Datadog vs. New Relic: a side-by-side comparison for 2026

Both Datadog and New Relic are leading observability platforms, and over the past few years both have invested heavily in AI. At first glance, they offer many of the same capabilities, including APM, infrastructure monitoring, log management, digital experience monitoring, security, and AI-powered incident investigation. Once you start using them, though, their differences become much more apparent.

Datadog's biggest strength is the breadth of its platform. It started as a cloud infrastructure monitoring tool and has steadily expanded into almost every part of the observability and security stack. Everything runs through a single proprietary agent and backend, making it easy to adopt additional products as your needs grow. The tradeoff is pricing, since costs increase as you enable more features. On the AI side, Bits AI SRE became generally available in December 2025 and can automatically investigate alerts as they fire. Datadog also offers an MCP server, currently in Preview, along with mature products for Cloud SIEM, workload protection, and code security.

New Relic approaches the problem differently. While it has been a full-stack APM platform since 2008, its biggest recent changes have focused on pricing, storage, and AI. Instead of charging per host and per product, New Relic primarily charges based on data ingest and user type, which can be a better fit for organizations managing large fleets of infrastructure. Its SRE Agent, introduced in Preview in February 2026, is part of a broader Agentic Platform that includes a no-code AI agent builder, MCP support, and Intelligent Root Cause Analysis. At New Relic NOW in June 2026, the company also introduced AI Observability for monitoring production AI applications, collaborative Notebooks for SRE teams, and plans to expand its FedRAMP authorization from Moderate to High.

Neither platform is the better choice for everyone. The right one depends on what you're monitoring, how your team works, and how you expect costs to scale over time. In this comparison, we'll look at how they stack up across architecture, APM, infrastructure monitoring, log management, AI capabilities, security, pricing, and enterprise features.

Quick comparison at a glance

Platform architecture and philosophy

The architectural difference between Datadog and New Relic comes down to how each platform thinks about billing and data storage. Both are multi-product SaaS platforms, both have unified backends, and both have invested heavily in AI. The divergence is in the billing model and what that means for team access.

Datadog: proprietary SaaS with per-host, per-feature stacking

You install the Datadog Agent on every host, everything flows into Datadog's hosted infrastructure, and the investigation workflow is designed to be seamless across products. When an alert fires you can click from the alert to the trace to the surrounding logs to the infrastructure metrics without switching interfaces, because everything shares the same proprietary backend.

The cost stacks. Infrastructure at $15 to $23 per host per month is the foundation, APM runs $31 to $40 per host per month on top, logs charge $0.10/GB for ingestion plus $1.70 per million events to index, and every product you add creates another billing dimension. OpenTelemetry metrics are treated as custom metrics with surcharges. The high-water mark billing model means a traffic spike sets your billing rate for the whole month. Every engineer has unlimited read access, but each product you enable adds to the bill regardless of headcount.

New Relic: unified NRDB backend

New Relic rebuilt its backend on NRDB (New Relic Database), a unified telemetry store that holds logs, metrics, traces, and events in one place queryable via NRQL. Every product reads from the same database. The pricing model charges on two dimensions: how much data you ingest and what type of user needs access. Basic users are free and can view dashboards, run queries, and configure alerts. Core users at $49/month get logs in their IDE and error tracking. Full platform users at $349/month on the Pro tier (annual) get everything including APM, infrastructure monitoring, distributed tracing, and digital experience.

The result is a different tension than Datadog's. Datadog's costs compound with every feature you add. New Relic's costs compound with every engineer who needs full platform access. A team of five engineers where everyone needs to investigate incidents needs five full platform seats at $349 each, which is $1,745 per month before a single byte of data is ingested. New Relic has added a Compute pricing model as an alternative that removes per-user fees in exchange for Compute Capacity Unit charges, which changes the economics for larger teams with high user counts relative to their data volume.

The generous free tier is a real differentiator: 100 GB/month of data ingest, one full platform user, and unlimited basic users forever, with no credit card required. For small teams or teams evaluating the platform, this is the most useful free tier in the category.

APM and distributed tracing

Both platforms are serious APM products with strong trace visualization, service maps, and distributed tracing. The differences are in instrumentation philosophy, query language lock-in, and the depth of profiling available.

Datadog: agent-based APM with the deepest profiling in the category

Datadog APM is one of the most feature-complete distributed tracing products available. Service maps visualize live dependencies. The Continuous Profiler captures code-level CPU and memory attribution per function. Dynamic Instrumentation lets you add log lines and custom metrics to a running production service without redeploying. Watchdog surfaces anomalies automatically. The frontend-to-backend correlation is seamless because RUM and APM share the same backend.

APM costs $31 to $40 per host per month on top of the base infrastructure fee. High-throughput microservices regularly exceed the 150 GB and 1 million span per-host limits. OTel metrics create custom metric surcharges.

New Relic: dual-agent APM with thread profiling and NRQL-powered analysis

New Relic APM offers two instrumentation paths. Traditional APM agents are language-specific libraries installed per service, providing method-level traces, thread profiling, and detailed transaction data. The eBPF agent (eAPM) provides zero-code instrumentation at the kernel level for Kubernetes clusters, discovering services automatically across any language. New Relic recommends running both for maximum depth, which means two systems to maintain in parallel.

Thread-level CPU profiling via the traditional APM agents is a genuine differentiator: you can see exactly which functions consume CPU in production at the method level without performance overhead. Infinite Tracing accepts 100% of trace data and retains the most significant traces for high-volume environments, which is useful for teams that cannot afford sampling blind spots.

The NRQL dependency is worth thinking through. Every alert, dashboard, and runbook your team builds around New Relic's APM data is written in NRQL. That is a real long-term switching cost that does not show up on the invoice but accumulates over time. OTel native support with no surcharge is a meaningful advantage over Datadog for teams standardizing on open instrumentation.

Log management

Both platforms make all ingested logs searchable. The billing structure and cost at scale are meaningfully different.

Datadog: two-tier billing where indexing is where the real cost hides

Datadog logs use two billing layers. You pay $0.10/GB for ingestion regardless of whether you ever query those logs. Then you pay $1.70 per million events to index them, making them searchable. Most teams ingest everything and index selectively, which means a portion of your logs are always in archive and invisible unless you pay to rehydrate them. At 100 GB/day of log volume, the Datadog log bill alone approaches $107,000 per year before APM, RUM, or infrastructure.

The query experience is genuinely strong: faceted search, Log Patterns that cluster similar lines, Sensitive Data Scanner, and seamless trace correlation because everything shares the same backend.

New Relic: all logs searchable, flat ingest pricing with a 4x premium

New Relic makes 100% of ingested logs searchable via NRQL with no separate indexing fee. Pattern detection groups similar log messages automatically. AI log alert summarization generates a hypothesis when an alert fires. Long-term archival is available up to seven years without rehydration. The query language is NRQL rather than SQL, which is capable but requires learning a proprietary syntax.

The cost is $0.40/GB for data ingest beyond the free 100GB/month tier, which is four times Datadog's ingestion-only rate of $0.10/GB. Datadog's model gets more expensive as you add indexing, but at low index ratios New Relic can cost more per queryable log. At high volume where most logs need to be searchable, New Relic's model becomes more predictable because there is no separate indexing decision to make.

Infrastructure monitoring and cloud metrics

Both platforms monitor infrastructure comprehensively with strong Kubernetes support and cloud integrations. The cost models diverge again: Datadog stacks per-host fees, New Relic charges on data ingest but requires full platform user seats for engineers to actually view and act on infrastructure data.

Datadog: comprehensive fleet visibility on a compounding per-host model

Datadog infrastructure monitoring starts at $15/host/month and that is the foundation on which APM, database monitoring, and network monitoring all stack. Kubernetes monitoring is deep. Network Performance Monitoring tracks service-to-service traffic flows. Cloud Cost Management ties spending to infrastructure metrics. The high-water mark billing model means a five-day traffic spike sets your billing rate for the whole month. OTel metrics are charged as custom metrics.

New Relic: dimensional metrics with native cloud integrations

New Relic's infrastructure agent collects CPU, memory, disk, network, and process metrics across Linux, Windows, and macOS. Cloud integrations for AWS, Azure, and GCP require no agent. On-host integrations cover NGINX, MySQL, Redis, Apache, and RabbitMQ. Raw metric retention is 30 days with aggregated rollup data retained for 13 months, which is useful for trend analysis.

Cardinality does not create penalty charges in New Relic the way it does in Datadog, since New Relic bills on data ingest volume rather than unique metric combinations. The access restriction matters though: infrastructure monitoring is a full platform feature, meaning every engineer who needs to view host metrics during an incident requires a full platform user seat at $349/month on Pro.

Digital experience monitoring

Both platforms have mature digital experience suites. New Relic has two consecutive years of Gartner Magic Quadrant recognition in this category. Datadog's session replay and product analytics are more fully featured. Both require engineers to have full platform access to work with the data.

Datadog: full digital experience suite with seamless backend correlation

Browser RUM, Mobile RUM across iOS, Android, React Native, and Flutter, Session Replay, Synthetic Monitoring, Product Analytics, and Experiments. The frontend-to-backend correlation is seamless because RUM and APM share the same backend. Each component is a separate line item on the invoice.

New Relic: mature DEM suite with Gartner recognition and mobile depth

New Relic Browser Monitoring captures Core Web Vitals, session replay, rage and dead click detection, and user journey analytics. Mobile monitoring covers iOS, Android, React Native, and Flutter natively. APM 360 connects frontend sessions to backend traces. Synthetic monitoring runs from global probe locations.

New Relic was named a Leader in the 2025 Gartner Magic Quadrant for Digital Experience Monitoring for the second consecutive year, which reflects a level of recognized maturity in this specific category. The access model applies here too: viewing and acting on digital experience data requires a full platform user seat.

AI capabilities

Both platforms have made major AI investments in 2025 and 2026. Datadog's AI went GA first; New Relic's is more recent but architecturally more ambitious in scope.

Datadog Bits AI: autonomous investigation that fires at alert time

Datadog's Bits AI SRE went GA in December 2025. When an alert fires, it starts investigating without anyone prompting it: querying traces, reviewing logs, checking recent deployments, producing a root-cause hypothesis. By the time you open your laptop, the investigation is already in progress. Beyond Bits AI SRE, there is Bits Chat for conversational queries, Bits Code for in-editor help, Bits Security Analyst for SIEM triage, and an MCP Server in Preview for Claude and Cursor integration.

The GA status is meaningful: Bits AI SRE is production-ready and available to all Datadog customers now, not a preview feature.

New Relic SRE Agent and Agentic Platform: broader architecture, still in Preview

New Relic launched the SRE Agent at New Relic Advance on February 24, 2026. It is an always-on AI teammate designed to perform deep full-stack diagnostics during incidents, combining reasoning capabilities with deterministic tools including Intelligent Root Cause Analysis (iRCA), which searches the entity topology graph and applies probabilistic causal models to narrow down the problem space.

The Agentic Platform is New Relic's broader investment. It provides a no-code drag-and-drop builder for custom AI agents, a central orchestration layer for managing agents at scale, a dynamic runtime for multi-step logic, built-in RBAC and governance, and MCP support. The evaluation engine tests agent performance on an ongoing basis. Engineers, SREs, and technical product managers can build custom agents without writing code, which is a capability Datadog does not currently match.

At New Relic NOW in June 2026, New Relic added several more AI capabilities: AI Observability for monitoring LLM agents and AI applications in production (cost management, reasoning visibility, quality guardrails), Preflight for monitoring AI-generated code in the IDE, ChatGPT App Monitoring, Notebooks for collaborative SRE runbooks using variables to turn one-off investigations into repeatable runbooks, and New Relic Knowledge for fusing real-time telemetry with historical incident data and system change context.

The honest caveat: the SRE Agent and most Agentic Platform features are still in Preview as of June 2026. Datadog's equivalent went GA six months earlier. New Relic's 2026 AI Impact Report found that users of AI features resolved incidents 25% faster than non-AI users on the platform, but that covers applied intelligence and anomaly detection as well as the newer agentic features.

Query language and user experience

Both platforms use proprietary query languages, and both are building AI overlays to reduce how often you have to write queries directly. The underlying syntax still matters for alerts, dashboards, and runbooks.

Datadog: multiple query languages by product

Datadog uses different query syntaxes depending on what you are querying. Infrastructure metrics use PromQL or Datadog's metric query language. Logs use Datadog's log search syntax with terms and operators. APM uses trace search. The click-based query builders abstract a lot of this, but when writing custom alerts or debugging edge cases, you are working in product-specific syntax that does not transfer elsewhere. The 15-minute cap on live data queries is a practical limitation for certain investigation workflows.

New Relic: NRQL everywhere, powerful but proprietary

New Relic uses NRQL across all data types: logs, metrics, traces, and events. NRQL looks like SQL but is proprietary. Teams familiar with SQL can leverage that foundation, but NRQL has its own syntax, functions, and idioms. Once your team has invested months in NRQL-based alerts, dashboards, and runbooks, you accumulate switching costs you did not explicitly sign up for. PromQL queries are supported but interpreted into NRQL, which means behavior can diverge from native PromQL at the edges.

New Relic's interface is generally regarded as more intuitive than Datadog's in user reviews. The investigation flow involves navigating between APM, Infrastructure, and Logs sections, each of which uses the same NRQL syntax. The Entity Explorer ties entities together across products. Lookout surfaces anomalies across entities without requiring explicit monitors.

Incident management and alerting

Datadog: seat-based incident management with On-Call

Datadog's incident management is a seat-based SKU covering incident declaration, responder assignment, timeline management, and Slack/Teams integration. On-call scheduling through Datadog On-Call (launched late 2024) or PagerDuty and OpsGenie integrations. ML-based anomaly detection (Watchdog) fires alerts without manual threshold tuning. SLO tracking monitors error budgets.

New Relic: alerting with Applied Intelligence, external tools for on-call

New Relic's alerting covers threshold, anomaly, and NRQL-based conditions across all signal types. Applied Intelligence groups related alerts, reduces noise through correlation, and generates incident summaries. AI log alert summarization provides a hypothesis before you start investigating. SLO tracking defines error budgets and alerts when you are burning down budget faster than expected.

New Relic does not include on-call scheduling or phone/SMS delivery as native features. Teams integrate PagerDuty, OpsGenie, or similar tools for that layer. For five on-call engineers on PagerDuty Professional, that adds $245 to $415 per month on top of the New Relic license. Datadog On-Call eliminates that dependency for Datadog customers, though most teams still use external tools for complex on-call workflows.

Team management and user access

User access is where the pricing models diverge most concretely, and where the day-to-day experience for engineering teams differs most visibly.

Datadog: unlimited users with feature-gated billing

Datadog gives you unlimited users at no per-seat charge. Three role tiers: Admin (full access), Standard (key workflow features, can invite new users), and Read-only (view-only dashboards). The cost pressure in Datadog is not user count but feature and data volume. Every engineer can read dashboards and investigate incidents regardless of their role, because the billing is on the product side rather than the user side.

The incident management feature is billed per contributing user per month, which is a separate dimension from the role-based access model.

New Relic: three user types with direct pricing impact

New Relic has three user types with meaningfully different access: Basic users are free and can run queries, configure alerts, and view dashboards. Core users at $49/month get logs in their IDE via CodeStream and error tracking. Full platform users at $349/month (Pro annual) get APM, infrastructure monitoring, distributed tracing, digital experience monitoring, and everything else.

The practical consequence: in a large engineering organization, not everyone gets a full platform seat. Engineers without a full platform seat cannot dig into traces, investigate infrastructure metrics, or work with digital experience data during incidents. This creates a visibility asymmetry that does not exist in Datadog's model, where feature access is determined by which products are enabled rather than by which users have seats.

The Compute pricing model is an alternative that eliminates per-user fees entirely in exchange for CCU-based billing. For teams with large user counts relative to data volume, this can be more economical. It requires working through New Relic's sales process to evaluate.

Pricing comparison

Both platforms have complex pricing models that require understanding your specific workload before a real comparison is possible.

Datadog: per-host plus per-feature stacking

Infrastructure at $15 to $23 per host per month, APM at $31 to $40 per host per month on top, log ingestion at $0.10/GB, log indexing at $1.70 per million events, custom metrics at $1 per 100 beyond the per-host allotment, and the high-water mark billing model that sets your monthly rate at the month's peak host count.

A 100-host deployment with APM, logs, and RUM commonly runs $20,000 to $30,000 per month.

New Relic: ingest-plus-user with a meaningful free tier

New Relic free tier: 100GB/month, one full platform user, unlimited basic users. Standard: $10 for the first full platform user, $99/month per additional user up to five. Pro: $349/user/month annual ($418.80 monthly). Enterprise: custom. Data ingest beyond the free 100GB: $0.40/GB Standard, $0.60/GB Data Plus.

For a team of eight engineers where everyone needs full platform access on the Pro plan, the user license alone is $2,792/month before accounting for a single byte of data ingest. For a team with many basic users who only occasionally need APM access, the model is much more economical.

Rough cost comparison: 100 hosts, 8 full platform users, 5TB/month telemetry

The New Relic number is lower at this configuration but the gap narrows or reverses depending on your specific mix. Teams with many engineers needing full access will feel New Relic's user costs. Teams with high data volume and few engineers will find Datadog's per-feature stacking more painful. Neither model is universally cheaper.

Security capabilities

Datadog: Cloud SIEM and a full security platform woven into observability

Datadog has Cloud SIEM for threat detection across logs and cloud audit trails, Workload Protection for runtime kernel-level threat detection, App and API Protection against injection attacks, Code Security covering SAST/IAST/SCA and secret scanning, Cloud Security Posture Management, and Vulnerability Management. The integration between security signals and observability data is Datadog's key differentiator: a security alert and the APM trace that triggered it live in the same system.

New Relic: Security RX in preview, compliance-focused current offering

New Relic's current security posture centers on compliance certifications: SOC 2, HIPAA (Data Plus), and FedRAMP Moderate. Security RX was announced in preview in 2026 as a vulnerability correlation feature that connects security findings to engineering context. At New Relic NOW in June 2026, the company committed to expanding FedRAMP authorization from Moderate to High and achieving DoD Impact Level 4, targeting regulated enterprise and government workloads.

New Relic does not have a Cloud SIEM product matching Datadog's depth. If active threat detection, workload protection, and security analytics are primary requirements alongside observability, Datadog is the more complete platform today.

What each platform genuinely lacks

Datadog gaps worth knowing:

• No free tier; evaluation requires a paid trial
• No self-hosted option
• High-water mark billing can move your invoice unexpectedly from traffic spikes
• OpenTelemetry metrics charged as custom metrics with surcharges
• No status pages included in base offering (separate SKU)
• On-call scheduling requires Datadog On-Call add-on or external tools
• No no-code AI agent builder

New Relic gaps worth knowing:

• Full platform user costs at $349/month create access restrictions at scale
• Engineers without a full platform seat cannot investigate APM or infrastructure during incidents
• NRQL is proprietary and creates long-term switching costs
• No status pages
• No built-in on-call scheduling or phone/SMS delivery
• SRE Agent and Agentic Platform are still in Preview as of June 2026
• No Cloud SIEM matching Datadog's depth
• Ingest pricing at $0.40/GB is 4x Datadog's ingestion-only rate

Final thoughts

As you've seen throughout this comparison, Datadog and New Relic are both excellent observability platforms, but they solve different problems particularly well.

If your priority is having a single platform that combines observability, security, and AI-powered operations, Datadog is the stronger choice. Its portfolio extends beyond monitoring to include Cloud SIEM, workload protection, code security, and continuous profiling, while Bits AI SRE can investigate incidents automatically as they occur. The tradeoff is cost. Datadog's modular pricing makes it easy to adopt new capabilities, but your bill can grow quickly as you enable more products.

On the other hand, if your focus is making observability accessible across a large engineering organization while keeping pricing more predictable, New Relic has a compelling advantage. Its pricing model is based on data ingest and user type instead of hosts, and it offers one of the most generous free tiers on the market. At the same time, the company is investing heavily in AI with its Agentic Platform, AI Observability, and collaborative workflows, making it an attractive option for teams building and operating AI-powered applications.

Ultimately, there isn't a universal winner. The better platform depends on your infrastructure, your team's size, the amount of telemetry you generate, and the capabilities you expect to use over the next few years. Before committing to either platform, compare your expected host count, data volume, users, and feature requirements. That exercise will usually tell you far more than any feature checklist can.

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