic incident resolution. Observe is a Snowflake-native platform built on a data lake and a proprietary Knowledge Graph, now owned by Snowflake, and it bills per gigabyte with compute included rather than charging per seat. It treats observability as a data problem first, which is exactly the thesis Snowflake bought.

Which one fits your team? That depends on questions this comparison works through in order. Are you comfortable learning a proprietary query language, and if so, which one, NRQL or OPAL? Do you want the platform to collect data through mature agents, or are you standardized on OpenTelemetry already? How much does your future run through Snowflake's data cloud? And when an alert fires at 2am, how much of the response does each platform actually handle before you reach for another tool? Both are strong platforms. The interesting differences are in where that strength is concentrated, and the rest of this piece is about locating it.

Quick comparison at a glance

Read the table below as a map of two philosophies that happen to agree on one thing, that all telemetry should live in one store, and disagree on almost everything else about how you get it there and pay for it.

Platform architecture and the Snowflake question

Both platforms put logs, metrics, traces, and events in one place, so the architecture comparison is not about whether the data is unified. It is about who built the store, what language you use to ask it questions, and who owns the roadmap now. Ask yourself: are you buying observability from a company whose only business is observability, or from a data-cloud giant for whom observability is one workload among many?

New Relic: one database, one language, one independent vendor

New Relic is built on NRDB, a unified telemetry database that holds every data type and exposes all of it through NRQL. When an alert fires, you move from the alert to the APM trace to the surrounding logs to the infrastructure state without changing interfaces or query languages. That coherence is the product. The tradeoff is that NRQL is proprietary, so the dashboards and saved investigations your team builds are tied to New Relic, and the vendor is an independent observability company rather than part of a larger data platform. For teams that want a focused observability partner and one place to ask questions, that independence is a feature, not a limitation.

Observe: a data lake, a Knowledge Graph, and Snowflake underneath

Observe's architecture is more unusual, and now more strategically loaded. All telemetry lands in a data lake that has been built on Snowflake since Observe's inception, which is exactly why the acquisition was a natural fit rather than a bolt-on. On top of that lake, Observe builds a Knowledge Graph that models the relationships between your services, infrastructure, and telemetry, and that graph is what powers its cross-signal correlation and its AI SRE. Snowflake has framed the combined direction as an open-standard architecture on Apache Iceberg and OpenTelemetry, aimed at petabyte-scale telemetry for AI workloads.

The query language is OPAL, a proprietary streaming language purpose-built for time-series and event data. It is expressive for temporal queries that are awkward in standard SQL, and it comes with a real learning curve, since it is not a language your team already knows. So Observe asks you to adopt a proprietary data model and a proprietary language, in exchange for correlation depth and, now, the backing of a company that treats telemetry as a first-class data-cloud workload. If your infrastructure future already runs through Snowflake, that changes the calculus in Observe's favor. If it does not, you are weighing that depth against the dependency.

One architectural fact matters for the sections that follow: Observe is a pure observability platform. It does not include incident management, on-call scheduling, or status pages. New Relic reaches further into the operational workflow but still stops short of customer communication.

Query language and onboarding

Here is a question that rarely makes it into a feature matrix but decides how fast a new hire becomes useful: what language will your engineers actually type at 2am, and how long does it take to learn? Both platforms answer with something proprietary, and the size of the learning curve differs.

New Relic: NRQL, a SQL-like language you can pick up quickly

NRQL reads like SQL with observability-specific functions layered on. Engineers who know SQL are productive in it fast, and New Relic AI can now write NRQL from plain-English prompts, which lowers the barrier further. The cost is portability: NRQL only runs inside New Relic, so the query library you build does not travel. But as proprietary languages go, it is one of the more approachable, and the ramp for a new SRE is short.

Observe: OPAL depth, with a Query Builder to soften the curve

OPAL is more of a departure. It is a streaming, pipeline-style language with temporal operators that go beyond what SQL expresses cleanly, and for senior engineers who want that power, it rewards the investment. For everyone else, Observe provides a visual Query Builder that generates OPAL without requiring you to write it, and every UI action produces an OPAL equivalent, so you can start in the interface and expand into code. That is a thoughtful answer to the learning-curve problem, but the honest version is that OPAL takes anywhere from a few hours to a few days to become fluent in, against NRQL's few hours. When you are hiring an SRE who needs to contribute in week one, which language they already resemble matters.

Instrumentation and deployment

How data gets into a platform decides what stays invisible, because a service nobody instruments never shows up. This is one of the sharper differences between the two, and it comes down to whether the vendor collects for you or hands you OpenTelemetry and a to-do list.

New Relic: agents, eBPF, or OpenTelemetry

New Relic gives you three ways in. Its language agents auto-instrument mainstream frameworks and capture transaction, query, and error detail with no code you write. Its eBPF-based eAPM offers zero-code Kubernetes instrumentation. And it accepts OpenTelemetry natively with no surcharge. That breadth means you can get deep detail out of the box on supported stacks and still standardize on OTel where you prefer portability. The catch is that the richest detail is tied to New Relic's own agents.

Observe: OpenTelemetry, and only OpenTelemetry

Observe's Observe Agent is the upstream OpenTelemetry collector with no proprietary modifications, and it supports over 400 pre-built integrations for cloud, Kubernetes, serverless, and container environments. That is a clean, standards-based story with no lock-in at the collection layer, and the integration breadth is a real advantage for heterogeneous stacks. The limitation is that APM requires each service to be instrumented with OTel SDKs. In a legacy or polyglot environment, any service nobody has instrumented yet is a blind spot, and closing it is manual work. How many of your services are uninstrumented right now because nobody scheduled the time? That number is your coverage gap in Observe.

APM and distributed tracing

Both platforms describe their APM as OpenTelemetry-native, and both correlate traces to logs in one interface. The differences are instrumentation depth and what the platform does with a deployment. When a release makes something slow, how quickly does each one connect the regression to the change that caused it?

New Relic: agent-based depth with Infinite Tracing

New Relic's APM is among the most established anywhere. Language agents deliver thread-level CPU profiling that shows which function is burning cycles in production, a level of in-process detail Observe does not match. Infinite Tracing collects all your traces and retains the most significant rather than sampling blindly, and because RUM and APM share NRDB, frontend-to-backend correlation needs no configuration. Tracing is part of the ingest-and-seats model rather than a separately metered product.

Observe: OTel-native APM with strong deployment tracking

Observe describes itself as the first OpenTelemetry-native APM provider, and its Service Explorer builds RED metrics for every service and database automatically from OTel spans, with no extra configuration once services are instrumented. Its standout is deployment tracking: it correlates deployment markers with performance changes, surfaces new error types introduced by a release, and compares RED metrics across concurrent versions including canary rollouts. You can pivot from a service alert to individual traces and correlated logs in seconds, and traces are retained unsampled for 13 months. The tradeoff is the same one that runs through Observe, which is that every traced service needs OTel instrumentation first, so coverage depends on the instrumentation work being done.

Log management

Logs are where retention economics and query language collide. Both platforms keep everything searchable without forcing you into cold archives, which is a real advantage over tiered-storage tools. The question that separates them: once your logs are all hot, what does it cost to keep them there, and what language do you search them in?

New Relic: flat per-GB, all searchable, no query fees

New Relic charges $0.40/GB past the free tier and makes every ingested log searchable through NRQL with no per-query charge. There are no indexing decisions and no archive to configure, and retention can run up to seven years without rehydration for compliance cases. Because logs share NRDB with traces and metrics, correlation is automatic. The place bills climb is volume: logs are usually the largest contributor to ingestion, and the default eight-day retention nudges retention-hungry teams toward the pricier Data Plus rate.

Observe: always-hot logs, queried in OPAL

Observe keeps all log data always hot, with no indexing, tiers, or retention gymnastics to manage, and it handles log bursts without buffers or pipelines. That is a clean model. The friction points are language and rate. Search is in OPAL rather than SQL, softened by the Query Builder, and the published ingestion rate of $0.49/GiB is roughly five times New Relic's $0.40/GB starting figure. Observe bundles compute into that rate rather than charging separately for query processing, and volume discounts apply on committed subscriptions, so how much you query versus ingest changes the effective gap. If your team runs a query-heavy workload, the compute-included model narrows the difference the sticker rates suggest.

Metrics and infrastructure

Both platforms handle metrics without the cardinality penalties that make host-priced tools painful, so the comparison is about pricing shape and breadth rather than whether high cardinality is affordable. The question worth holding: does the metrics meter reward you for adding the dimensions you actually need?

New Relic: dimensional metrics on the shared meter

New Relic supports dimensional metrics natively, queried through NRQL alongside everything else, so infrastructure, custom, and APM-derived metrics correlate automatically. Cost follows the same two-axis model, with metric volume feeding the per-GB meter. The known caution is that high-cardinality custom events are a place bills can jump unexpectedly, so it pays to understand how detailed telemetry is metered before enabling it broadly.

Observe: metrics from 400+ integrations, priced per DPM

Observe captures metrics across cloud, Kubernetes, serverless, and applications from over 400 pre-built integrations, which is broader out-of-the-box coverage than New Relic's integration catalog. Its Kubernetes experience is one of the more complete in the market, with pod-level visualizations that correlate automatically to the logs and traces in those pods. Metrics are priced at $0.008 per DPM, data points per minute, with 13-month retention, a different model than per-GB. How that lands depends heavily on your scrape interval and metric count, so if you scrape at fifteen-second intervals across a large fleet, model it out rather than trusting the headline rate.

AI SRE, MCP, and the Knowledge Graph

This is the category Snowflake paid for, so it deserves care. Both platforms have shipped an AI SRE and an MCP server, and both are investing hard. The difference is architecture and maturity. One builds its AI on relationship modeling; the other is racing an autonomous agent toward general availability. Which approach earns your trust depends on what you weigh more, depth of correlation or breadth of proven autonomy.

New Relic: an assistant that is GA, an SRE agent that is not yet

New Relic AI, its assistant, is generally available and writes NRQL from plain English, summarizes data, and answers questions. Applied Intelligence, its AIOps layer that groups related alerts and generates summaries, is also GA, and AI Observability for LLM pipelines reached general availability in June 2026. The newer piece is autonomy: an SRE Agent introduced in preview in February 2026, described as an always-on teammate that diagnoses incidents before an engineer acknowledges the page, followed by Autopilot, an automated SRE agent, and Ground Truth, announced in June 2026 for grounding your own agents in New Relic data. The platform supports MCP. The honest status is that the assistant and Applied Intelligence are GA while the autonomous SRE capabilities remain largely in preview, so confirm current availability for your account if agentic resolution is central to your decision.

Observe: an AI SRE built on the Knowledge Graph

Observe's AI SRE is built on its Knowledge Graph, which is what gives it depth. Rather than pattern-matching against flat logs, it traverses the modeled relationships between your services when building an investigation, which is how Observe and Snowflake support the claim of resolving issues up to ten times faster, a vendor figure worth reading as directional. Its MCP server is clever in a specific way: instead of asking an AI agent to write OPAL directly, Observe defined a JSON schema that agents generate, then validates and converts it to OPAL using the Knowledge Graph for semantic correctness. That insulates the agent from OPAL's learning curve while keeping OPAL's power for execution. For teams that want AI investigation grounded in an explicit model of their system, this is a genuine strength, and the Snowflake backing suggests continued investment behind it.

LLM observability

If part of your stack is an LLM product, this section may decide the whole comparison, because monitoring model calls is a real and distinct problem. LLM calls have unpredictable latency, variable cost per request, and failure modes like hallucination and context overflow that do not look like ordinary service errors. Both vendors now address this, which was not true a year ago. So the question narrows: how mature and how dedicated is each one's answer?

New Relic reached general availability for its AI Observability, monitoring for LLM pipelines, in June 2026, bringing model-call visibility into the same NRDB-backed platform as the rest of your telemetry. Observe ships a dedicated LLM Observability product as one of three named pillars alongside its AI SRE and O11y AI, focused on tracking token usage, latency, and cost for LLM-powered systems. Both are credible. Observe's is a longer-standing, purpose-named product, while New Relic's arrives inside a broader suite so the correlation to your application traces is immediate. If LLM monitoring is a near-term requirement, evaluate both directly against your model stack rather than assuming parity.

Incident response and alerting

Here the shared limitation surfaces, and it is worth stating plainly. New Relic reaches further into the response than Observe does, but neither one closes the loop all the way to your customers. The question that matters at 2am: once the alert fires, who gets paged, and what does your status page say?

New Relic: native paging plus AIOps

New Relic detects and correlates well. Applied Intelligence reduces alert noise and generates summaries, SLO tracking watches error budgets, and on-call scheduling is available through New Relic On-Call, giving it a native paging path rather than an immediate handoff to a third party. What it does not include is customer-facing status pages, so external communication during an incident still means a separate tool. For teams that want detection, correlation, and paging in one platform, New Relic covers more of that than most observability suites.

Observe: monitoring and alerting, then out to other tools

Observe's incident story is shorter by design. It generates alerts, surfaces anomalies, and triggers its AI SRE investigation, but it does not include an on-call scheduler, phone or SMS delivery, escalation policies, or post-mortems. Teams using Observe add PagerDuty, at roughly $49 to $83 per user per month, or an equivalent, for the actual response workflow, plus a separate status page tool for customer communication. That is a reasonable design if you already run those tools, and an extra bill and handoff if you do not.

Pricing and enterprise readiness

The two pricing models barely resemble each other, which is the whole point. New Relic meters people and data. Observe meters data and bundles the compute. The right comparison depends on your ratio of engineers to telemetry, and on one new variable: your relationship with Snowflake. So before comparing sticker rates, ask what each platform leaves for you to buy elsewhere.

New Relic bills on two meters that add together. Every account gets 100 GB of ingest free per month, then $0.40/GB on Original data or $0.60/GB on Data Plus, plus $0.05/GB to store in the EU. On top of ingest, full platform users cost $10 for the first and $99 each up to five on Standard, or $349 per user annually and $418.80 monthly with no cap on Pro, while core users are $49 and basic users are free. The seat meter is what grows fastest as a team scales, and Data Plus is what you pay for long retention and for HIPAA and FedRAMP eligibility.

Observe publishes per-GiB starting rates with compute included and no overage fees: $0.49/GiB for logs, $0.59/GiB for traces, and $0.008 per DPM for metrics with 13-month retention, plus $0.01/GiB per month for retention beyond defaults. There are no user seats, which is a real advantage for large teams, and enterprise pricing is a custom quote. The per-GiB rates run several times New Relic's per-GB figure, but the compute-included model absorbs query processing that would be a separate cost elsewhere, and if you already hold Snowflake contracts, the deep Snowflake integration can change the effective economics. What neither headline captures is scope: Observe prices only observability, so incident management, status pages, and error tracking arrive as separate line items, while New Relic reaches into on-call but still needs a status page tool.

On enterprise readiness, both carry SOC 2 Type II and GDPR. New Relic adds HIPAA and FedRAMP Moderate eligibility through Data Plus, plus SSO, SCIM, RBAC, and audit logs, which makes it the clearer fit for regulated procurement, though it offers no on-premises option. Observe inherits Snowflake's governance posture and provides an assigned Data Engineer for enterprise customers, a hands-on model that goes beyond typical account management, and its compliance portfolio may expand under Snowflake over time. Neither currently carries the other's regulated-industry edge cleanly, so match your specific compliance list against both.

Final thoughts

The Snowflake deal makes the underlying choice harder to ignore: what kind of vendor do you want behind your observability, and what are you willing to trade for it? New Relic makes the stronger case when the priority is an independent, full-stack suite with mature telemetry agents, framework-aware instrumentation, native on-call, and HIPAA and FedRAMP eligibility. The tradeoff is a two-axis bill that grows with both headcount and data, along with a proprietary query language that keeps more of your workflow inside the platform.

Observe approaches the same problem from the other direction. Its appeal is a Snowflake-native architecture, deep Knowledge Graph correlation, a dedicated LLM observability product, thirteen months of unsampled traces, and now the roadmap of the largest data-cloud vendor behind it. That combination becomes especially compelling if your infrastructure already runs through Snowflake and your team is comfortable learning OPAL and instrumenting services with OpenTelemetry. But the narrower scope still matters: paging, status pages, and error tracking remain part of a broader stack you have to assemble yourself.

That leaves the decision resting on three practical questions. How much of your future runs through Snowflake? The more that answer grows, the more the economics and integration story lean toward Observe. How quickly can your team become productive in the platform's query language? That affects onboarding, hiring, and how fast engineers can contribute during an incident. And how much of the incident lifecycle do you expect one vendor to own? Neither platform takes you cleanly from the first page through resolution and the final customer update without help. Both New Relic and Observe are strong at what they were built for. The mistake is treating either one as the complete incident stack when the real choice is about which gaps you are more comfortable filling yourself.

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