At first glance, New Relic and Elastic appear to solve the same problem. Both can collect logs, metrics, traces, and application telemetry, and both have evolved into broad platforms for monitoring modern systems. The difference is what each platform is fundamentally trying to be.
New Relic is built specifically for observability. Everything from its data model and query language to its AI capabilities is designed to help engineering teams detect, investigate, and resolve production issues as quickly as possible. The platform brings together APM, infrastructure monitoring, logs, digital experience monitoring, and AI-powered investigations into a single, managed experience.
Elastic takes a broader approach. Observability is one part of a platform that also includes enterprise search, security, SIEM, and XDR. That shared foundation gives Elastic tremendous flexibility. Organizations can use the same Elasticsearch cluster for multiple workloads, extending well beyond observability. The tradeoff is that Elastic is inherently a more complex platform, particularly for teams that don't already have experience with the Elastic Stack.
Those architectural differences are also reflected in how the two products are priced. New Relic's generous free tier makes it easy for small teams to get started, and costs are driven primarily by data ingest and user access as adoption grows. Elastic's pricing depends on how you deploy it. Managed deployments are priced around infrastructure resources or serverless usage, while self-managed deployments let you run the platform on your own infrastructure if you have the expertise to operate Elasticsearch yourself.
Ultimately, this comparison isn't just about observability features. It's about deciding whether you want a dedicated observability platform or a broader data platform that also delivers observability alongside search and security. Throughout this article, we'll compare the two across architecture, APM, infrastructure monitoring, log management, AI capabilities, security, and pricing to help you decide which approach better fits your organization.
| Feature | New Relic | Elastic Observability |
|---|---|---|
| Primary purpose | Full-stack observability platform | Observability + security + search (Elasticsearch-native) |
| Deployment model | SaaS only | Serverless, Hosted, self-managed, air-gapped |
| Free tier | Yes (100GB/month + 1 full platform user, forever) | 14-day trial |
| Pricing model | Per-user + data ingest (GB) | Resource-based (hosted) or usage-based (serverless) |
| OTel support | Yes (native, no surcharge) | Yes (EDOT, first-class, no surcharge) |
| APM / distributed tracing | Yes (primary strength) | Yes (EDOT/OTel-native, ML correlation) |
| Log management | Yes (all logs searchable, $0.40/GB) | Yes (petabyte-scale, tiered storage, AI pattern detection) |
| Infrastructure monitoring | Yes | Yes (400+ integrations) |
| Kubernetes monitoring | Yes | Yes (pod-to-APM correlation) |
| Universal/continuous profiling | Yes (thread profiling via APM agents) | Yes (Universal Profiling, eBPF-based) |
| ML anomaly detection | Yes (Applied Intelligence, alert-driven) | Yes (100+ zero-config jobs, Platinum+) |
| Real user monitoring | Yes (browser + mobile, Gartner Leader) | Yes (browser + mobile APM) |
| Session replay | Yes | No |
| Synthetic monitoring | Yes | Yes |
| LLM observability | Yes (AI Observability, June 2026) | Yes |
| AI investigation | Yes (SRE Agent, Preview Feb 2026) | Yes (AI Assistant, Streams, embedded ML) |
| MCP server | Yes (Preview, Agentic Platform) | Yes |
| Cloud SIEM | Limited (Security RX in preview) | Yes (full SIEM, XDR, endpoint security) |
| Incident management | Alerting + Applied Intelligence | Not included (integrate PagerDuty/OpsGenie) |
| On-call scheduling | Via integrations | Not included |
| Status pages | No | No |
| Self-hosted option | No | Yes (self-managed, air-gapped) |
| SOC 2 Type II | Yes | Yes |
| HIPAA | Yes (Data Plus) | Yes |
| FedRAMP | Yes (Moderate, expanding to High) | Yes (High in process) |
New Relic built NRDB as a single store for logs, metrics, traces, and events, all queryable through NRQL. The investigation workflow that results is the whole pitch: an alert fires, you click from the alert to the relevant APM trace to the surrounding logs to the infrastructure state at that moment, all without leaving a screen or changing query languages. Every product New Relic ships reads from the same database. That unity is what you're paying for, and it shows up most clearly when you're in the middle of an incident and need to move fast.
The pricing reflects that unity in a specific way. OTel support is native and carries no surcharge, which is genuinely different from some competitors. But the seat model compounds with headcount: full platform users at $349/month on Pro get everything, and anyone who needs to investigate APM or infrastructure data during an incident needs a full platform seat. For a team of 15 engineers with rotating on-call, those seats add up before a byte of telemetry applies.
Elastic puts Elasticsearch at the center of everything. Logs, metrics, traces, and security events all flow into the same engine. Kibana sits on top as the visualization layer, with separate sections for APM, Logs, Infrastructure, and Security. The modern platform is unified at the storage layer in a way the old Elastic stack wasn't, and that matters for investigation: when an alert fires, the underlying data is connected.
Where Elastic genuinely differentiates from New Relic is deployment flexibility. Elastic Cloud Serverless means you never touch infrastructure. Elastic Cloud Hosted means you control cluster configuration. Self-managed via Kubernetes gives you full control. Air-gapped and on-premises deployments are possible in ways New Relic, as a SaaS-only product, simply cannot support. For regulated industries where telemetry cannot leave your network boundary, Elastic is often the only enterprise observability option that fits.
The honest tradeoff is that Kibana presents different query interfaces depending on what you're looking at: ES|QL in some contexts, KQL in others. Getting full value from Elastic requires Elasticsearch expertise that most teams build over time rather than arriving with. User reviews consistently flag the learning curve as real, particularly for teams without prior Elasticsearch experience, and this is a meaningful difference from New Relic's more opinionated and immediately usable interface.
| Architectural factor | New Relic | Elastic Observability |
|---|---|---|
| Data storage | NRDB (unified, proprietary) | Elasticsearch (unified, tiered storage) |
| Query language | NRQL (proprietary, unified) | ES\ |
| Data collection | APM agents, eBPF (eAPM), or OTel | Elastic Agent + EDOT (OTel-native) |
| Investigation flow | Single view, cross-signal seamless | Navigate between Kibana sections |
| Deployment options | SaaS only | Serverless, Hosted, self-managed, air-gapped |
| OTel support | Yes (native, no surcharge) | First-class (EDOT, no surcharge, schema preserved) |
| Self-hosted option | No | Yes |
| Cost pressure grows with | Engineer headcount needing full access | Resource provisioning (hosted) or ingest volume (serverless) |
Both platforms focus on telemetry and monitoring. Neither includes built-in on-call scheduling with phone and SMS delivery or customer-facing status pages. Better Stack brings all of that together alongside logs, metrics, and traces, so you can go from alert to post-mortem without switching tools.
From heartbeat monitoring to incident timelines to status pages, one platform for the whole reliability lifecycle. Start free.
Both platforms are genuinely OTel-native, and neither charges a surcharge for it. That shared foundation matters more than it might seem, because it means your instrumentation is portable regardless of which one you pick.
New Relic offers traditional language-specific APM agents alongside its eBPF-based eAPM for zero-code Kubernetes instrumentation. Running both gives you thread-level CPU profiling, showing exactly which function is consuming cycles in production, which is a level of resolution most eBPF-only approaches can't match. Infinite Tracing retains the most significant traces out of 100% of collected data rather than sampling blindly, which matters for teams debugging intermittent performance issues that sampling would miss. APM 360 connects frontend sessions to backend traces so slow page loads trace all the way through to their root cause.
Elastic APM is built around EDOT, the Elastic Distributions of OpenTelemetry. You instrument with standard OTel SDKs, ship to Elastic, and get service maps, trace waterfalls, and RED metrics with no proprietary agent to maintain. Where Elastic has a genuine edge over New Relic in APM is the machine learning layer: latency correlation automatically surfaces which request attributes (region, customer tier, endpoint) are statistically linked to elevated latency, and failure correlation identifies which service attributes correlate with errors. These run continuously on your OTel data without additional configuration, and user reviews consistently cite them as meaningful for reducing investigation time.
Elastic also ships Universal Profiling, an eBPF-based continuous profiler that runs at the kernel level without requiring application instrumentation. It gives you code-level CPU visibility across your entire stack without touching a service. New Relic's thread-level profiling is deeper at the function level but requires the APM agent to be installed per language.
| APM / tracing | New Relic | Elastic Observability |
|---|---|---|
| Instrumentation | APM agents, eBPF (eAPM), or OTel | EDOT / OTel SDKs (auto-instrumentation available) |
| OTel support | Yes (native, no surcharge) | First-class (EDOT, no surcharge) |
| Code-level profiling | Yes (thread profiling via APM agents) | Yes (Universal Profiling, eBPF-based) |
| Continuous ML correlation | Applied Intelligence (alert-driven) | Yes (latency + failure correlation, zero-config) |
| Infinite / full trace collection | Yes (Infinite Tracing) | Configurable |
| Frontend-to-backend correlation | Yes (APM 360) | Via RUM + APM configuration |
| APM pricing | Included in data ingest + user license | Included in Observability subscription |
Both New Relic and Elastic fold APM into a broader pricing model, but you're still paying either by user seat or by subscription tier. Better Stack's tracing is priced purely by data volume with no span indexing fees and no cardinality penalties, and the AI SRE activates automatically during incidents to investigate root cause before you have to ask.
Full-fidelity distributed tracing from every service, priced by volume with no surprises. Explore Better Stack tracing.
This is the section where Elastic's origin story matters most. New Relic is a good logging product. Elastic's log analytics is what Elasticsearch was built for, and at petabyte scale or with complex long-term retention requirements, the difference is real.
New Relic makes every ingested log searchable through NRQL, no separate indexing tier, with AI alert summarization generating a hypothesis when something fires. The 100GB/month free tier absorbs most of what small teams generate. Past that it's $0.40/GB. Long-term retention runs up to seven years without rehydration. The investigation experience is excellent specifically because logs, traces, and infrastructure metrics share the same backend: clicking from a log line to the trace that produced it requires no configuration.
Elasticsearch's inverted index architecture enables full-text search across petabytes of data in milliseconds. Streams, Elastic's agentic log analysis tool released in late 2025, automatically groups logs into patterns, highlights anomalies, and pinpoints spikes without manual configuration. The AI Assistant provides conversational log investigation grounded in your actual observability data and runbooks via RAG.
The tiered storage model is where Elastic changes the economics of log retention in a way New Relic can't match. Recent data stays on hot nodes. Older data transitions automatically through warm, cold, and frozen tiers based on policies you define. Frozen tier provides searchable snapshots, meaning historical data is queryable without full rehydration. For organizations with compliance requirements mandating years of log retention, this architecture is fundamentally different from paying to rehydrate archived logs every time you need to look at them. The logsdb index mode can also reduce stored data footprint by up to 65% through compression, which materially improves the effective cost per GB.
The nuance worth understanding on Serverless: Elastic billing is measured against uncompressed, enriched data at the end of the ingest pipeline, before Elasticsearch's compression runs. Billed volumes are consistently higher than raw source data sizes. This isn't hidden, but it's a common source of first-invoice surprise and worth modeling against your actual data before committing.
| Log management | New Relic | Elastic Observability |
|---|---|---|
| Billing | $0.40/GB (100GB/month free) | Usage-based (serverless) or resource-based (hosted) |
| All logs searchable | Yes | Yes (tiered, searchable snapshots for cold/frozen) |
| Long-term retention | Up to 7 years, no rehydration | Frozen tier with searchable snapshots |
| AI log analysis | AI alert summarization | Streams (agentic, automatic pattern detection) |
| Query language | NRQL | ES\ |
| Scale ceiling | Enterprise | Petabyte-scale |
| Data compression | No | Yes (logsdb, up to 65% storage reduction) |
| Self-hosted | No | Yes |
Both New Relic and Elastic make all ingested logs searchable, but the cost models produce surprises at scale in different ways. Better Stack stores logs in a unified warehouse with SQL querying and no per-event charges. You pay for what you send, and all of it is searchable.
Unified log management with SQL search, live tail, and no indexing surprises. See how it works.
Neither platform charges cardinality penalties on standard metrics, which removes one common source of bill shock from this comparison. What's left is ML depth, Kubernetes integration quality, and the access model.
New Relic's infrastructure agent covers Linux, Windows, and macOS with no-agent cloud integrations for AWS, Azure, and GCP. Raw metrics stay around for 30 days with 13 months of aggregated rollups for trend analysis. Kubernetes monitoring is well-developed. The catch consistent throughout New Relic's model: viewing infrastructure data during an incident requires a full platform seat at $349/month, so engineers who aren't already provisioned can't access the data when it matters most.
Elastic's infrastructure monitoring covers servers, VMs, containers, and serverless environments through 400+ out-of-the-box integrations. The Kubernetes monitoring ties pod and container-level metrics directly to the services running on them: when a pod OOMKills, the APM view for the affected service shows the event in context without manual wiring. That infrastructure-to-APM correlation is well-designed and works once the data sources are connected.
The ML anomaly detection at the Platinum tier is a real differentiator. Over 100 preconfigured jobs activate without manual threshold tuning, learning normal behavior and seasonality from your data. New Relic's Applied Intelligence covers anomaly detection too but is more alert-driven and requires more configuration to reach the same coverage. Elastic has no per-user access restriction for infrastructure data the way New Relic's seat model creates.
| Infrastructure monitoring | New Relic | Elastic Observability |
|---|---|---|
| Cardinality penalties | No | No |
| ML anomaly detection | Applied Intelligence (alert-driven) | Yes (zero-config, 100+ jobs, Platinum+) |
| Kubernetes depth | Yes | Yes (pod-to-APM correlation) |
| Access to view metrics | Full platform user required ($349/month) | No per-user restriction |
| Integration count | 700+ | 400+ |
| Self-hosted | No | Yes |
Both platforms charge for infrastructure telemetry in ways tied to either user seats or subscription tier. Better Stack takes a different approach: no per-host fees, no cardinality penalties, and infra metrics that live alongside uptime monitors, on-call schedules, and incident timelines.
Infrastructure monitoring connected to alerting, on-call, and incident management, all in one place. Get started free.
New Relic has the more complete DEM suite, and the gap is concrete rather than marginal.
New Relic covers Browser RUM, Mobile RUM across iOS, Android, React Native, and Flutter, Session Replay, Synthetic Monitoring, Product Analytics, and Experiments. Session Replay lets you watch exactly what a user experienced when they hit a bug. APM 360 connects frontend sessions to backend traces. New Relic was named a Leader in the 2025 Gartner Magic Quadrant for Digital Experience Monitoring for the second consecutive year, which reflects real recognized maturity in this category.
Elastic covers browser RUM, mobile APM for iOS and Android via Elastic APM agents, synthetic monitoring with multi-step user journey tests, and uptime monitoring. The synthetic testing is well-built and runs from Elastic's globally managed infrastructure. What Elastic doesn't have is session replay. If watching a user session recording to understand what they experienced before a bug is a regular part of your frontend debugging workflow, that gap is real. Product analytics features and auto-captured event funnels are also more limited than New Relic's suite.
| Digital experience | New Relic | Elastic Observability |
|---|---|---|
| Browser RUM | Yes | Yes |
| Mobile RUM | Yes (iOS, Android, React Native, Flutter) | Yes (iOS, Android via APM agents) |
| Session replay | Yes | No |
| Synthetic monitoring | Yes | Yes (multi-step, global infrastructure) |
| Product analytics | Yes | Limited |
| Gartner DEM recognition | Leader, 2025 MQ (2x consecutive) | Not named |
Both companies have invested seriously in AI, but the philosophy is different: New Relic's flagship AI is proactive and autonomous, Elastic's is embedded and continuous.
The SRE Agent, launched February 2026, fires automatically when an alert triggers and starts investigating without anyone prompting it. By the time you open your laptop it has typically identified a likely root cause from APM traces, logs, and recent deployments. The Agentic Platform around it adds a no-code agent builder, orchestration, governance, and MCP support. Applied Intelligence, which groups related alerts and generates summaries, is GA today. The caveat that matters for procurement decisions: the SRE Agent and most of the Agentic Platform remain labeled Preview.
Elastic's AI story runs in two directions simultaneously. The AI Assistant is embedded throughout Kibana and is grounded via RAG in your actual observability data and runbooks, not general model knowledge. Ask it to investigate an alert and it pulls logs, traces, and relevant knowledge base context into a coherent response. Streams groups logs into patterns and highlights anomalies automatically without you starting a conversation.
The continuous ML layer is where Elastic's AI investment is deepest. Zero-config anomaly detection learns normal behavior and seasonality from your data. Latency and failure correlation continuously identify which request attributes are statistically linked to degraded performance. These run persistently on your data, not just when prompted. For security, Attack Discovery uses LLMs to correlate related alerts into comprehensible threat summaries, which has no real equivalent in New Relic's observability stack.
Elastic does have an MCP server, making it accessible to external AI clients. New Relic's MCP server is explicitly developer-facing (Claude, Cursor), while Elastic's is more broadly positioned.
| AI capability | New Relic | Elastic Observability |
|---|---|---|
| Autonomous investigation | Yes (SRE Agent, Preview Feb 2026) | AI Assistant (prompt-driven) |
| Continuous ML anomaly detection | Applied Intelligence (alert-driven) | Yes (zero-config, 100+ jobs, continuous) |
| Log pattern analysis | AI alert summarization | Streams (agentic, automatic) |
| ML latency/failure correlation | Alert-driven | Yes (continuous, zero-config) |
| MCP server | Yes (Preview, developer-facing) | Yes |
| No-code AI agent builder | Yes (Agentic Platform, Preview) | No |
| GA status of flagship AI | Applied Intelligence GA; SRE Agent Preview | AI Assistant GA; Streams GA |
Both platforms have AI investigation features. What neither one includes is a direct path from a root cause hypothesis to an on-call notification and a customer-facing status page update. Better Stack's AI SRE connects to the full incident lifecycle so the investigation and the response happen in the same place.
Autonomous root cause investigation connected to on-call, incidents, and status pages. See the AI SRE.
This is where the platforms diverge most sharply, and if security is part of your evaluation at all, it's the most important section.
New Relic's security story is certification-based rather than product-based. SOC 2, HIPAA on Data Plus, FedRAMP Moderate with a stated expansion toward High. Security RX, previewed in 2026, correlates vulnerability findings with engineering context, but it's a correlation feature sitting on top of an observability platform rather than a built-out threat detection product. If SIEM, XDR, or endpoint security are requirements, New Relic is not in this conversation.
Elastic Security is a full SIEM, XDR, and endpoint security platform built on Elasticsearch. It's named a Gartner Magic Quadrant Leader for Observability Platforms and a Visionary in the Gartner SIEM Magic Quadrant. The detection rules are MITRE ATT&CK aligned and available on GitHub, meaning they're inspectable and community-hardened in a way proprietary rule sets aren't. Attack Discovery uses LLMs to correlate related alerts into comprehensible threat summaries. Elastic Defend provides endpoint protection, and the Elastic AI SOC Engine (EASE) adds AI-driven alert correlation across existing security tooling without requiring a full migration.
The consolidation argument for Elastic is straightforward: if you're evaluating observability and security as a combined procurement decision rather than separate ones, Elastic's architecture was designed for exactly that in a way New Relic's wasn't.
| Security | New Relic | Elastic |
|---|---|---|
| Cloud SIEM | Limited (Security RX in preview) | Yes (full, AI-powered, MITRE ATT&CK) |
| XDR / endpoint security | No | Yes (Elastic Defend) |
| Workload protection (runtime) | No | Yes |
| AI threat triage | No | Yes (Attack Discovery, EASE) |
| FedRAMP | Yes (Moderate, expanding to High) | Yes (High in process) |
| Self-hosted / air-gapped | No | Yes |
| Customer-managed encryption (BYOK) | No | Yes (AWS KMS, Azure Key Vault, GCP KMS) |
New Relic comes closer to owning the incident response workflow through its On-Call product, but neither platform handles the full picture natively.
New Relic's Applied Intelligence groups related alerts and generates AI-driven summaries. SLO tracking monitors error budgets. On-call scheduling comes through New Relic's native On-Call add-on or PagerDuty and OpsGenie integrations. Phone and SMS delivery requires those external tools either way.
Elastic's alerting covers metrics, logs, APM signals, and SLO burn rate conditions. The SLO tracking is well-designed, alerting when you're burning down budget at a rate that threatens your target. Beyond that, on-call scheduling, escalation policies, phone and SMS delivery, and structured incident workflows all require external tools. For five responders on PagerDuty, that adds roughly $245 to $415 a month on top of the Elastic contract.
| Incident management | New Relic | Elastic Observability |
|---|---|---|
| Native incident management | Alerting + Applied Intelligence | No (integrations only) |
| Alert intelligence | Yes (AI grouping, summaries) | Yes (ML-based) |
| On-call scheduling | Via New Relic On-Call or external | Not included |
| SLO tracking | Yes | Yes (native, burn rate alerting) |
| Phone/SMS delivery | Via New Relic On-Call or external | Via PagerDuty/OpsGenie |
| Status pages | No | No |
The pricing structures are different enough that you need to know your actual team size, data volume, and feature needs before the comparison is meaningful.
New Relic's bill has two independent inputs: ingest and seats. A team of 10 engineers all needing full platform access on Pro pays $3,490/month in seat fees before a byte of telemetry counts against the bill. Past the 100GB/month free tier, ingest is $0.40/GB. The seat cost is the variable most teams underestimate at the start of an evaluation.
Elastic on Serverless Observability prices on ingest and retention volume. On Hosted, you pay for provisioned cluster resources. Either way, the Platinum tier at roughly $125/month per instance adds the full AI Assistant and 99.95% SLA, which most independent reviews consider the right starting point for production workloads. The key nuance on Serverless billing: charges are measured against uncompressed, enriched data at the end of the ingest pipeline, not raw source sizes. Teams estimating based on raw data consistently find their actual bill higher than expected.
Scenario: 10 engineers needing full access, 1TB/month telemetry
| Cost component | New Relic (Pro, annual) | Elastic Observability (Hosted, Platinum) |
|---|---|---|
| Full platform user licenses | $3,490/month (10 x $349) | No per-user fees |
| Data ingest (1TB, minus 100GB free) | ~$360/month | Resource-based (cluster sizing) |
| Log management | Included in ingest | Included in subscription |
| APM | Included in ingest | Included in subscription |
| On-call (5 responders, PagerDuty) | ~$245-415/month | ~$245-415/month |
| Estimated monthly total | ~$4,095-4,265/month + cluster costs | ~$245-415/month + cluster provisioning |
The comparison illustrates the seat-cost problem clearly. For a small, lean team monitoring high data volumes, New Relic's ingest-based model can actually be cheaper because the seat count stays low. For a larger engineering org where 10 or 15 people all need investigative access during incidents, Elastic's subscription model (where anyone can view the data without an additional per-person fee) starts looking significantly different.
| Pricing factor | New Relic | Elastic Observability |
|---|---|---|
| Free tier | Yes (100GB + 1 full user, forever) | 14-day trial |
| Per-user fee | Yes (full platform $349/month) | No |
| OTel surcharges | No | No |
| Long-term log retention | Up to 7 years, no rehydration | Frozen tier with searchable snapshots |
| Self-hosted option | No | Yes |
| Hard budget cap | No | No |
Both New Relic and Elastic require separate tools for on-call scheduling with phone delivery and status pages. Better Stack consolidates logs, metrics, traces, on-call scheduling, incident management, and status pages into one platform with one bill.
Fewer vendors, fewer context switches, and a single place for the full reliability workflow. Talk to us.
New Relic gaps worth knowing:
Elastic Observability gaps worth knowing:
The comparison that matters here isn't which platform has more features. It's whether you're buying a purpose-built observability tool or a data platform that happens to do excellent observability alongside other things.
New Relic is the purpose-built tool. If your primary need is engineers debugging production applications with the smoothest possible investigation workflow, New Relic is the stronger product. The NRQL-unified experience, the developer-facing MCP server, the Gartner-recognized DEM suite, and the free tier that genuinely works for small teams all point to a product built around the engineering workflow. The seat cost is the main thing to model carefully before committing: it's the variable that most teams underestimate, and it compounds fast with headcount.
Elastic is the broader platform. If observability is one part of a larger data and security consolidation, if your log volumes are large enough that Elasticsearch's tiered storage architecture changes the retention economics meaningfully, if you need self-hosted or air-gapped deployment, or if SIEM and XDR belong in the same procurement conversation as observability, Elastic's architecture was designed for exactly that combination. The learning curve is real and the configuration overhead is genuine, but the depth is also genuine once your team has built the Elasticsearch fluency to unlock it.
One thing worth modeling explicitly before committing to either: if your engineering team is large and everyone needs investigative access during incidents, New Relic's seat costs may be the deciding factor regardless of any other comparison in this article. And if your telemetry volumes are high enough that long-term log retention is a real budget line, Elastic's frozen tier with searchable snapshots is a fundamentally different economic model than paying to rehydrate archived logs.
Neither New Relic nor Elastic includes uptime monitoring, on-call scheduling with phone and SMS, incident management, and customer-facing status pages as a unified product. Better Stack brings all of that together with logs, metrics, and traces, with usage-based pricing and no per-seat fees.
The full reliability lifecycle in one place. Start free, no credit card required. Try Better Stack.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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