7 Best Alternatives to New Relic in 2026

Why look beyond New Relic

New Relic provides a comprehensive observability platform that includes application performance monitoring (APM), infrastructure monitoring, log management, and synthetic monitoring. Its strength lies in offering a unified view across various telemetry data sources, supporting a wide range of programming languages and frameworks through its SDKs and agents. The platform is designed for full-stack visibility, helping teams identify and resolve issues from the application layer down to the infrastructure.

Despite its capabilities, organizations may seek alternatives due to several factors. Cost can be a consideration, as New Relic's usage-based pricing model, particularly for data ingest and advanced user types, may escalate with growing data volumes. Teams might also look for platforms with deeper specialization in specific areas, such as advanced security analytics, or more integrated AI-driven anomaly detection and root cause analysis features. Additionally, some users may prefer solutions offering a different user interface or a more tailored experience for specific cloud environments or operational workflows. The learning curve for New Relic's extensive feature set can also lead some organizations to explore platforms with a more streamlined initial setup or a different approach to data visualization and alerting.

Top alternatives ranked

1. 1. Datadog — Cloud-native monitoring and security platform

   Datadog offers a unified platform for monitoring, security, and analytics across applications, infrastructure, and logs. It provides extensive integrations with cloud providers, databases, and services, making it a strong choice for cloud-native and hybrid environments. Datadog's APM provides detailed trace analysis, while its infrastructure monitoring covers hosts, containers, and serverless functions. Log management and security monitoring are also integrated, offering a holistic view of system health and potential threats. The platform emphasizes real-time data collection and visualization through customizable dashboards. Datadog's Machine Learning capabilities assist in anomaly detection and forecasting, aiming to proactively identify issues. Its monitoring-as-code approach supports automation and integration into CI/CD pipelines.

   • Best for: Cloud-native observability, extensive third-party integrations, real-time analytics, security monitoring, serverless monitoring.
   • Datadog product profile
   • Datadog official site

2. 2. Dynatrace — AI-powered full-stack observability with intelligent automation

   Dynatrace provides an AI-powered software intelligence platform designed for comprehensive observability across multi-cloud and hybrid environments. Its core strength lies in its OneAgent technology, which automatically discovers and maps all dependencies in a stack, providing deep code-level visibility without manual configuration. Dynatrace's Davis AI engine automatically detects anomalies, identifies root causes, and provides actionable insights, reducing alert fatigue and accelerating problem resolution. The platform covers APM, infrastructure monitoring, log management, digital experience monitoring, and application security. It is particularly suited for large enterprises requiring automated operations, advanced analytics, and a proactive approach to performance and security.

   • Best for: Large enterprises, automated root cause analysis, AI-driven operations, deep code-level visibility, multi-cloud environments, digital experience monitoring.
   • Dynatrace product profile
   • Dynatrace official site

3. 3. Splunk — Data platform for security, observability, and operations

   Splunk offers a data platform that enables organizations to collect, index, and analyze machine-generated data from various sources. While widely known for its security information and event management (SIEM) capabilities, Splunk also provides robust observability features through Splunk Observability Cloud, which includes APM, infrastructure monitoring, log investigation, and real-user monitoring. Its strength lies in its ability to handle massive volumes of data and provide powerful search, correlation, and reporting functionalities. Splunk's flexible data model allows for custom dashboards and alerts, supporting a wide range of use cases from operational intelligence to compliance. Organizations with significant existing investments in Splunk for security often extend its use to observability for a unified data approach.

   • Best for: Large-scale data ingestion and analysis, security operations, operational intelligence, custom reporting, hybrid deployments, organizations with existing Splunk investments.
   • Splunk product profile
   • Splunk official site

4. 4. ServiceNow — IT Operations Management with integrated workflows

   ServiceNow, primarily known for its IT Service Management (ITSM) and IT Operations Management (ITOM) capabilities, offers solutions that extend into observability through its ITOM Visibility and ITOM Health products. These tools help organizations discover infrastructure components, map service dependencies, and monitor operational health. While not a dedicated APM platform like New Relic, ServiceNow focuses on connecting monitoring data with ITSM workflows, enabling automated incident creation and resolution based on observed issues. It's particularly strong for enterprises looking to integrate observability data directly into their service delivery and operational processes, leveraging a single platform for IT management and automation. ServiceNow's strength is in its workflow automation and CMDB integration.

   • Best for: Large enterprise IT service management, integrating observability with IT operations workflows, automated incident response, service mapping, CMDB integration.
   • ServiceNow product profile
   • ServiceNow documentation

5. 5. Snowflake — Data Cloud for analytics and data-driven insights

   Snowflake is a cloud data platform designed for data warehousing, data lakes, data engineering, and secure data sharing. While not an observability platform in the traditional sense of APM or infrastructure monitoring, it serves as a powerful backend for storing, processing, and analyzing vast amounts of telemetry data collected from other sources. Organizations can use Snowflake to consolidate logs, metrics, and traces, enabling complex analytical queries, long-term historical analysis, and custom dashboards for operational intelligence. Its scalable architecture and support for various data types make it suitable for advanced analytics on observability data, particularly for correlating events across different systems and building custom reporting solutions that go beyond the capabilities of some dedicated observability tools.

   • Best for: Consolidating observability data for advanced analytics, long-term data retention, custom reporting and dashboards, large-scale data warehousing for telemetry, data sharing.
   • Snowflake product profile
   • Snowflake documentation

Side-by-side

How to pick

Selecting an observability platform requires evaluating your organization's specific needs, existing technology stack, and operational priorities. Consider the following decision-tree style guidance:

1. Assess your primary use case:
   • If your core need is extensive, automated full-stack observability with deep code-level insights and AI-driven root cause analysis, Dynatrace is a strong contender, particularly for large, complex enterprise environments.
   • If you prioritize cloud-native monitoring, broad integrations, and a unified platform for both observability and security in dynamic cloud environments, Datadog offers a comprehensive solution.
   • If your organization already has significant investments in data analytics and security, and you require powerful search and correlation capabilities across massive datasets, Splunk, especially its Observability Cloud, could be a natural extension.
   • If your focus is on integrating observability data directly into IT service management workflows and automating incident resolution within a consolidated IT operations platform, ServiceNow ITOM is a specialized choice.
   • If your primary goal is to consolidate and analyze vast amounts of telemetry data for custom reporting, long-term retention, and advanced analytics, often leveraging data from other observability tools, Snowflake can serve as a powerful data backend.
2. Evaluate your technical environment:
   • For highly distributed, microservices-based, and serverless architectures in the cloud, Datadog and Dynatrace offer robust support and specialized monitoring capabilities.
   • If you operate a hybrid environment with a mix of on-premises and cloud infrastructure, Splunk's flexibility in data ingestion and analysis can be beneficial.
   • Consider the programming languages and frameworks your applications use. Most top alternatives support common languages, but specific niche technologies might be better served by one platform over another due to agent availability or instrumentation depth.
3. Consider your team's expertise and operational model:
   • If your team prefers a platform that minimizes manual configuration and offers automated insights, Dynatrace's AI-driven approach can reduce operational overhead.
   • If your team is proficient in data querying and custom dashboarding, Splunk's powerful search language and flexible reporting can be advantageous.
   • For organizations aiming to streamline IT operations and leverage a single platform for service management and monitoring, ServiceNow provides integrated workflows.
4. Analyze pricing and scalability:
   • Review the pricing models carefully, as they often vary based on data ingest, hosts monitored, or computing resources. Estimate your potential usage to compare total cost of ownership.
   • Consider how each platform scales with your anticipated growth in data volume and infrastructure complexity. Cloud-native solutions like Datadog and Snowflake are designed for elastic scalability.
5. Trial and proof of concept:
   • Before making a final decision, conduct trials or proofs of concept with your top 2-3 alternatives. Evaluate ease of setup, data collection, dashboarding, alerting, and problem resolution in your actual environment.
   • Engage your development, operations, and security teams in the evaluation process to ensure the chosen solution meets diverse stakeholder requirements.