Datadog is the market-leading observability platform with the broadest surface area — APM, infrastructure monitoring, logs, security, synthetics, and more — starting at $15/host/month for infrastructure. New Relic overhauled its pricing in 2021 to consumption-based: 100GB of data ingest free per month, then $0.30/GB, plus a free full-platform user. New Relic is dramatically cheaper for mid-size teams that don't need Datadog's breadth. Datadog wins on depth and ecosystem; New Relic wins on cost transparency and getting started fast.
Quick comparison
| Feature | Datadog | New Relic |
|---|---|---|
| Starting price | $15/mo | Free plan |
| Free plan | No | Yes |
| Open source | No | No |
| Self-hostable | No | No |
| G2 rating | Not listed | Not listed |
| Best for | engineering and ops teams running complex, multi-product observability stacks who need APM, logs, security, and synthetics in one place | mid-size engineering teams that want full-stack observability without per-host pricing and are willing to manage data ingest volume |
| Starting price | $15/host/month for infrastructure monitoring (APM adds more). | 100GB/month free, then $0.30/GB. One full-platform user always free. |
| Free plan | No | Yes |
| Open source | No | No |
| Self-hostable | No | No |
| Pricing model | Per-host, per-product pricing — costs add up fast across APM, logs, and security. | Consumption-based on data ingest GB — predictable until you scale ingestion. |
| Best for | Large teams needing deep, cross-product observability with rich alerting and integrations | Mid-size teams wanting cost-efficient full-stack monitoring with a generous free tier |
Feature breadth and depth
Datadog has no peer on raw surface area. It ships APM, distributed tracing, infrastructure monitoring, log management, synthetic monitoring, real user monitoring, cloud security posture management, and application security — all in a single product with shared context. You can correlate a spike in error rate, trace it to a specific service, pivot to that host's CPU metrics, and check the logs — all without leaving the platform. New Relic covers the same categories (APM, infrastructure, logs, browser monitoring, mobile, synthetics) and has improved significantly since its 2021 redesign, but Datadog's integrations tend to run deeper and its alerting system is more flexible. If you run Kubernetes at scale, AWS multi-account, or need network performance monitoring, Datadog's depth becomes a real operational advantage rather than a spec sheet win.
Ease of setup and onboarding
New Relic's consumption-based model removes the friction of figuring out which agent or add-on SKU you need before you start. Install the New Relic agent, ship data, and the free 100GB/month covers most small teams entirely — no credit card required for meaningful usage. Datadog's onboarding is more fragmented: infrastructure monitoring, APM, logs, and security are separate products with separate billing, and the per-host model means costs escalate as your fleet grows. Both products have strong documentation and quickstart guides, but New Relic's single-agent approach for all telemetry types (metrics, traces, logs, events) means less configuration decision-making at the start. For teams standing up observability from scratch, New Relic gets to first useful dashboard faster.
Integrations and ecosystem
Datadog lists over 700 integrations, covering virtually every cloud service, data store, messaging system, and third-party SaaS tool. Many of these integrations are deep — not just pulling a metric, but enriching it with metadata, providing out-of-the-box dashboards, and alerting on service-specific signals. The Datadog Marketplace also has third-party apps and monitors from partners. New Relic's integration library is substantial but smaller, and the depth varies more across integrations. For standard AWS, GCP, and Azure services, both products are well covered. The gap shows up in niche databases, specialized infrastructure, and APM instrumentation for less common languages. If your stack is unusual or you rely on deep integrations for alerting context, Datadog is the safer choice.
Pricing and total cost
This is the biggest decision driver for most teams evaluating the two. Datadog's per-host pricing for infrastructure starts at $15/host/month (billed annually), with APM adding $31/host/month on top, and logs charged separately at $0.10/GB ingested plus $1.70/million log events indexed. For a 50-host environment with APM and logs, costs can easily reach $4,000-6,000/month. New Relic's model is simpler: 100GB/month of data ingest free, then $0.30/GB, plus $99/month per full-platform user (basic users are free). A mid-size team monitoring the same 50 hosts could pay significantly less if they manage ingest volume. Datadog can win at enterprise scale when bundled contracts apply, but for teams under $5,000/month in observability spend, New Relic is almost always cheaper.
Alerting, dashboards, and workflows
Datadog's alerting system is one of its strongest features — composite monitors, anomaly detection, forecast alerts, and multi-condition triggers are all first-class. Dashboards are flexible, support template variables, and can aggregate data from across all Datadog products in a single view. The incident management workflow (Datadog Incident Management) ties alerts to services, runbooks, and Slack channels in a structured way. New Relic's alerting is capable and improved considerably with the New Relic One redesign, but complex multi-condition alerts and composite monitors require NRQL queries that can feel like working around the UI. For teams with sophisticated alerting requirements — where a single outage page requires correlating five different signals — Datadog's alerting flexibility is a real advantage in daily operations.
AI and automation features
Both products have invested in AI-driven features: anomaly detection, root cause suggestions, and predictive alerting. Datadog's Watchdog automatically detects anomalies and surfaces them without requiring alert configuration, which is genuinely useful for catching regressions you didn't think to monitor. New Relic has Applied Intelligence, which correlates alerts and suggests root causes, and has made AI-driven noise reduction a selling point. For most teams, the practical difference is small day-to-day, but Datadog's automatic anomaly detection is more polished and requires less configuration to be useful. At the high end, both offer LLM observability and AI monitoring features catered to teams building AI products.
Pricing deep-dive
Datadog
- Infrastructure Monitoring: $15/host/month (billed annually)
- APM: $31/host/month (billed annually), includes infrastructure
- Log Management: ~$0.10/GB ingested, $1.70/million log events indexed per month
- Synthetics, RUM, Security: separate add-on pricing per test/session/host
- Enterprise discounts available via contract negotiation at larger volumes
New Relic
- Free: 100GB data ingest/month, 1 full-platform user, unlimited basic users
- Data Plus: $0.50/GB above 100GB (extended retention and compliance features)
- Standard: $0.30/GB above 100GB ingest
- Full-platform users: $99/month each (billed annually); basic users always free
- Pro and Enterprise tiers available with volume discounts and SLA guarantees
Pricing verdict: Datadog's per-host model is intuitive to budget but expensive at scale — a 50-host environment with APM can easily run $2,000-3,000/month before logs. New Relic's consumption model is cheaper for most teams under 200 hosts, especially if you actively manage what telemetry you send. The switch in 2021 from per-host to ingest-based pricing made New Relic significantly more competitive for mid-market teams. For enterprises with large negotiated contracts, Datadog can still win on total cost when bundled discounts apply.
How to migrate from Datadog to New Relic
What real users say
Datadog: Datadog users consistently praise its depth of integrations, the quality of its dashboards, and how well the products work together. The most common complaints are cost at scale — bills can surprise teams as they add more products — and the complexity of navigating so many different pricing levers. Teams that have fully bought into the Datadog ecosystem rarely leave; teams that haven't fully committed often balk at the cost growth.
New Relic: New Relic users appreciate the generous free tier, the shift to consumption-based pricing, and the unified data model where all telemetry types (metrics, events, logs, traces) live in the same queryable store. Common frustrations include NRQL's learning curve for non-SQL users, the UI redesign that came with New Relic One (which divided long-time users), and the fact that some integrations feel shallower than Datadog's equivalents.
Sources: Synthesized from G2, Gartner Peer Insights, Reddit r/devops and r/sysadmin discussions, public pricing pages, and vendor documentation.
Final verdict
Choose Datadog if...
- Choose Datadog if you need APM, logs, security, and synthetics in a single deeply integrated platform where data from all products shares context and correlation.
- Choose Datadog if your team runs Kubernetes at scale, multi-cloud infrastructure, or complex alerting conditions that require composite monitors and anomaly detection without manual NRQL queries.
- Choose Datadog if you have an enterprise contract or are at the scale where Datadog's bundled pricing becomes competitive — typically 200+ hosts with negotiated rates.
Choose New Relic if...
- Choose New Relic if your team is under 200 hosts and wants full-stack observability (APM, infrastructure, logs, browser) without Datadog's per-product, per-host pricing adding up.
- Choose New Relic if you want to start with a real free tier — 100GB/month is enough for many small teams to run APM and infrastructure monitoring at no cost.
- Choose New Relic if your primary use case is APM and distributed tracing and you want a simpler pricing model where a single agent ships all telemetry types.
Consider neither if: Consider Grafana + Prometheus if you want open-source observability you can self-host with no per-host or per-GB fees. Consider Dynatrace if you want AI-driven full-stack monitoring with automatic baselining that requires minimal manual alert configuration.