Here is a fact that upends the usual incumbent-versus-challenger script. Both of these platforms now ship an always-on AI agent that starts investigating the instant an alert fires, building a root cause hypothesis before an engineer has opened the incident. The difference is maturity, and it runs the wrong way from what you would expect. Logz.io's agentic layer, OrionIQ, launched in April 2026 and is available to every customer today, with agents that begin correlating telemetry at alert fire using your runbooks, prior incidents, and deployment history. New Relic's equivalent, the SRE Agent, is designed around the same "diagnose before you acknowledge" idea but remains in preview. The smaller, open-source-rooted vendor got its flagship AI to general availability first.
That reversal is a good way into what actually separates these two, because it reflects their deeper difference. Logz.io is managed open source: it runs OpenSearch for logs, Prometheus for metrics, and Jaeger for traces on your behalf, so you get the open standards, the familiar query languages, and the portability of the open-source stack without operating any of it yourself. Its pricing follows from that, consumption-based on the data you send, with no per-seat fees and unlimited users. New Relic is a proprietary unified platform: everything lands in NRDB and is queried with NRQL, one backend and one language, with breadth and polish that the open-source route does not match, priced on data ingest plus $349 per month for every engineer who needs full access.
So the choice is not really about which one detects problems, since both are capable and both now investigate them with AI. It is about what you want to be locked into and what you want to pay for. Logz.io keeps your telemetry in open formats and charges for data, which suits teams that value open standards, want a real security analytics product alongside observability, and would rather not pay per head. New Relic gives you a single deeply integrated platform with a mature digital experience suite and broad managed coverage, at the cost of a proprietary query layer and a seat bill that grows with your team.
This comparison works through where each of those bets pays off, using current 2026 pricing and product status, and it is honest about the places where each platform simply does not compete.
| Feature | New Relic | Logz.io |
|---|---|---|
| Primary purpose | Unified full-stack observability SaaS | Managed open-source observability + SIEM |
| Foundation | Proprietary (NRDB, NRQL) | Open source (OpenSearch, Prometheus, Jaeger) |
| Free tier | Yes (100GB/month + 1 full user, forever) | 14-day free trial (no card) |
| Pricing model | Per-user + data ingest (GB) | Consumption ($/GB/day) or subscription, no seats |
| Per-user fees | Yes ($349/month full platform, Pro) | No (unlimited users) |
| Query language | NRQL (proprietary) | OpenSearch/Lucene, PromQL, Jaeger (open) |
| Log management | Yes (all searchable, $0.40/GB, no query fees) | Core strength (OpenSearch-backed) |
| Infrastructure monitoring | Yes (seat-gated) | Prometheus-based (PromQL, Kubernetes 360) |
| Distributed tracing | Yes (thread profiling, Infinite Tracing) | Yes (Jaeger-based, App 360) |
| Real user monitoring | Yes (Gartner DEM Leader, 2x) | No |
| Session replay / synthetics | Yes | No |
| Cloud SIEM / security analytics | Security RX (preview, correlation) | Yes (full Cloud SIEM product) |
| AI investigation | SRE Agent (Preview) | OrionIQ agents (GA, April 2026) |
| AI assistant / RCA | Applied Intelligence (GA) | AI Agent + RCA (Observability IQ) |
| MCP server | Yes (Preview) | Yes (GA) |
| Incident management / on-call | Applied Intelligence + New Relic On-Call | Alerting only (PagerDuty/Opsgenie) |
| Status pages | No | No |
| Data optimization | Drop rules | Data Optimization Hub (avg 32% cut) |
| SOC 2 Type II | Yes | Yes |
| ISO 27001 / PCI DSS | No / No | Yes / Yes (Level 1) |
| HIPAA / FedRAMP | Yes (Data Plus) / Yes (Moderate) | Yes / No |
The split here is open standards managed for you against a proprietary unified backend, and it shapes portability, query languages, and lock-in.
New Relic puts logs, metrics, traces, and events into NRDB and makes all of it queryable through NRQL. The investigation experience follows directly: an alert fires, and you click from the alert to the APM trace to the surrounding logs to the infrastructure state without switching interfaces or query languages, because everything shares one backend. OpenTelemetry is native with no surcharge, ingest runs $0.40/GB past the free tier, and the free tier itself (100GB per month and one full platform user, forever) is usable indefinitely for a small team. The cost concentrates in seats at $349 per month per full platform user on Pro. The tradeoff for that unity is lock-in: NRQL is proprietary, and every dashboard and saved investigation your team builds lives inside the platform.
Logz.io's Open 360 platform is built on open-source foundations that it operates for you: OpenSearch for log management with Lucene and OpenSearch Dashboards, Prometheus-compatible metrics with PromQL, and Jaeger for distributed tracing, unified into one interface with Kubernetes 360 and App 360 views on top. The appeal is open standards without the operational burden of running the stack yourself: you keep the query languages your team already knows, your telemetry stays in portable formats rather than a proprietary store, and Logz.io handles scaling, upgrades, and availability. Pricing is consumption-based on the data you send, or a subscription with unlimited volume, and either way there are no per-seat fees and unlimited users.
The tradeoffs against New Relic are real. Open 360 stitches several open-source engines together rather than presenting the single-store, single-language cohesion of NRDB, so the experience, while unified, sits on more moving parts underneath. And because pricing is data-and-retention driven with a daily consumption rate, forecasting depends on modeling your volume and how long you keep each data type, where New Relic's flat per-GB ingest is simpler to predict. What Logz.io buys you is freedom from query-language lock-in and a cost model that never charges for a human to log in.
| Architectural factor | New Relic | Logz.io |
|---|---|---|
| Data model | Unified proprietary store (NRDB) | Managed open source (OpenSearch, Prometheus, Jaeger) |
| Query languages | NRQL (proprietary) | Lucene/OpenSearch, PromQL, Jaeger |
| Lock-in surface | NRQL + hosted data | Minimal (open formats and languages) |
| Pricing mechanism | Ingest (GB) + seats | Consumption ($/GB/day) or subscription |
| Per-seat cost | Yes ($349/month full user) | None (unlimited users) |
| Operational burden | None (fully managed) | None (fully managed) |
| Cost grows with | Engineer headcount + ingest | Data volume + retention |
New Relic gates full access behind seats and Logz.io stops at alerting, but neither one carries you from a firing alert to the engineer who needs paging and the customers who need an update. Better Stack folds on-call scheduling, phone and SMS escalation, incident timelines, and status pages into the same platform as your logs, metrics, and traces.
One platform from the first heartbeat check to the closing post-mortem. Start free.
Both trace requests across services. New Relic goes deeper into the process; Logz.io keeps things standards-based on Jaeger.
New Relic offers language-specific APM agents with thread-level CPU profiling that shows exactly which function is consuming cycles in production, plus eBPF-based eAPM for zero-code Kubernetes instrumentation. Infinite Tracing collects 100% of your traces and retains the most significant ones instead of sampling blindly. APM 360 ties frontend sessions to backend traces in one interface, and because RUM and APM share NRDB, that correlation needs no configuration. Tracing is included in the ingest-and-seats model rather than metered separately.
Logz.io Distributed Tracing is built on Jaeger and OpenTelemetry, with App 360 providing service-level views, latency analysis, and trace-to-log correlation across the same platform. Because it is OTel and Jaeger native, moving traces in is a collector configuration rather than a proprietary agent rollout, and your instrumentation stays portable. Tracing is priced by span volume in the consumption model, around $0.16 per million spans per day with a 10-day retention window, so sampling strategy drives both fidelity and cost.
The limitation against New Relic is depth. Logz.io shows you the spans your OpenTelemetry instrumentation emits, not the in-process, method-level call stack that New Relic's language agents capture, and there is no thread-level profiling. For most distributed-systems debugging the span data is enough, and the open-standards portability is a genuine plus. For pinpointing a slow method inside a service, New Relic reaches a layer that Jaeger-based tracing does not.
| APM / tracing | New Relic | Logz.io |
|---|---|---|
| Instrumentation | APM agents, eBPF, or OTel | OpenTelemetry + Jaeger |
| Code-level profiling | Yes (thread profiling) | No (span-level only) |
| Trace retention / model | Infinite Tracing (smart retention) | Span volume, 10-day window |
| Standards / portability | OTel supported | Jaeger + OTel native |
| Frontend-to-backend | Seamless (shared NRDB) | Backend traces (no native RUM) |
| APM pricing | Included in ingest + user license | Per span volume ($0.16/1M/day) |
New Relic charges per engineer who needs to look, and Logz.io meters traces by span volume within a short retention window. Better Stack's eBPF tracing captures HTTP, gRPC, and database traffic at the kernel level with zero code changes, priced purely by data volume, and its AI SRE investigates root cause automatically the moment an incident fires.
Full-fidelity distributed tracing from every service, billed by volume with nothing hidden. Explore Better Stack tracing.
Logs are Logz.io's heartland, the product it grew up on, and its OpenSearch foundation is a genuine strength. The comparison turns on query familiarity, retention economics, and cost predictability.
New Relic charges $0.40/GB past the free tier, and every ingested log is searchable through NRQL with no per-query charge. There are no indexing decisions, no tier routing, and no archive to configure. Run fifty broad queries during an incident or five narrow ones and the bill is identical. Seven-year retention without rehydration covers compliance cases, and because logs share NRDB with traces and metrics, correlation is automatic. The flat, ingest-based rate is also the simplest to forecast: you pay once for the gigabyte, not per day it sits in storage.
Logz.io runs managed OpenSearch with Lucene and OpenSearch Dashboards, so any team coming from the ELK stack is productive immediately and can often bring existing queries and dashboards along. You get fast search, smart filtering, auto-complete, and customizable dashboards, plus a Data Optimization Hub, LogMetrics, and drop filters that let you cut low-value data before it is stored, which Logz.io says removes an average of 32% of customers' data and cost. Retention is tiered, and archiving to your own storage keeps compliance logs cheap.
The place to model carefully is the pricing unit. Logz.io's consumption rate is roughly $0.92 per ingested GB per day for logging with 7-day hot retention, rising to about $1.61 for 30-day retention, so cost is a function of both volume and how long you keep data, which is more variable to forecast than New Relic's flat $0.40/GB ingest. Against that, Logz.io charges nothing per seat, so unlimited engineers can search, and the data-optimization tooling is more sophisticated than New Relic's drop rules. For heavy log users who actively prune and tune retention, Logz.io's model rewards the effort; for teams that want a single predictable per-GB number, New Relic is simpler.
| Log management | New Relic | Logz.io |
|---|---|---|
| Backend | NRDB (proprietary) | Managed OpenSearch |
| Pricing unit | $0.40/GB ingested (flat) | ~$0.92/GB/day (7-day), ~$1.61 (30-day) |
| Query language | NRQL | Lucene / OpenSearch Dashboards |
| All logs searchable | Yes | Yes |
| Data optimization | Drop rules | Data Optimization Hub (avg 32% cut) |
| Seat gate on search | Yes (full platform seat) | No (unlimited users) |
| Retention | Up to 7 years, no rehydration | Tiered + archive to storage |
New Relic prices log access per seat, and Logz.io bills per gigabyte per day of retention. Better Stack stores logs in a unified, ClickHouse-backed warehouse with SQL search and live tail, no per-event charges, no per-day retention meter, and no seat gate on who can query.
Unified log management with SQL search, live tail, and no indexing surprises. See how it works.
Both cover cloud infrastructure and Kubernetes. The differences are the metrics engine, the query language, and who is allowed to look.
New Relic covers Linux, Windows, and macOS with agentless cloud integrations for AWS, Azure, and GCP, plus solid Kubernetes monitoring, all included in the ingest-plus-seats model with no per-host fee. The consistent caveat applies: viewing infrastructure data during an incident requires a full platform seat, so an engineer without one provisioned cannot pull up host metrics at 2am. The data is there; access is priced per person.
Logz.io's Infrastructure Monitoring is Prometheus-based, so it speaks PromQL and ingests from existing Prometheus and OpenTelemetry setups without re-instrumentation, and Kubernetes 360 gives dedicated cluster, node, pod, and namespace views. Metrics are priced by time series, roughly $0.40 per 1,000 time-series metrics per day with 18-month retention, which rewards controlling cardinality and can get expensive if labels like pod ID or customer ID multiply your series. There are no seats, so every engineer views infrastructure data at no extra cost, and because it is Prometheus-native, teams already standardized on Prometheus and Grafana find it familiar. What it does not match is New Relic's breadth of managed cloud integrations and the polish of agentless setup that pulls thousands of cloud metrics with a few clicks.
| Infrastructure monitoring | New Relic | Logz.io |
|---|---|---|
| Metrics engine | NRDB | Prometheus-compatible |
| Query language | NRQL | PromQL |
| Pricing model | Included in ingest + user license | Per 1,000 time series/day |
| Access to view metrics | Full platform seat required | All users, no seat model |
| Kubernetes | Yes | Yes (Kubernetes 360) |
| Cloud integrations | AWS, Azure, GCP (agentless) | Prometheus/OTel + integrations |
New Relic gates infrastructure views behind seats and Logz.io meters metrics by time-series volume. Better Stack charges no per-host fees and no cardinality penalties, and its infrastructure metrics sit next to uptime monitors, on-call schedules, and incident timelines rather than in a separate silo.
Infrastructure monitoring connected to alerting, on-call, and incident response in one place. Get started free.
This is the section where the usual order is reversed, and it deserves an honest look because both platforms built serious agentic AI in the last year.
New Relic's SRE Agent, introduced in February 2026, is designed as an always-on teammate that begins diagnosing an incident before an engineer has acknowledged the page, querying traces, reviewing logs, checking recent deployments, and producing a root cause hypothesis. The Agentic Platform adds a no-code agent builder, orchestration, an evaluation engine, and MCP support. Applied Intelligence, which groups related alerts and generates summaries, is generally available, and AI Observability for LLM pipelines reached general availability in June 2026. The caveat is maturity: the SRE Agent, much of the Agentic Platform, and the MCP server remain in preview, so the flagship autonomous piece is still a bet.
OrionIQ is Logz.io's agentic observability platform, launched in April 2026 and available to all customers. Its design mirrors New Relic's ambition closely: agents begin working when an alert fires rather than waiting for a human to start them, immediately analyzing telemetry, correlating signals across logs, metrics, and traces, and building a root cause finding using the team's runbooks, prior incident history, and deployment context, so the investigation is underway before an engineer opens the incident. Alongside it, the AI Agent, part of Logz.io's Observability IQ suite, gives a chat-based natural-language interface inside Explore, Kubernetes 360, and App 360, and the Root Cause Analyzer uses GenAI to diagnose exceptions and recommend fixes. Logz.io also ships a generally available MCP server that connects Claude, ChatGPT, Cursor, or any MCP-compatible agent to its logs, metrics, dashboards, and alerts using existing account tokens, which it positions as a first-mover native integration with leading LLM clients. Agentic usage is priced separately by tokens or per invocation.
The honest reading is that Logz.io reached general availability ahead of New Relic on two fronts that matter: the autonomous, fire-on-alert agent and the MCP server, both of which remain in preview on New Relic's side. New Relic's counterweight is the surrounding platform: Applied Intelligence has been generally available and battle-tested for far longer, AI Observability for LLM monitoring is GA, and the Agentic Platform aims at a broader build-your-own-agent story. For autonomous investigation and MCP access you can turn on today, Logz.io is ahead; for breadth and track record across the wider AI operations surface, New Relic still leads.
| AI capability | New Relic | Logz.io |
|---|---|---|
| Autonomous investigation | SRE Agent (fires on alert, Preview) | OrionIQ (fires on alert, GA April 2026) |
| Alert grouping / summaries | Applied Intelligence (GA, mature) | AI Agent + RCA |
| Natural language assistant | Agentic Platform (Preview) | AI Agent / Observability IQ (GA) |
| MCP server | Yes (Preview) | Yes (GA) |
| Organizational context | Yes | Yes (runbooks, incidents, deploys) |
| LLM / AI observability | AI Observability (GA, June 2026) | Limited |
| No-code agent builder | Yes (Agentic Platform, Preview) | No |
| AI pricing | Included / tiered | Tokens or per invocation |
Both platforms now investigate autonomously, and both start at alert fire. What neither includes is a direct line from a root cause finding to an on-call notification, an incident timeline, and a 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 wired straight into on-call, incidents, and status pages. See the AI SRE.
Neither platform is primarily a security vendor, but this is where Logz.io has a product New Relic does not, and where their compliance certifications diverge.
Logz.io ships a full Cloud SIEM built on the same platform, consolidating, prioritizing, and investigating security events with prebuilt rules and threat intelligence, priced from around $1.27 per daily ingested GB including log management, with a security add-on around $0.35/GB/day. It carries a broad compliance set, PCI DSS Level 1, SOC 2 Type II, HIPAA, GDPR, and ISO 27001, which suits teams that want observability and security analytics under one roof with the certifications to match.
New Relic's security story is narrower: Security RX, in preview, correlates vulnerability findings with engineering context rather than acting as a SIEM or providing runtime protection. New Relic holds SOC 2 Type II, HIPAA on Data Plus, and FedRAMP Moderate with expansion to High underway, but it has no ISO 27001 or PCI DSS. So the two split the compliance ground: New Relic owns FedRAMP for federal work, while Logz.io owns PCI and ISO 27001 and offers an actual SIEM. For a team that needs security analytics alongside observability, this section alone can decide the comparison in Logz.io's favor.
| Security and compliance | New Relic | Logz.io |
|---|---|---|
| Security analytics / SIEM | Security RX (preview, correlation) | Full Cloud SIEM product |
| SOC 2 Type II | ✔ | ✔ |
| ISO 27001 | ✘ | ✔ |
| PCI DSS | ✘ | ✔ (Level 1) |
| HIPAA | ✔ (Data Plus) | ✔ |
| FedRAMP | ✔ (Moderate, expanding to High) | ✘ |
Neither platform owns the full incident lifecycle, and both lean on external tools for reliable paging.
New Relic's Applied Intelligence groups related alerts and generates AI-driven summaries, SLO tracking watches error budgets, and on-call scheduling comes through New Relic On-Call or external integrations, giving it a native paging path.
Logz.io alerts on logs, metrics, traces, and security events, with anomaly detection and routing to Slack, PagerDuty, Opsgenie, Microsoft Teams, and webhooks, and OrionIQ enriches those alerts with automated root cause findings. What it does not include is the paging layer itself, no on-call scheduling, escalation policies, or phone and SMS delivery, so getting the right person paged reliably means adding PagerDuty or Opsgenie. Neither platform offers status pages, and neither closes the loop from a detected problem to a coordinated, customer-facing response on its own.
| Incident management | New Relic | Logz.io |
|---|---|---|
| Alert intelligence | Applied Intelligence (GA) | OrionIQ + anomaly detection |
| Notification channels | Broad | Slack, PagerDuty, Opsgenie, Teams, webhooks |
| On-call scheduling | Via New Relic On-Call or external | Not included |
| Escalation policies | Via New Relic On-Call or external | Not included |
| Phone / SMS delivery | Via New Relic On-Call or external | Via external tools |
| Status pages | No | No |
The models diverge on the axis that usually dominates the bill: people. New Relic charges per seat; Logz.io charges for data and nothing for users.
New Relic charges $349 per month per full platform user on Pro, plus $0.40/GB ingest past the 100GB free tier. Fifteen engineers who all need full access pay $5,235 per month in seats before any telemetry is counted. Logz.io has no per-seat fees at all: its consumption model bills for data by type and retention, roughly $0.92/GB/day for logs with 7-day retention, $0.40 per 1,000 time-series metrics per day, $0.16 per million spans per day, and Cloud SIEM from $1.27/GB/day, with agentic AI billed by tokens or invocations. Subscription plans offer unlimited daily volume and users with overage at 1.4 times the rate, and there is a 14-day free trial with no card.
Scenario: 15 engineers needing full access, moderate telemetry across logs, metrics, and traces
| Cost component | New Relic (Pro, annual) | Logz.io |
|---|---|---|
| User licenses | $5,235/month (15 x $349) | $0 (unlimited users) |
| Logs | ~$160/month ($0.40/GB ingest) | Consumption ($/GB/day by retention) |
| Metrics / traces | Included in ingest | Metered per series / per span |
| AI investigation | Included / tiered | Tokens or per invocation |
| On-call (5 responders, PagerDuty) | Via New Relic On-Call or ~$245-415/month | ~$245-415/month (required) |
| Estimated monthly total | ~$5,640-5,810/month | Data + AI usage, no seat component |
The structural difference matters more than the exact totals. New Relic's bill is anchored to headcount, so a fifteen-engineer team pays over $5,000 a month in seats before telemetry, while Logz.io charges those same fifteen engineers nothing and bills only for data and AI usage. Whether Logz.io's total lands higher or lower depends heavily on your volume and, crucially, your retention windows, since its per-GB-per-day model makes long retention expensive where New Relic's flat ingest includes generous retention. The honest guidance is to model your real data volume and retention against Logz.io's consumption calculator, and your real headcount against New Relic's seats, because those two numbers, not the feature lists, usually decide it.
One point each way: New Relic's forever-free tier (100GB and one full user) is the most generous evaluation path in managed observability, and Logz.io's no-seat model plus its Data Optimization Hub, which trims an average of 32% of ingested data, are built precisely to keep a data-priced bill under control.
| Pricing factor | New Relic | Logz.io |
|---|---|---|
| Free option | 100GB + 1 full user, forever | 14-day trial (no card) |
| Per-user fee | $349/month (full platform, Pro) | None (unlimited users) |
| Billing driver | Ingest + seats | Data volume + retention |
| Retention cost | Included (up to 7 years) | Per-day meter (tiered) |
| Data optimization | Drop rules | Data Optimization Hub (avg 32% cut) |
| Predictability | High (flat ingest + seats) | Variable (volume x retention) |
| Cost scales with | Engineer headcount + ingest | Data and retention, not people |
New Relic charges per engineer, and Logz.io meters data by the gigabyte per day of retention. Better Stack consolidates logs, metrics, traces, on-call scheduling, incident management, and status pages into one platform with straightforward usage-based pricing and no per-seat fees.
Fewer vendors, no seat math, and a single home for the whole reliability workflow. Talk to us.
This section is short because it is asymmetric, and saying so is more useful than pretending otherwise.
New Relic carries a mature, analyst-recognized digital experience suite: Browser RUM, Mobile RUM across iOS, Android, React Native, and Flutter, Session Replay, Synthetic Monitoring, and Product Analytics, all correlated to backend traces through the shared NRDB, and it is a two-time consecutive Gartner Magic Quadrant Leader for DEM.
Logz.io does not offer real user monitoring, session replay, or synthetic testing. Its scope is backend observability, logs, metrics, traces, and security analytics, so the correlation story stops at the server side. If frontend experience monitoring is part of what you are evaluating, this is a decisive point in New Relic's favor that no pricing or open-standards advantage on the backend offsets. If your frontend monitoring lives elsewhere or you do not need it, the gap is irrelevant and you can weight the rest of the comparison accordingly.
| Digital experience | New Relic | Logz.io |
|---|---|---|
| Browser RUM | Yes (Gartner DEM Leader, 2x) | No |
| Mobile RUM | Yes (iOS, Android, RN, Flutter) | No |
| Session replay | Yes | No |
| Synthetic monitoring | Yes | No |
| Frontend-to-backend correlation | Seamless (shared NRDB) | Not applicable |
New Relic gaps worth knowing:
Logz.io gaps worth knowing:
The clearest way to separate these platforms is to look at what each one asks you to give up.
Logz.io preserves openness and removes seat-based pricing, but leaves a major gap in frontend visibility. Its managed OpenSearch, Prometheus, and Jaeger stack keeps teams close to familiar standards, while the integrated Cloud SIEM and generally available AI investigation layer extend it beyond traditional observability. The compromise is no frontend monitoring, retention costs that rise with daily data volume, and a platform built across several underlying engines.
New Relic closes that visibility gap, but at the cost of higher lock-in and a bill tied partly to headcount. Its RUM, session replay, synthetics, code-level profiling, Applied Intelligence, and FedRAMP coverage create a broader and more unified experience. In exchange, teams accept $349-per-seat pricing and a proprietary query layer that makes moving away more difficult.
That makes the decision less about feature count and more about which limitation matters least. Logz.io is the better trade when open standards, security analytics, and avoiding seat fees outweigh the need for digital experience monitoring. New Relic is the stronger option when frontend coverage and platform depth justify the additional cost and lock-in. Test both against real ingest, retention, and user numbers before deciding.
Whether you choose New Relic's unified breadth or Logz.io's open-source-managed stack, you still need separate tools for on-call scheduling, phone and SMS escalation, and customer-facing status pages. Better Stack brings all of that together with logs, metrics, and traces in one platform, 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.
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Better Stack vs Logz.io compared across logs, metrics, tracing, pricing, incident management, AI, SIEM, and more. See which observability platform fits your team
Logz.io is an end-to-end cloud monitoring solution allowing you to unify logs, metrics, and trace analytics. It's based on ELK stack, what makes it quite expensive at scale, and is also often hard to start with.
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