# New Relic vs Axiom: A complete comparison for 2026
Start with a question that sounds simple and is not: what are you actually buying when you pay an observability bill? Are you paying for a place to keep and search everything your systems emit, or for a system that watches those systems on your behalf and tells you when something is wrong? New Relic and Axiom answer that question from opposite ends, and the answer shapes not just what you see on a dashboard but what shows up on your invoice.
**New Relic is a mature full-stack observability suite that meters you on two axes at once, the people who log in and the gigabytes you send.** It has spent more than a decade building agents for 25-plus languages, a query language of its own in NRQL, and a growing layer of agentic AI that now diagnoses incidents before an engineer acknowledges the page. **Axiom is an event data platform that meters you on compute and storage, and treats keeping every event as the default rather than a premium.** It came at the same problem from the storage layer up, compressing machine data so aggressively that retaining all of it stops being the expensive choice.
So which one fits your stack? That depends on a few more questions this comparison works through in detail. Do you want telemetry collection to happen automatically, or are you comfortable instrumenting each service with OpenTelemetry? Do you need the platform to run your incident response, or do you already have that solved elsewhere? And are you optimizing for a familiar, batteries-included suite, or for a lean data layer that your AI agents can query directly? Both tools are strong. They are strong at different things, and the rest of this piece is about telling those things apart.
## Quick comparison at a glance
Before the section-by-section detail, here is the shape of the decision. Read the table as a map of where each platform invests, not as a scoreboard.
| Category | New Relic | Axiom |
|---|---|---|
| Product type | Full-stack observability suite | Event data platform |
| Collection | Agent-based (25+ languages) plus OTel | OpenTelemetry SDK and API only |
| Logs | NRDB, NRQL, 8-day default retention | EventDB with APL, 95%+ compression, no sampling |
| Traces | Agent and OTel distributed tracing | OTel-native, full waterfall, 1M+ spans per view |
| Metrics | Dimensional metrics, NRQL | MetricsDB (GA March 2026), no cardinality tax |
| AI | NRAI assistant (GA), SRE Agent and Autopilot (preview) | MCP server (GA), Spotlight, AI engineering toolkit |
| Incident response | Alerts, AIOps, workflows; integrates out for paging | Monitors and alerts only |
| Status pages | Not native (integration required) | Not in scope |
| RUM | Browser and mobile monitoring included | Not in scope |
| Pricing model | Per user plus per GB ingested | Compute credits plus storage |
| Free plan | 100 GB/month, 1 full user | 1,000 GB data loading, 100 GB storage |
| Enterprise | SOC 2, GDPR; HIPAA/FedRAMP via Data Plus | SOC 2 Type II, GDPR; HIPAA with NDA |
## Platform architecture
Every observability tool eventually reveals what it was designed to be, and the tell is what happens at the edges. Does the platform assume it owns the whole workflow, or does it assume it is one specialized piece of a larger stack? New Relic and Axiom sit on different sides of that assumption, and it explains most of the differences that follow.
### New Relic: an agent-based suite on a single database
New Relic is built around NRDB, a unified telemetry database that stores metrics, events, logs, and traces in one place and exposes all of it through NRQL. Collection happens primarily through New Relic's own agents, which install per language and instrument your application automatically once deployed. The agent approach is why New Relic can offer deep, framework-aware detail out of the box: transaction traces, database query breakdowns, and error capture arrive without you writing instrumentation code.
The breadth is the point. APM, infrastructure monitoring, log management, browser and mobile monitoring, synthetics, and alerting all read from the same database and share the same query layer. If your goal is to open one tool and see the full stack, from a slow SQL query up to a degraded page load, New Relic was designed for exactly that. The tradeoff is that the agents are New Relic's, so the instrumentation you accumulate is tied to the platform unless you standardize on OpenTelemetry, which New Relic also accepts.
### Axiom: an event data platform built from the storage up
Axiom started with a different premise: that the hard part is not the dashboard, it is storing and querying enormous volumes of machine data cheaply and without sampling. EventDB, its columnar store, compresses data 25 to 50 times during ingestion and reports 95%-plus compression, which is why teams can retain everything instead of deciding in advance what to drop. MetricsDB, generally available since March 2026, extends the same object-storage and ephemeral-compute model to time-series data, so high cardinality stops being a billing event.
Collection is OpenTelemetry-native and nothing else. There are no proprietary agents to install; your existing OTel collectors and SDKs route directly into Axiom. That is a strength for teams already standardized on OTel and a real cost for teams that would rather not instrument every service by hand. What Axiom deliberately does not include is the operational layer: no on-call scheduling, no paging, no status pages, no error tracking product, no RUM. Those are not roadmap gaps. They are outside the platform's stated scope, which is unified machine data and, increasingly, AI engineering.
Which philosophy is right for you comes down to a question of ownership. Do you want a vendor to own collection and hand you a finished suite, or do you want to own your instrumentation through OTel and pair a lean data platform with whatever operational tools you already trust?
| Architecture aspect | New Relic | Axiom |
|---|---|---|
| Backend | NRDB (unified telemetry database) | EventDB + MetricsDB |
| Collection | Agents (25+ languages) plus OTel | OpenTelemetry only |
| Compression | Standard | 95%+ (25-50x at ingest) |
| Query language | NRQL | APL (logs/traces/events) + MPL (metrics, preview) |
| Zero-code instrumentation | Partial (agents auto-instrument) | No (OTel SDK required) |
| Operational layer | Alerts, AIOps, workflows | Not in scope |
| Scope | Full-stack suite | Event data platform |
[summary]
### Skip the instrumentation project entirely
Both platforms ask you to instrument before you see anything: New Relic through its language agents, Axiom through OpenTelemetry SDKs on every service. [Better Stack](https://betterstack.com/) takes a third path with an eBPF collector that deploys as a Kubernetes DaemonSet and captures HTTP, gRPC, and database traffic at the kernel level, with no code changes and no per-language SDK to maintain. In a polyglot environment, that removes an entire category of upgrade and version-drift work.
**Instrument once at the kernel, not once per service.** [Start free in minutes](https://betterstack.com/).
[/summary]
## Distributed tracing and APM
Tracing is where the collection philosophy becomes something you can see. Both platforms produce OpenTelemetry-shaped traces in the end, but the path to get there, and what the trace connects to once you have it, differ enough to matter. The question worth asking here: when a request is slow, how many clicks and how many tools stand between you and the span that explains it?
### New Relic: agent-driven APM with deep language detail
New Relic's APM is one of the most established in the market. Its agents capture transaction traces, external service calls, and database queries automatically, and distributed tracing stitches those spans across services into a single view. Because the agents understand common frameworks, you get method-level and query-level detail without configuring it, and the errors inbox groups exceptions back to the transactions that produced them. For teams running mainstream language stacks, the depth per service is hard to beat.
The cost shows up in two places. Verbose agent data contributes to the per-GB ingestion meter, and the richest detail is tied to New Relic's own agents rather than portable OTel instrumentation. New Relic does accept OTel data, so you are not locked out of a standards-based approach, but the deepest experience assumes you are running its agents.
### Axiom: OTel-native tracing with a query-first workflow
Axiom stores every span in EventDB with no sampling, which means the trace waterfall reflects the worst-case request, not an average shaped by what survived ingestion. The Console renders over a million spans in a single trace view in production, and trace IDs are correlated automatically to the logs around them, so you can pivot from a span to its surrounding events in one query with no join. Service and operation dashboards roll the same trace data into an aggregate APM view, with high-cardinality service metrics sitting alongside on MetricsDB.
The differentiator is the query layer. APL expresses span-level aggregations and latency-percentile analysis more concisely than most trace UIs allow, which matters if your work involves slicing traces by custom dimensions, especially in AI engineering where cost-per-token and latency distributions are the whole game. The limitation is the same one that runs through Axiom: every traced service needs OTel instrumentation, and in a polyglot codebase that maintenance is real.
| Tracing feature | New Relic | Axiom |
|---|---|---|
| Instrumentation | Agents (auto) plus OTel | OTel SDK per service |
| Database query detail | Automatic via agents | Via OTel configuration |
| Sampling | Configurable, often applied | None (every span kept) |
| Trace-to-log pivot | Yes, within NRDB | Automatic, no join |
| Query language for spans | NRQL | APL (more expressive for complex analysis) |
| Waterfall view | Yes | Yes (1M+ spans) |
[summary]
### See the whole request, front end to database
New Relic ties its richest tracing to proprietary agents, and Axiom has no front-end monitoring to connect a slow page to the trace behind it. [Better Stack](https://betterstack.com/tracing) captures traces with eBPF and stores them in OpenTelemetry format, then correlates a browser session, the JavaScript error inside it, and the backend trace that caused it in one interface, because RUM, tracing, and logs share the same data layer.
**One trace from the click to the query, no reinstrumentation.** [Explore tracing free](https://betterstack.com/tracing).
[/summary]
## Log management
Both platforms let you keep and query large volumes of logs, and both avoid the per-log indexing fees that make legacy tools painful. The differences are in retention economics, query language, and what the logs are connected to. Ask yourself: how often do you avoid keeping a log line because of what retention will cost, and how much investigation does that avoidance quietly break?
### New Relic: logs in NRDB, queried with NRQL
New Relic log management lands every log in NRDB alongside the rest of your telemetry, so you can pivot from a log to the trace or host it belongs to without leaving the platform. Logs are queried with NRQL, the same language as everything else, and log patterns help collapse noisy repetitive lines into groups. The catch is retention and cost. Default retention on the Original data option is eight days, and extending it to 30, 60, or 90 days requires the pricier Data Plus tier. Because logs are often the largest contributor to ingestion, verbose logging is where New Relic bills climb fastest, and the default agent configuration forwards a lot unless you filter it.
### Axiom: EventDB and APL, built to keep everything
Axiom's premise is that you should never sample logs to control cost. EventDB's compression means retaining full-fidelity logs is the default rather than a budget decision, and every byte you load is queryable on day one with no separate indexing SKU to unlock search. APL is a pipeline-style language that composes filters, transformations, and aggregations in a way that reads naturally for complex event analysis and is more expressive than SQL for many temporal patterns.
Where Axiom pulls ahead specifically is edge and serverless ingestion. Vercel, Cloudflare, AWS Lambda, and Fluent Bit all have first-class paths in, which suits modern serverless architectures well. The tradeoff, again, is that logs live in a data platform, not an operational suite, so what happens after a query surfaces a problem is left to whatever tools you have bolted on around Axiom.
| Log management | New Relic | Axiom |
|---|---|---|
| Backend | NRDB | EventDB |
| Query language | NRQL | APL |
| Default retention | 8 days (90 with Data Plus) | Retention by storage, no forced tiering |
| Sampling | Optional, sometimes needed for cost | None |
| Indexing fees | None (per-GB ingest) | None (per-GB ingest) |
| Serverless/edge ingest | Supported | Excellent (Vercel, Cloudflare, Lambda native) |
[summary]
### Query every log in SQL, keep all of it
New Relic bills logs on the same meter as everything else and defaults to short retention, while Axiom keeps everything but leaves the response workflow to other tools. [Better Stack](https://betterstack.com/logs) stores logs in ClickHouse where 100% of ingested data is immediately searchable with plain SQL, no tiering and no rehydration, and charts build straight from a log query. Because logs live beside traces, metrics, and incidents, the line you found connects to the alert it triggered.
**Full-text and SQL search over every log, priced per GB.** [Search your logs free](https://betterstack.com/logs).
[/summary]
## Metrics and cardinality
The biggest recent change in this comparison is on the metrics side, and it is entirely Axiom's. For years, metrics were the gap that separated Axiom from full observability platforms. That gap closed in March 2026. So the question shifts from "does each platform do metrics" to "how does each one treat the thing that makes metrics expensive, which is cardinality?"
### New Relic: dimensional metrics on the shared meter
New Relic supports dimensional metrics natively and queries them through NRQL like every other data type. For teams already inside New Relic, this is convenient: infrastructure metrics, custom metrics, and APM-derived metrics all live together and correlate with traces and logs. The cost model is the same two-axis structure as the rest of the platform, so metric volume feeds the per-GB ingestion meter. High-cardinality custom events are a known place where bills can jump unexpectedly, and teams have reported multiplied invoices after enabling detailed telemetry without realizing how it was metered.
### Axiom: MetricsDB with no active-series tax
MetricsDB is architecturally different from typical metrics stores. Rather than charging by active time series, which is the mechanism behind most surprise metrics bills, Axiom bills metrics per GB ingested, the same as everything else, with no series count to manage. Track every container, service instance, and GPU, and the cost model does not punish you for the dimensions. Ingest starts at $0.12/GB with volume discounts, and metrics are queryable through the same MCP server and a dedicated metrics skill from day one.
MPL, the Metrics Processing Language, ships alongside the GA release as a public preview. It is a pipeline-based, code-first language purpose-built for time-series work and designed to be readable by humans and composable by AI agents. If you already think in APL and want metrics in the same query surface, or if you are querying through an AI assistant, this is a real fit. If your team lives in PromQL, note that Axiom provides a migration guide rather than native PromQL.
| Metrics | New Relic | Axiom |
|---|---|---|
| Backend | NRDB dimensional metrics | MetricsDB (GA March 2026) |
| Cardinality pricing | Feeds per-GB meter; custom events can spike | Per-GB, no active-series tax |
| Query language | NRQL | MPL (public preview) |
| OTel metrics | Native | Native |
| AI agent access | Via MCP server | Via MCP server + metrics skill |
| PromQL | Not native | Migration guide, not native |
[summary]
### Metrics without the cardinality penalty
New Relic meters high-cardinality custom data in ways that can surprise you, and Axiom's metrics query in MPL rather than the PromQL many teams already know. [Better Stack](https://betterstack.com/infrastructure-monitoring) charges for metrics by volume with no per-series penalty, and speaks PromQL natively alongside SQL and drag-and-drop charts, so a high-cardinality tag like customer_id changes your cost linearly instead of exponentially.
**PromQL-native metrics, priced by volume not by series.** [Chart your metrics free](https://betterstack.com/infrastructure-monitoring).
[/summary]
## AI and agentic features
This is the section where the 2026 story is moving fastest, and where the two platforms are chasing different definitions of what AI in observability should do. One is building an autonomous teammate that runs the incident. The other is building the best possible data surface for the agents you already run. Which matters more to you? That is the question this section is really about.
### New Relic: an assistant, and an SRE agent moving toward autonomy
New Relic AI, its assistant, is generally available and answers questions, writes NRQL from plain English, and summarizes what your data shows. On top of that, New Relic has been building agentic capabilities: an SRE Agent, introduced in preview in February 2026 and described as an always-on teammate that diagnoses incidents and recommends next steps before an engineer acknowledges the page. In June 2026, New Relic announced Autopilot, an out-of-the-box automated SRE agent that triages incidents, identifies root causes, and scopes remediations, along with Ground Truth for grounding your own agents in New Relic's data. The platform supports the Model Context Protocol, and New Relic's own 2026 AI Impact Report claims teams using its AI features resolved incidents 25% faster, a vendor figure worth treating as directional rather than independently verified.
The honest status matters here. The AI assistant is GA. The autonomous SRE capabilities are newer, with the SRE Agent in preview and Autopilot recently announced, so if agentic incident resolution is central to your decision, confirm the current availability for your account rather than assuming general availability.
### Axiom: MCP, Spotlight, and a real AI engineering toolkit
Axiom's AI investments point at a different target. Its MCP server is GA and available as a remote connection at mcp.axiom.co, with an SRE skill and a metrics skill shipping alongside it. The design is the differentiator: cell budgets cap runaway responses, schema introspection exposes only what an agent needs to plan, and result shaping is built around agent token economics so that agents do not burn context on wide real-world data. Spotlight is Axiom's proactive investigation layer, where selecting a region on a chart compares those events against baseline, analyzes every field, and generates an AI summary of what changed.
The genuine standout is AI engineering observability, which New Relic does not match feature for feature. Axiom includes purpose-built tooling for teams shipping generative AI: tracing multi-step agent workflows, evaluating prompts, and tracking LLM cost, tokens, and latency per provider, with GenAI functions in APL that extract prompts and calculate costs without manual JSON parsing. If part of your stack is an LLM product and part of your debugging time goes to unpacking model payloads, that toolkit is a strong reason to look at Axiom.
[SCREENSHOT: Axiom Spotlight AI investigation or MCP server interface]
| AI capability | New Relic | Axiom |
|---|---|---|
| Assistant | New Relic AI (GA) | Via MCP + APL |
| Autonomous SRE agent | SRE Agent (preview), Autopilot (announced 2026) | Not in scope |
| MCP server | Supported | GA (remote, SRE + metrics skills) |
| Proactive investigation | Applied Intelligence / AIOps | Spotlight |
| AI engineering (LLM cost, prompt eval, agent tracing) | Not purpose-built | Purpose-built toolkit |
| Natural language to query | Yes (to NRQL) | Yes (to APL/MPL via MCP) |
## Incident response and alerting
Here is where the shared limitation surfaces, and it is worth stating plainly because it cuts across both tools. New Relic has more incident intelligence than Axiom: alert conditions, anomaly detection, AIOps correlation through Applied Intelligence, and incident workflows that route to Slack, PagerDuty, and ServiceNow. Axiom has monitors and alerting, and not much beyond that on the response side. But ask the question that matters at 3am: once an alert fires, who gets paged, how, and what does your customer see? For both platforms, the honest answer involves another tool.
### New Relic: strong detection, integrated response
New Relic detects and correlates well. Its AIOps reduces alert noise, its workflows push notifications where your team already works, and its incident intelligence connects a firing alert to the telemetry behind it. What it does not natively own is the full paging-and-communication layer. On-call scheduling with phone and SMS escalation, and customer-facing status pages, are typically handled by dedicated tools like PagerDuty and Atlassian Statuspage, which New Relic integrates with rather than replaces. That is a reasonable design if you already run those tools. It is an extra line item and an extra handoff if you do not.
### Axiom: monitors, and then out to other tools
Axiom's incident story is shorter by design. Monitors and alerts exist, and its SRE skill for the MCP server helps agents lower MTTR during an investigation, but on-call scheduling, phone and SMS paging, escalation policies, incident channels, post-mortems, and status pages are all outside the platform. Teams running Axiom pair it with PagerDuty, OpsGenie, incident.io, or similar for the response workflow and a separate status page tool for customer communication.
| Incident capability | New Relic | Axiom |
|---|---|---|
| Alerting and anomaly detection | ✔ | ✔ |
| AIOps / alert correlation | ✔ | ✘ |
| On-call scheduling | ✘ (integration) | ✘ |
| Phone/SMS paging | ✘ (integration) | ✘ |
| Escalation policies | ✘ (integration) | ✘ |
| Post-mortems | ✘ | ✘ |
| Customer status pages | ✘ (integration) | ✘ |
[summary]
### Detection, paging, and status in one place
Both platforms hand off the part of an incident that happens after detection: New Relic routes out to PagerDuty and Atlassian, and Axiom leaves it entirely to other tools. [Better Stack](https://betterstack.com/incident-management) keeps on-call scheduling, unlimited phone and SMS alerts, escalation policies, Slack-native incident channels, automatic post-mortems, and customer status pages in the same platform as the telemetry that triggered them, at $29/month per responder.
**From alert to on-call to status page, without a second vendor.** [See incident management](https://betterstack.com/incident-management).
[/summary]
## Pricing
Pricing is where the two philosophies stop being abstract. New Relic and Axiom both call themselves usage-based, but they meter completely different things, and the right comparison depends on what you actually need each one to do. The question to hold onto: are you paying mostly for people and data, or mostly for compute and storage?
### New Relic: users plus data ingested
New Relic bills on two meters that add together. Every account gets 100 GB of ingest free per month, then $0.40/GB on the Original data option or $0.60/GB on Data Plus, with an extra $0.05/GB to store data in the EU region. On top of ingest, you pay for seats: on Standard the first full platform user is $10 and additional full users are $99 each up to a cap of five, while Pro removes the cap at $349 per user on an annual commitment or $418.80 per user monthly. Core users are $49 each, and basic users are free but read-only. Enterprise is a custom quote.
Two things drive real bills. Logs are usually the largest ingestion contributor, and the default eight-day retention pushes retention-hungry teams toward the pricier Data Plus rate. And because seats and data are additive rather than bundled, spend can move more than expected month to month, particularly in microservices environments where telemetry volume is uneven.
### Axiom: compute credits plus storage
Axiom prices around compute and storage with automatic in-console volume discounts. There is a $25/month platform base, then data-loading compute billed roughly $0.06 to $0.12 per GB (decreasing with volume, with 1,000 GB per month free), query compute billed in GB-hours (100 GB-hours free), and storage at $0.030/GB per month (100 GB free). Enterprise features are add-ons rather than bundled: SSO at $100/month, Directory Sync at $100/month, RBAC at $50/month, and audit logs at $50/month.
The Axiom telemetry number tends to look lower in isolation, and for pure data retention it often is. But a fair total requires adding back the things Axiom does not include. There are no user seats to buy, which is a genuine advantage for large teams, but there is also no incident management, no status pages, no error tracking, and no RUM, so those functions arrive as separate line items from other vendors.
### What the meters miss
Neither pricing model is dishonest, but each hides its real cost in a different place. New Relic's is per-user spend as the team grows and Data Plus for retention. Axiom's is the collection of adjacent tools you still need to operate production, since it prices only the data layer. So the honest comparison is not sticker to sticker. It is: New Relic full-suite spend for your team size and data volume, versus Axiom telemetry spend plus whatever you pay for paging, status, errors, and front-end monitoring.
On compliance and enterprise readiness, the two are close with one asterisk each. New Relic offers SOC 2 and GDPR across the board, and HIPAA and FedRAMP eligibility through the Data Plus option, which makes it the stronger fit for regulated workloads that need those out of a single contract. Axiom carries SOC 2 Type II and GDPR, with HIPAA available under an NDA and a minimum annual spend, and gates SSO, directory sync, RBAC, and audit logs behind paid add-ons rather than bundling them into an enterprise tier. If included SSO and governance controls matter to your procurement process, price those add-ons into the Axiom comparison before deciding.
| Pricing element | New Relic | Axiom |
|---|---|---|
| Model | Per user + per GB ingested | Compute credits + storage |
| Free tier | 100 GB/month, 1 full user | 1,000 GB loading, 100 GB storage |
| Data rate | $0.40/GB Original, $0.60/GB Data Plus | ~$0.06-0.12/GB loading + $0.030/GB storage |
| User seats | $99-$418.80 full, $49 core, basic free | None |
| Retention | 8 days default, 90 with Data Plus | Storage-based |
| HIPAA/FedRAMP | Via Data Plus | HIPAA via NDA; no FedRAMP |
| Enterprise controls | Included | SSO/RBAC/audit as add-ons |
[summary]
### Predictable pricing without per-seat math
New Relic adds a per-user meter on top of data, and Axiom prices only the data layer so the operational tools arrive as separate bills. [Better Stack](https://betterstack.com/) prices telemetry by volume and operations by responder, so one platform covers logs, traces, metrics, RUM, error tracking, on-call, and status pages, and its MCP server connects Claude, Cursor, and other assistants to all of it. Ask "who's on-call?" or "show me 500s in the last hour" in natural language and get an answer across the whole stack.
**One predictable bill for the full operational stack.** [Start free, no credit card](https://betterstack.com/).
[/summary]
# Final thoughts
Circle back to the question this comparison opened with: **what are you actually buying?** If the answer is a **mature, full-stack suite** that collects telemetry for you, understands your frameworks out of the box, and is investing hard in autonomous incident resolution, **New Relic is the stronger fit**, provided you are comfortable with a **two-axis bill that grows with both your team and your data**, and provided you already have on-call and status pages solved. Its regulated-industry story, with **HIPAA and FedRAMP eligibility through Data Plus**, is also the clearer of the two.
If the answer is a **lean, OTel-native data platform** that keeps every event without sampling, closes the metrics gap with no cardinality tax, and gives your AI agents a purpose-built surface to query, **Axiom is compelling**, especially if you ship LLM products and need real AI engineering observability. The caveat is scope: **Axiom prices the data layer honestly and low, but leaves paging, status pages, error tracking, and RUM to other tools**, so the total cost of operating production is spread across a stack you assemble.
So how should you actually choose? Start by listing **what you need the platform to do after an alert fires, not just before**. If that list is long and you want it in one place, weigh how much of it each tool leaves to third parties. If it is short because you already own those tools, the decision narrows to **collection philosophy, query language, and which pricing meter fits your growth**. Both New Relic and Axiom are strong at what they set out to do. **The mistake is assuming either one covers the whole journey from detection to resolution on its own, because neither fully does.**
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