New Relic vs Datadog: The Complete Comparison

TL;DR: New Relic vs Datadog at a Glance

Choosing between New Relic and Datadog? Here's what you need to know:

• New Relic excels with its all-in-one pricing model and user-friendly interface, making it ideal for teams wanting simplicity and predictable costs. Its NRQL query language strikes the perfect balance between power and accessibility.
• Datadog shines with its extensive integration ecosystem (500+ integrations) and advanced analytics capabilities, perfect for complex, multi-cloud environments. Its granular controls and customization options reward technical power users.
• Pricing Structure: New Relic offers consumption-based pricing (per user + per GB) with all features included. Datadog uses a modular pricing model where you pay for each component separately.
• Learning Curve: New Relic is generally easier to set up and learn initially, while Datadog offers more depth but requires more technical expertise to maximize.

Let's break it down.

Why This Comparison Matters

You're staring at your production dashboard, watching response times creep up. Your Slack channels light up with user complaints. Now what?

That's where observability platforms like New Relic and Datadog come in—they're your early warning system before things go sideways.

But choosing between them isn't straightforward. They both monitor your stack but with different approaches, pricing models, and strengths that might align better with your specific needs.

I've spent countless hours on both platforms—debugging production issues at 2 AM and setting up monitoring for everything from scrappy startups to enterprise systems. This comparison cuts through the marketing speak to help you decide which tool deserves your team's investment.

Core Features: What Both Platforms Offer

Both New Relic and Datadog cover the essential monitoring bases but with different implementation approaches:

But the similarities end when you look under the hood. Let's get into what sets them apart.

Interface & Usability

New Relic's Interface

New Relic's UI feels like your favorite code editor—clean, intuitive, and built for speed. Their "New Relic One" platform unifies all their products into a cohesive experience.

Key usability wins:

• Fast Setup: Faster time to first insight (typically 5-10 minutes after installation)
• Guided Workflows: Step-by-step processes for setting up key monitoring tasks
• Query Builder: Visual interface that doesn't require memorizing a new query language
• Entity Explorer: Automatically discovers and maps relationships between services
• Out-of-the-box Dashboards: Pre-configured visualizations that show what you care about
• Applied Intelligence: AI-assisted troubleshooting that correlates incidents
• Mobile App: Fully-featured iOS and Android apps for on-the-go monitoring

But it's not all smooth sailing. The sheer number of features can be overwhelming for first-time users, and some advanced customizations require digging through multiple menus.

Datadog's Interface

Datadog's interface is like a well-organized toolbox—everything has its place, but you need to know what you're looking for. Their UI leans technical but rewards power users.

Key usability wins:

• Highly Customizable Dashboards: Drag-and-drop widgets with pixel-perfect positioning
• Unified Search: Search across all telemetry data from a single search bar
• Live Containers View: Real-time visibility into container performance
• Network Map: Visual representation of service dependencies and traffic flow
• Notebooks: Collaborative troubleshooting with shareable, interactive documents
• Service Catalog: Centralized inventory of all monitored services with ownership information
• Clipboard History: Saves recent queries for quick reuse

The drawback? The learning curve is steeper, especially for teams without dedicated DevOps specialists. First-time users often need to consult documentation to complete basic tasks, and the wealth of configuration options can be overwhelming.

Pricing

New Relic's Pricing

New Relic switched to a consumption-based pricing model that's refreshingly straightforward:

Core Components:

• Full-Stack Observability: $99 per core user per month (minimum 3 users)
• Pay per data ingested: $0.30 per GB beyond free tier
• A free tier with 1the 00GB per month and 1 full user

What You Get:

• All platform capabilities (no separate feature charges)
• Unlimited basic users (dashboards and alerts only)
• 13 months of full data retention
• No per-host or per-container charges
• Built-in vulnerability management

The kicker is their "all-you-can-observe" approach—one price gives you access to the entire platform rather than paying for individual features.

Small teams can start for as little as $99/month for the standard tier, while a mid-sized team might spend $1,000-2,000/month for full capabilities.

Hidden Costs to Watch For:

• Data ingestion can grow quickly, especially with detailed logging
• Enterprise tier (with 99.99% SLA) comes at a premium
• Additional costs for managed services like synthetic monitoring beyond the base allocations

Datadog's Pricing

Datadog uses a modular pricing structure where each component is priced separately:

Core Infrastructure:

• Infrastructure Monitoring: $15 per host per month (annual plan)
• APM & Continuous Profiler: $31 per host per month
• Log Management: Starting at $0.10 per GB ingested with 15-day retention
• Real User Monitoring: $1.50 per 1,000 sessions
• Synthetic Monitoring: $5 per 1,000 API test runs, $12 per 1,000 browser tests

Additional Components:

• Network Performance Monitoring: $7 per host
• Security Monitoring: $0.20 per GB
• Database Monitoring: $7 per host
• Serverless Monitoring: $2 per million functions invoked
• CI Visibility: $12 per CI user

This à la carte approach means you only pay for what you use, but costs can add up quickly as you adopt more features. A typical small-to-mid-sized setup easily runs $500-1000/month, while larger implementations can scale to $10,000+/month.

Volume Discounts:

• Significant discounts available for annual commitments
• Custom enterprise pricing for large-scale deployments
• Committed-use discounts for predictable workloads

Integration Ecosystem

New Relic Integrations

New Relic offers 400+ integrations covering:

Cloud Platforms:

• AWS (40+ services including Lambda, ECS, EKS)
• Azure (VM, App Service, Functions, AKS)
• GCP (Compute Engine, GKE, Cloud Functions)
• Heroku, DigitalOcean, IBM Cloud

Databases:

• SQL: MySQL, PostgreSQL, SQL Server, Oracle
• NoSQL: MongoDB, Cassandra, Redis, Elasticsearch
• Time-series: InfluxDB, Prometheus

Containers & Orchestration:

• Kubernetes with cluster explorer
• Docker with containerized agent
• OpenShift integration
• Istio service mesh monitoring

Web Servers & Languages:

• Apache, Nginx, IIS
• Java, .NET, Node.js, Python, PHP, Ruby, Go
• Serverless & Lambda monitoring

Their OpenTelemetry support is solid, giving you flexibility if you ever want to switch platforms. The built-in Pixie integration for Kubernetes is a standout feature, providing code-level visibility without manual instrumentation.

Datadog Integrations

Datadog is the integration champion with a staggering 600+ out-of-the-box integrations including:

Cloud Providers:

• AWS (comprehensive coverage of 120+ services)
• Azure (50+ services with deep monitoring)
• GCP (60+ Google Cloud services)
• Oracle Cloud, Alibaba Cloud, IBM Cloud

Infrastructure:

• Physical and virtual servers
• Network devices (Cisco, Juniper, F5)
• Storage systems (NetApp, EMC)
• Load balancers and proxies

Containers & Orchestration:

• Advanced Kubernetes monitoring
• Docker with auto-discovery
• Mesos, Marathon, Nomad
• Service mesh integrations (Istio, Linkerd)

DevOps Tools:

• CI/CD: Jenkins, CircleCI, GitHub Actions, GitLab
• Configuration management: Ansible, Puppet, Chef
• Collaboration: Slack, PagerDuty, ServiceNow

Their agent architecture makes adding new integrations straightforward, often requiring just a configuration change. The auto-discovery feature for containerized environments is particularly powerful, automatically detecting and monitoring new services as they spin up.

Performance & Scalability

New Relic Performance

New Relic built their platform for scale from day one:

Data Ingestion & Storage:

• Handles billions of daily data points
• Telemetry Data Platform built on NRDB (New Relic Database)
• Sub-second query performance on trillion-row datasets
• Time-slice query optimization

Retention Policies:

• Standard: 13 months for metrics, 30 days for events
• Configurable retention for different data types
• Data summarization for long-term trending

High Availability:

• Multi-region deployment
• 99.9% SLA (99.99% for Enterprise)
• Regional data residency options

For most teams, New Relic will scale seamlessly with your application—whether you're handling thousands or millions of users. The platform has been battle-tested by companies processing billions of transactions daily.

Performance Optimizations:

• Query response caching
• Automatic data summarization
• Intelligent sampling options to reduce data volume
• Query language optimized for time-series analysis

Datadog Performance

Datadog's architecture separates collection from analysis, enabling:

Data Processing:

• Dedicated intake tier for high-throughput data collection
• Custom metrics at 10-second granularity
• Live query capability with minimal latency
• Tag-based metadata for high-cardinality data

Scaling Capabilities:

• Horizontal scaling across environments
• Custom retention periods based on data importance
• Fine-grained sampling controls for high-volume tracing
• Multi-pipeline log processing

Availability:

• Multi-cloud deployment architecture
• 99.9% uptime SLA
• Geographic data storage options for compliance

Enterprise Scalability:

• Role-based access control with fine-grained permissions
• Audit trails for configuration changes
• Usage monitoring and quota management
• Cross-organization visibility

Advanced Capabilities

New Relic Advanced Features

New Relic has evolved beyond monitoring into a full observability platform:

AIOps & Intelligence:

• Proactive anomaly detection
• Incident correlation across telemetry
• Applied Intelligence with contextualized alerts
• Automatic baseline creation and threshold adjustment

Developer Experience:

• CodeStream IDE integration
• Error tracking with source code context
• Deployment markers with Git integration
• Change tracking with service correlation

Business Intelligence:

• Custom dashboards with business metrics
• User journey and conversion tracking
• SLO/SLA monitoring with error budgets
• Real user session replay and analysis

Security & Governance:

• Vulnerability management built-in
• Compliance reporting and auditing
• RBAC with SSO integration
• API access with token management

Their NRQL query language strikes a balance between power and approachability, with SQL-like syntax that most developers can pick up quickly:

SELECT average(duration) FROM Transaction 
WHERE appName = 'Production' 
FACET name 
TIMESERIES 1 minute SINCE 3 hours ago

Custom Visualizations:

• New Relic One programmable visualizations
• Custom dashboards with React components
• Data export to business intelligence tools
• Webhook integrations for custom actions

Datadog Advanced Features

Datadog pushes the boundaries of what observability platforms can do:

ML & Analytics:

• Watchdog automatic anomaly detection
• ML-based forecasting and trend analysis
• Outlier detection across services
• Log pattern analysis with clustering

Networking & Security:

• Full-stack network performance monitoring
• DNS monitoring and analysis
• Cloud security posture management
• Container security and compliance

Development & Testing:

• Continuous profiler for code optimization
• Continuous testing integration
• Database query performance analysis
• CI visibility with test analytics

Collaboration:

• Incident management workflow
• War room for collaborative debugging
• Shared context in notifications
• Timeline for change correlation

Their notebook feature for collaborative troubleshooting is genuinely useful during incidents, allowing teams to share query results and findings:

# Example Datadog query language syntax
sum:system.cpu.user{host:production-*} by {host}.rollup(avg, 60) / 
sum:system.cpu.system{host:production-*} by {host}.rollup(avg, 60)

Advanced Analytics:

• Metric correlation suggestions
• Multi-variate alerting with complex conditions
• Custom agent checks and integrations
• Executive reporting and business dashboards

Deployment & Administration: Setup & Maintenance

New Relic Deployment

Getting New Relic up and running involves:

Agent Installation:

• Single unified agent for infrastructure and APM
• One-line installation for most environments
• Auto-instrumentation for common frameworks
• Serverless deployment via layers (AWS) or extensions (Azure)

Configuration Management:

• Infrastructure-as-code support via Terraform
• Configuration profiles for consistent deployment
• API-first approach for automation
• Centralized agent configuration

Administrative Tasks:

• User management with SAML/SSO options
• Team-based access controls
• Alert policy templates and inheritance
• Account structure with sub-accounts for large organizations

Data Management:

• Custom attributes and tagging
• Data dropping and filtering options
• NRQL data manipulation (similar to a data warehouse)
• Drop rules for sensitive information

Datadog Deployment

Datadog's deployment approach offers more granular control:

Agent Architecture:

• Separate agents for different telemetry types
• Extensive configuration options via YAML
• Auto-discovery for dynamic environments
• Helm charts for Kubernetes deployment

Configuration Options:

• Environment variables and configuration files
• Centralized agent configuration
• Terraform provider with comprehensive coverage
• API-driven setup and configuration

Administration:

• Advanced RBAC with custom roles
• Audit trail for all configuration changes
• Organization-level settings and policies
• Private locations for synthetic tests

Data Controls:

• Sensitive data scrubbing
• Custom pipelines for log processing
• Retention filters and exclusion filters
• Metric transformation and aggregation

Community & Support

New Relic Community

New Relic's community resources include:

Support Options:

• Standard: 12-hour response time, business hours
• Pro: 2-hour response time, 24/7 for P1 issues
• Enterprise: 1-hour response, dedicated technical account manager

Educational Resources:

• New Relic University with certification tracks
• Extensive documentation with practical examples
• Regular webinars and virtual events
• Full Observability Developer platform with courses

Community Engagement:

• Explorer's Hub for user-to-user assistance
• Active forum with quick response times
• Nerdlog product updates and webinars
• GitHub repositories for open-source components

Professional Services:

• Implementation assistance
• Custom dashboard development
• Migration services
• Expert services hours included with enterprise plans

Their support team is responsive, typically resolving technical issues within hours. The knowledge base contains detailed troubleshooting guides with specific error codes and solutions.

Datadog Community

Datadog has built a strong community ecosystem:

Support Tiers:

• Standard: Email support during business hours
• Premium: 24/7 phone and email with 1-hour response for critical issues
• Enterprise: Technical account manager and custom SLAs

Learning Resources:

• Datadog Learning Center with hands-on labs
• Extensive documentation and knowledge base
• Regular virtual workshops and training sessions
• Annual Dash conference with technical deep-dives

Community Platforms:

• Active Slack community
• User groups in major tech hubs
• Dedicated forums for specialized topics
• GitHub repositories with examples and integrations

Professional Services:

• Implementation planning and architecture
• Migration assistance from other platforms
• Custom integrations development
• Training and enablement programs

Their premium support tiers offer impressively fast response times, though their basic support can be slower during peak times. The annual Dash conference has become a significant event for sharing observability best practices.

Use Cases

New Relic Ideal Use Cases

New Relic tends to shine for:

Team Profiles:

• Full-stack teams needing an all-in-one solution
• Organizations wanting predictable costs
• Teams with mixed technical expertise
• Companies with monolithic or early-stage microservice architectures

Industry Fits:

• E-commerce platforms monitoring user experience and transactions
• SaaS companies tracking application performance
• Media companies monitoring content delivery
• Financial services requiring compliance reporting

Technical Environments:

• Traditional server environments transitioning to cloud
• Java, .NET, and Ruby heavy stacks
• Teams standardized on a single cloud provider
• Applications where user experience is critical

Notable Users:

• American Express: Uses New Relic for transaction monitoring
• Gannett: Monitors digital media properties
• Zendesk: Tracks service platform performance
• Morningstar: Financial data delivery monitoring

Datadog Ideal Use Cases

Datadog is typically the go-to for:

Team Profiles:

• DevOps-mature organizations
• Multi-cloud and hybrid environments
• Teams with advanced monitoring needs
• Organizations willing to invest in observability as a core capability

Industry Fits:

• Gaming companies with real-time monitoring needs
• Enterprise IT with complex infrastructure
• High-frequency trading platforms
• Tech companies with microservice architectures

Technical Environments:

• Kubernetes-heavy deployments
• Multi-cloud infrastructure
• High-scale systems with billions of events
• Networks with complex topology

Notable Users:

• Samsung: Monitoring consumer electronics services
• Whole Foods: Retail systems monitoring
• The Washington Post: Digital content delivery
• Peloton: IoT and service platform monitoring

Migration Considerations

If you're considering switching from one platform to another, keep these factors in mind:

Moving from New Relic to Datadog

Challenges:

• Recreating dashboards and alerts (different query languages)
• Setting up multiple agents instead of one unified agent
• Adapting to a different data model and tagging structure
• Higher potential costs if using multiple Datadog products

Benefits:

• More granular control over monitoring configuration
• Potentially better multi-cloud visibility
• Advanced network monitoring capabilities
• More extensive integration options

Migration Path:

1. Run both platforms in parallel during the transition
2. Use OpenTelemetry as a bridge between systems
3. Prioritize core infrastructure monitoring first
4. Gradually rebuild alerts and dashboards
5. Train team on the new query language and interface

Moving from Datadog to New Relic

Challenges:

• Adapting to a different querying approach (NRQL vs. Datadog Query Language)
• Potentially less granular control over some configurations
• Rebuilding custom integrations and dashboards
• Adjusting to a unified data model

Benefits:

• Simplified agent deployment and management
• Potentially lower costs with the unified pricing model
• Easier onboarding for new team members
• All capabilities included without add-on purchases

Migration Path:

1. Start with APM and infrastructure monitoring
2. Implement the unified New Relic agent
3. Set up core alerting policies
4. Rebuild critical dashboards
5. Transition historical data analysis needs

How to Choose between New Relic and Datadog

Ask yourself these questions to determine which platform is right for you:

1. Budget alignment: Is predictable pricing (New Relic) or pay-for-what-you-use (Datadog) better for your financial structure?
2. Team expertise: Does your team have dedicated DevOps specialists who can maximize Datadog's capabilities, or would you benefit from New Relic's approachability?
3. Architecture complexity: Are you running a complex multi-cloud or hybrid environment (Datadog strength) or a more standardized stack (either works well)?
4. Growth trajectory: Where is your infrastructure headed in the next 2-3 years, and which platform better aligns with that vision?
5. Integration requirements: Which platform better supports your existing and planned technology stack?
6. Specific feature needs: Do you require advanced network monitoring (Datadog) or prefer a unified APM approach (New Relic)?
7. Data volume considerations: How much telemetry data do you generate, and how does that impact costs on each platform?
8. Compliance requirements: Do you have specific data retention or sovereignty needs that one platform handles better?
9. Alert management workflow: How do your teams handle incidents, and which platform's alerting philosophy aligns better?
10. Adoption strategy: Do you prefer an all-at-once approach (New Relic) or gradual component adoption (Datadog)?

Performance Benchmark on Both Platforms

To give you a concrete comparison, we ran benchmark tests on both platforms monitoring identical workloads:

Note that performance can vary significantly based on configuration, data volume, and query complexity.

The Final Verdict

There’s no clear-cut winner between New Relic and Datadog—both are solid monitoring platforms with their own strengths. The right choice depends on your needs, team structure, and how you like to work.

That said, if you’re looking for a managed observability solution that keeps costs in check without sacrificing performance, Last9 is worth considering. Industry leaders like Disney+ Hotstar, CleverTap, and Replit rely on Last9 for high-cardinality observability at scale.

As a telemetry data platform, we’ve monitored 11 of the 20 largest live-streaming events in history. With OpenTelemetry and Prometheus support, Last9 unifies metrics, logs, and traces—helping you optimize performance, reduce costs, and get real-time, correlated insights for better monitoring and alerting.

Talk to us today or start your free trial!