New Relic AI vs Azure Machine Learning

Detailed side-by-side comparison to help you choose the right tool

New Relic AI

🟢No Code

App Deployment

AI-powered observability platform that provides intelligent monitoring, anomaly detection, and automated root cause analysis for applications and infrastructure

Was this helpful?

Starting Price

$0/month (Free tier with 100 GB data ingest); paid plans usage-based, per-GB rates vary by data type and tier

Azure Machine Learning

App Deployment

Microsoft's cloud-based machine learning platform that provides ML as a service for building, training, and deploying machine learning models at scale.

Was this helpful?

Starting Price

Custom

Feature Comparison

Scroll horizontally to compare details.

FeatureNew Relic AIAzure Machine Learning
CategoryApp DeploymentApp Deployment
Pricing Plans8 tiers8 tiers
Starting Price$0/month (Free tier with 100 GB data ingest); paid plans usage-based, per-GB rates vary by data type and tier
Key Features
  • AI-powered anomaly detection and root cause analysis
  • Natural language querying via New Relic AI assistant
  • Full-stack observability across APM, infrastructure, logs, and browser
  • Automated machine learning (AutoML)
  • Drag-and-drop designer interface
  • Managed compute clusters with GPU support

New Relic AI - Pros & Cons

Pros

  • Generous free tier includes 100 GB ingest per month and full access to all platform capabilities, including the AI assistant, with no feature gating
  • Single unified platform consolidates APM, infrastructure, logs, traces, Kubernetes, browser, mobile, and synthetics — reducing the need to stitch together multiple vendors
  • New Relic AI assistant lets engineers query telemetry in natural language and auto-generates NRQL, lowering the learning curve for new team members
  • Strong Kubernetes and OpenTelemetry support with auto-instrumentation across major languages (Java, .NET, Node.js, Python, Go, Ruby, PHP)
  • Applied Intelligence correlates anomalies, deployments, and incidents to surface probable root cause and reduce alert noise during on-call rotations
  • Over 750 quickstart integrations and pre-built dashboards make initial setup faster than building dashboards from scratch in alternatives

Cons

  • Data ingest costs can escalate quickly past the 100 GB free tier, especially for log-heavy workloads, leading to surprise bills if retention and sampling aren't tuned
  • User-based pricing distinguishes Core, Full Platform, and Full Stack Observability users, which can become expensive for large engineering organizations
  • NRQL has a learning curve compared to PromQL or SQL, and although the AI assistant helps, complex queries still benefit from documentation deep-dives
  • UI can feel dense and overwhelming on first use, with many overlapping entity views, dashboards, and explorers that take time to navigate efficiently
  • Some advanced features like long-term data retention, HIPAA compliance, and FedRAMP require higher-tier paid plans rather than being included by default

Azure Machine Learning - Pros & Cons

Pros

  • Deep integration with the broader Microsoft ecosystem including Azure AD, Microsoft Fabric, Azure Databricks, and GitHub Copilot
  • Enterprise-grade security and compliance with certifications such as HIPAA, SOC 2, ISO 27001, and FedRAMP, suitable for regulated industries
  • Built-in responsible AI tooling for fairness, interpretability, and error analysis directly within the workspace
  • Support for hybrid and multicloud ML workloads through Azure Arc, allowing models to be trained and deployed on-premises or in other clouds
  • Scalable managed compute with on-demand GPU clusters (including NVIDIA A100 and H100 SKUs) and automatic scale-down to zero to control costs
  • Unified path from classical ML to generative AI through tight links with Microsoft Foundry and Azure OpenAI

Cons

  • Steep learning curve for teams new to Azure — workspace, resource group, and compute concepts add overhead before the first model trains
  • Pricing can be unpredictable since costs combine compute, storage, networking, and endpoint hours, making budgeting harder than flat-rate competitors
  • User interface is less polished and slower than competitors like Vertex AI or Databricks, with frequent UI redesigns between SDK v1 and v2
  • Limited value for teams not already on Azure — egress costs and identity setup make it impractical as a standalone ML platform
  • Some advanced features such as Foundry integrations and newer endpoint types lag behind AWS SageMaker in regional availability

Not sure which to pick?

🎯 Take our quiz →

🔒 Security & Compliance Comparison

Scroll horizontally to compare details.

Security FeatureNew Relic AIAzure Machine Learning
SOC2
GDPR
HIPAA
SSO✅ Yes
Self-Hosted
On-Prem
RBAC✅ Yes
Audit Log
Open Source
API Key Auth
Encryption at Rest
Encryption in Transit
Data ResidencyUS, EU
Data Retention
🦞

New to AI tools?

Read practical guides for choosing and using AI tools

🔔

Price Drop Alerts

Get notified when AI tools lower their prices

Tracking 2 tools

We only email when prices actually change. No spam, ever.

Get weekly AI agent tool insights

Comparisons, new tool launches, and expert recommendations delivered to your inbox.

No spam. Unsubscribe anytime.

Ready to Choose?

Read the full reviews to make an informed decision