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Best alternatives to New Relic

Discover open-source, free tier, and premium alternatives to New Relic. Compare scores, pros/cons, and deployment paths instantly.

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G

Grafana Stack

Alternative to New Relic

HybridOpen-Source (Apache 2.0) + Proprietary add-onsOpen CorePublic APIWebhooksPluginsSDK
SlackJiraAWSAzureGoogleOkta

Best for

Open-source observability and self-hosting teams

Cost

Core components are open source and free to self-host; managed cloud offerings and enterprise features are available via subscription.

Summary

Open-source observability stack centered on Grafana for visualization, commonly paired with Prometheus, Loki, Tempo, and Mimir for metrics, logs, traces, and alerting.

Why Switch

Teams switch from New Relic to Grafana Stack when they want a vendor-neutral, self-hostable observability stack with flexible dashboards and lower-cost open-source components.

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Migration Playbook

  1. Export metrics data from New Relic using the New Relic Query Language (NRQL) API or the Metrics API in JSON format. Map key fields such as metric names, timestamps, values, and tags to Prometheus-compatible metrics format. Import the transformed metrics into Prometheus by pushing them via the Prometheus Pushgateway or by configuring Prometheus to scrape the exported data endpoints.
  2. Extract logs from New Relic Logs by exporting them in JSON or CSV format, ensuring fields like timestamp, log level, message, and attributes are preserved. Map these fields to Loki's log entry format, including labels for filtering and querying. Import the logs into Loki by using the Loki Push API or by placing the log files in a location monitored by Promtail for ingestion.
  3. Export distributed tracing data from New Relic in OpenTelemetry or Zipkin format, capturing spans, trace IDs, timestamps, and attributes. Map these fields to Tempo's trace ingestion format. Import the traces into Tempo by sending them through Tempo's HTTP ingestion API or by configuring Tempo to receive traces via OpenTelemetry Collector configured to pull from New Relic exports.

Pros

  • 🟢Flexible and widely adopted open-source ecosystem
  • 🟢Excellent dashboards and visualization
  • 🟢Vendor-neutral and highly extensible
  • 🟢Strong community support

Cons

  • 🔴Requires assembling and operating multiple components
  • 🔴Less turnkey than commercial suites
  • 🔴Advanced enterprise features may require paid offerings

0 builders switched

Go to Grafana Stack
D

Dynatrace

Alternative to New Relic

SubscriptionEnterpriseCloud, On-PremisesPublic APIPluginsWebhooksSDK
GitHubJiraSlackAWSAzureGoogle

Best for

Large enterprise operations and AIOps teams

Cost

Primarily enterprise subscription pricing, typically quote-based and usage-driven; generally positioned at the higher end of the market.

Summary

Dynatrace is an AI-powered, full stack, automated performance monitoring platform for cloud environments, enabling enterprises to optimize application performance and infrastructure health.

Why Switch

Teams switch from New Relic to Dynatrace when they need deeper automatic discovery, AI-assisted root-cause analysis, and full-stack visibility for complex enterprise environments.

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Migration Playbook

  1. Export application performance data from New Relic using the New Relic REST API in JSON format, focusing on key metrics such as response times, error rates, and throughput. Map these metrics to Dynatrace's equivalent performance indicators, ensuring that application names and environment tags align. Import the data into Dynatrace via the Dynatrace API's Metrics Ingest endpoint to initialize monitoring baselines.
  2. Extract infrastructure monitoring data from New Relic by exporting host and server metrics through the New Relic Infrastructure API in CSV or JSON format. Map fields such as CPU usage, memory consumption, and disk I/O to Dynatrace's host metrics schema. Use Dynatrace's OneAgent deployment on target hosts for real-time data ingestion, and supplement with the imported historical data via the Dynatrace API to maintain continuity.
  3. Migrate alerting and notification configurations by exporting New Relic alert policies and conditions using the New Relic Alerts API in JSON format. Translate alert thresholds, notification channels, and incident preferences to Dynatrace problem detection and alerting rules. Configure these settings in Dynatrace through its API or UI, ensuring that alert recipients and escalation paths are preserved for seamless incident management.

Pros

  • 🟢Comprehensive full-stack observability with AI-driven root cause analysis
  • 🟢Strong automation capabilities reducing manual monitoring effort
  • 🟢Supports hybrid and multi-cloud environments with broad integrations
  • 🟢Highly scalable for enterprise-grade deployments

Cons

  • 🔴Pricing can be high for smaller organizations
  • 🔴Complexity in initial setup and configuration for some use cases

0 builders switched

Go to Dynatrace
E

Elastic Observability

Alternative to New Relic

SubscriptionEnterpriseHybridProprietaryPublic APIWebhooksPluginsSDK
GitHubGitLabSlackJiraAWSAzure

Best for

Teams needing scalable log search and full-stack observability

Cost

Commercial subscription pricing; self-managed and cloud options. Costs vary by data ingest, retention, and deployment size.

Summary

Elastic Observability is Elastic's commercial observability and log analytics suite, built on the Elastic Stack for search, logs, metrics, traces, and APM at scale.

Why Switch

Teams switch from New Relic to Elastic Observability when they want strong log search, flexible deployment options, and a search-driven platform for observability and security analytics.

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Migration Playbook

  1. Export application performance monitoring (APM) data from New Relic using the New Relic Insights API in JSON format, mapping key fields such as transaction names, response times, error rates, and custom attributes to corresponding Elastic APM fields like transaction.name, transaction.duration.us, error.count, and labels. Import this data into Elastic Observability via the Elastic APM Server API or by ingesting JSON files into Elasticsearch indices configured for APM data.
  2. Extract infrastructure metrics and host-level telemetry from New Relic using the Metrics API or by exporting data in CSV/JSON format, ensuring mapping of fields like CPU usage, memory utilization, disk I/O, and network traffic to Elastic Metrics fields (e.g., system.cpu.percent, system.memory.used.pct). Import these metrics into Elastic Observability by configuring Metricbeat to ingest the exported data or by directly indexing the data into Elasticsearch using the Bulk API.
  3. Export log data from New Relic Logs in JSON or raw text format, mapping log attributes such as timestamp, log level, message, and service name to Elastic Common Schema (ECS) fields like @timestamp, log.level, message, and service.name. Import logs into Elastic Observability by using Logstash or Filebeat to parse and forward the logs into Elasticsearch, ensuring proper pipeline configuration to maintain field mappings and enable unified observability.

Pros

  • 🟢Strong log search and analytics capabilities
  • 🟢Unified logs, metrics, traces, and APM
  • 🟢Flexible deployment options including self-managed and cloud
  • 🟢Large ecosystem and broad adoption

Cons

  • 🔴Can become expensive at high ingest volumes
  • 🔴Requires tuning and operational expertise
  • 🔴Some advanced features are tied to higher tiers

0 builders switched

Go to Elastic Observability
D

Datadog

Alternative to New Relic

SubscriptionEnterpriseCloudPublic APIWebhooksPluginsSDK
GitHubJiraSlackAWSAzureGoogle

Best for

Organizations needing comprehensive cloud monitoring with strong container and microservices support.

Cost

Subscription pricing based on usage and feature sets, suitable for startups to large enterprises.

Summary

Datadog is a monitoring and analytics platform for developers, IT operations teams, and business users in the cloud age.

Why Switch

Teams switch from New Relic to Datadog when they want a broader integrated observability suite with strong Kubernetes support, but are willing to manage more complex usage-based pricing.

SOC2HIPAAGDPRISO 27001

Migration Playbook

  1. Export application performance and infrastructure monitoring data from New Relic using the New Relic Insights API or by exporting JSON/CSV reports. Map key fields such as transaction names, response times, error rates, host metrics, and custom attributes to Datadog equivalents like APM traces, metrics, and tags.
  2. Use Datadog's API or import tools to ingest the exported data. For metrics and events, utilize the Datadog Metrics API and Events API, ensuring that New Relic's metric names and attributes are translated into Datadog's metric naming conventions and tags for proper categorization and filtering.
  3. Recreate dashboards and alerting rules in Datadog by mapping New Relic's dashboard widgets and alert conditions to Datadog's monitors and dashboards. Use Datadog's dashboard API to programmatically import or configure these elements, ensuring continuity in monitoring and alerting workflows.

Pros

  • 🟢Unified platform for metrics, traces, and logs
  • 🟢Strong integrations ecosystem including cloud and container platforms
  • 🟢Highly scalable and flexible alerting capabilities

Cons

  • 🔴Pricing can escalate with data volume
  • 🔴Some users find the UI complex for new users

0 builders switched

Go to Datadog
S

Splunk Observability Cloud

Alternative to New Relic

SubscriptionEnterpriseCloud-Native / SaaSProprietary, SubscriptionPublic APIWebhooksPluginsSDK
AWSAzureGoogleSlackJiraSalesforce

Best for

Enterprise observability and operational intelligence teams

Cost

Subscription pricing, typically quote-based and usage-dependent; enterprise deployments can be costly relative to smaller tools.

Summary

Commercial observability platform for metrics, traces, logs, and infrastructure monitoring, with strong analytics and enterprise integrations.

Why Switch

Teams switch from New Relic to Splunk Observability Cloud when they need enterprise-grade analytics and broader Splunk ecosystem integration for large-scale environments.

SOC2GDPR

Migration Playbook

  1. Export application performance metrics and infrastructure monitoring data from New Relic using the New Relic Query Language (NRQL) API or by exporting JSON/CSV reports. Map key fields such as timestamp, metric name, value, host, and application ID to Splunk Observability Cloud's metric ingestion format. Import the data into Splunk using the Metrics Ingest API or via the Splunk OpenTelemetry Collector configured for metrics ingestion.
  2. Extract distributed tracing data from New Relic by exporting trace spans in OpenTelemetry or Zipkin format through New Relic's Trace API or export tools. Map span attributes like trace ID, span ID, parent ID, operation name, start time, and duration to the corresponding fields in Splunk Observability Cloud's tracing ingestion schema. Import the trace data using Splunk's Trace Ingest API or the OpenTelemetry Collector configured for trace data.
  3. Export log data from New Relic Logs by using the New Relic Logs API or by exporting logs in JSON or syslog format. Map log fields such as timestamp, log level, message, host, and application context to Splunk Observability Cloud's log ingestion format. Import logs into Splunk via the HTTP Event Collector (HEC) or through the Splunk OpenTelemetry Collector configured for log ingestion.

Pros

  • 🟢Strong enterprise-grade analytics
  • 🟢Good integration with broader Splunk ecosystem
  • 🟢Scales well for large environments
  • 🟢Useful for logs and operational intelligence

Cons

  • 🔴Can be expensive
  • 🔴Product suite can be complex
  • 🔴May require significant tuning for cost control

0 builders switched

Go to Splunk Observability Cloud

Community FAQ

Questions by product

New Relic FAQ

Is it possible to self-host New Relic or is it only available as a cloud service?

New Relic is primarily offered as a cloud-based SaaS platform and does not support self-hosting. All telemetry data is processed and stored in New Relic's managed cloud infrastructure, so on-premises deployment is not available.

Community insight informed by Reddit discussions

Does New Relic provide any offline functionality or local data caching for telemetry when connectivity is lost?

New Relic agents typically buffer telemetry data locally for a short period when connectivity is interrupted, but there is no full offline mode. Data is sent to New Relic’s cloud as soon as the connection is restored. Extended offline operation or local querying is not supported.

Community insight informed by Hacker News discussions

Who owns the data collected by New Relic and what are the options for data export or migration?

Data collected by New Relic is owned by the customer, but it is stored within New Relic’s cloud environment. Customers can export raw data and query results via New Relic’s APIs or download reports, but there is no turnkey solution for full data migration out of the platform. Planning for data retention and export is recommended.

Community insight informed by StackOverflow discussions

Are there any limitations or rate limits on New Relic’s APIs that impact large scale telemetry ingestion or querying?

New Relic imposes rate limits on API usage depending on the account tier. High-volume telemetry ingestion is supported but may require enterprise agreements. Query APIs also have limits on request rates and data volume to ensure platform stability. Users should review New Relic’s API documentation for detailed quotas.

Community insight informed by Forums discussions

Grafana Stack FAQ

How complex is it to self-host the full Grafana Stack including Prometheus, Loki, Tempo, and Mimir?

Self-hosting the full Grafana Stack requires deploying and managing multiple independent components, each with its own configuration and resource needs. You need to set up Prometheus for metrics scraping, Loki for log aggregation, Tempo for tracing, and Mimir for long-term metrics storage. Coordination between these services and Grafana itself is necessary for a seamless observability experience. While Helm charts and Docker Compose setups exist to simplify deployment, operational complexity remains moderate to high, especially around scaling, storage management, and alerting configurations.

Community insight informed by Reddit discussions

Can the Grafana Stack operate fully offline without internet connectivity?

Yes, the Grafana Stack can operate fully offline since all components are open-source and self-hosted. None of the core functionality requires internet access once installed. However, initial setup may require downloading container images or binaries, and some plugins or data sources might need internet access unless pre-downloaded. Also, alerting integrations that rely on external services (e.g., PagerDuty, Slack) will not function offline unless you have local alternatives configured.

Community insight informed by Hacker News discussions

Who owns the data collected and stored by the Grafana Stack, and how is data privacy handled?

Since the Grafana Stack is fully self-hosted and open-source, you retain full ownership and control over all collected metrics, logs, and traces. Data is stored on your infrastructure, and no telemetry or usage data is sent to third parties by default. This setup ensures maximum data privacy and compliance with internal policies or regulations. You can also configure data retention and access controls within each component to further secure sensitive information.

Community insight informed by StackOverflow discussions

Are there any API limitations when querying metrics or logs across the Grafana Stack components?

Each component exposes its own API with some limitations. Prometheus’s query API is powerful but can be resource-intensive for complex queries or large datasets. Loki’s log query API supports flexible logQL queries but may have performance constraints on large-scale log volumes. Tempo’s trace API is optimized for distributed tracing but is less mature feature-wise compared to commercial tracing solutions. Grafana itself acts as a visualization layer and supports querying multiple datasources but does not unify APIs. Rate limiting and query timeouts should be configured carefully to avoid overload.

Community insight informed by Forums discussions

What are the recommended migration or export paths if we want to move data out of the Grafana Stack?

Migration and export depend on the component. Prometheus supports exporting metrics snapshots and remote write to other storage backends. Loki allows exporting logs via its API or by extracting data from its underlying storage (e.g., object stores). Tempo supports exporting traces in standard formats like Jaeger or Zipkin. Grafana dashboards and alert rules can be exported as JSON files for reuse. However, there is no single unified export tool for the entire stack, so migration requires component-specific approaches and careful planning.

Community insight informed by Reddit discussions

Dynatrace FAQ

Is it possible to self-host Dynatrace on-premises, or is it purely SaaS-based?

Dynatrace primarily operates as a SaaS platform with cloud-hosted services. However, it offers a Managed version that can be deployed on-premises or in private clouds, but this requires significant infrastructure and expertise to set up and maintain. The Managed deployment is more complex and suited for large enterprises with dedicated DevOps teams.

Community insight informed by Reddit discussions

Does Dynatrace support offline monitoring or edge environments with intermittent connectivity?

Dynatrace agents collect telemetry data locally and buffer it temporarily if connectivity is lost, but continuous offline operation with full functionality is not supported. The platform relies on cloud or managed cluster connectivity to perform AI-driven analysis and root cause detection, so extended offline use will limit its capabilities.

Community insight informed by Hacker News discussions

Who owns the monitoring data collected by Dynatrace and how is data privacy handled?

Data collected by Dynatrace is owned by the customer, but it is stored and processed within Dynatrace’s cloud or managed infrastructure depending on deployment. Customers can configure data retention policies and control access via role-based permissions. For sensitive environments, the Managed version allows keeping data within private networks to enhance privacy.

Community insight informed by StackOverflow discussions

Are there any limitations or rate limits on Dynatrace’s API for data extraction or integration?

Dynatrace APIs have documented rate limits to ensure platform stability, typically allowing several thousand requests per minute depending on the endpoint. Bulk data export is supported but may require pagination and batching. For large-scale integrations, it is recommended to use the official SDKs and follow best practices to avoid throttling.

Community insight informed by Forums discussions

What options does Dynatrace provide for migrating monitoring data or exporting historical performance metrics?

Dynatrace does not provide a native full export of historical monitoring data in bulk. However, users can export specific metrics, events, and logs via APIs or integrate with external data lakes and SIEM tools for long-term storage. Migration between environments typically involves reconfiguration rather than data transfer.

Community insight informed by Reddit discussions

Elastic Observability FAQ

How complex is it to self-host Elastic Observability for a medium-sized team?

Self-hosting Elastic Observability requires deploying and managing the full Elastic Stack components: Elasticsearch, Kibana, Beats, and APM Server. While Elastic provides Docker images and Helm charts for Kubernetes, you need solid expertise in cluster sizing, resource tuning, and security hardening. Operational overhead includes managing scaling, upgrades, and backups. For medium-sized teams, expect a learning curve and dedicated infrastructure management, but the flexibility and control can be worth it compared to Elastic Cloud.

Community insight informed by Reddit discussions

Does Elastic Observability support offline or air-gapped environments?

Elastic Observability can be deployed fully on-premises in air-gapped environments since all components run locally without requiring outbound internet access. However, you must manually handle license activation and updates by importing packages and licenses offline. Some cloud-based features and integrations will not be available, but core log, metrics, and APM collection and analysis work fully offline.

Community insight informed by Forums discussions

Who owns the data ingested into Elastic Observability and how is it stored?

When self-hosting Elastic Observability, you retain full ownership and control over all ingested data, which is stored in your Elasticsearch clusters. Elastic does not access your data unless you use Elastic Cloud or managed services. Data is stored in Elasticsearch indices with configurable retention policies. You are responsible for securing and backing up your data according to your compliance requirements.

Community insight informed by Hacker News discussions

Are there any API limitations for extracting logs and metrics from Elastic Observability?

Elastic Observability exposes extensive REST APIs through Elasticsearch and Kibana for querying logs, metrics, traces, and APM data. However, some advanced analytics and machine learning features are only accessible via higher-tier subscriptions or Elastic Cloud. The APIs support bulk data export but rate limits and cluster resource constraints can impact large-scale extraction workflows. Custom plugins or scripts may be needed for complex export scenarios.

Community insight informed by StackOverflow discussions

What are the best practices for migrating existing logs and metrics into Elastic Observability?

Migration typically involves exporting data from your current logging or metrics system in a compatible format (e.g., JSON, CSV) and ingesting it via Beats, Logstash, or Elasticsearch Bulk API. It’s important to map your existing schema to Elastic’s index patterns and configure ingest pipelines for parsing. For historical data, bulk reindexing can be resource-intensive, so plan for downtime or phased migration. Elastic’s documentation and community scripts can assist with common sources like syslog, Prometheus, or Splunk exports.

Community insight informed by Forums discussions

Datadog FAQ

Can Datadog be self-hosted or is it strictly SaaS?

Datadog is a fully managed SaaS platform and does not offer a self-hosted version. All data is processed and stored in Datadog's cloud infrastructure, so on-premises deployment is not supported.

Community insight informed by Reddit discussions

Does Datadog support offline data collection and batch upload when connectivity is restored?

Datadog agents collect metrics and logs in real-time and require network connectivity to send data to Datadog's cloud. While some buffering occurs locally in the agent, there is no full offline mode; prolonged network outages will result in data loss.

Community insight informed by Hacker News discussions

What are the data ownership and retention policies for data sent to Datadog?

All monitoring data sent to Datadog is owned by the customer but stored on Datadog's cloud infrastructure. Customers can configure retention periods per data type, but data deletion and export must be managed via Datadog's APIs or UI. There is no local data ownership since the platform is SaaS.

Community insight informed by StackOverflow discussions

Are there any limitations or rate limits on Datadog's API for exporting monitoring data?

Datadog's API enforces rate limits based on account type and endpoint, typically around 300 requests per minute for standard plans. Bulk export of large datasets may require pagination and batching. Users should consult the official API documentation to design efficient export workflows.

Community insight informed by Forums discussions

What are the recommended migration or export paths if moving away from Datadog?

Datadog provides APIs to export metrics, logs, and traces, but there is no one-click full data export feature. For migration, users typically export data via APIs or integrations into alternative storage or monitoring solutions. Planning for data retention and format compatibility is essential.

Community insight informed by Reddit discussions

Splunk Observability Cloud FAQ

Is Splunk Observability Cloud available for self-hosting or is it strictly SaaS?

Splunk Observability Cloud is offered as a fully managed SaaS platform and does not provide a self-hosted deployment option. Enterprises requiring on-premises solutions would need to consider Splunk Enterprise products instead, as the Observability Cloud is designed for cloud-native scalability and managed services.

Community insight informed by Reddit discussions

Can Splunk Observability Cloud function offline or in air-gapped environments?

No, Splunk Observability Cloud requires continuous internet connectivity to ingest and analyze telemetry data. It is a cloud-native SaaS platform and does not support offline operation or deployment in air-gapped environments. For isolated environments, customers typically use Splunk Enterprise with local data collection and analysis.

Community insight informed by Hacker News discussions

Who owns the data ingested into Splunk Observability Cloud and what are the data retention policies?

Data ingested into Splunk Observability Cloud remains the property of the customer. Splunk acts as a data processor under the customer’s control. Retention policies vary based on the subscription plan and can be configured, but by default, data is retained for a limited period depending on the data type (metrics, logs, traces). Customers should review their contract and Splunk’s data handling policies for specifics.

Community insight informed by StackOverflow discussions

Are there any API limitations or rate limits when using Splunk Observability Cloud for telemetry ingestion?

Yes, Splunk Observability Cloud enforces API rate limits to ensure platform stability and fair usage. These limits depend on the subscription tier and the specific API endpoint. For example, ingestion endpoints have defined throughput limits and burst capacities. Customers needing higher limits can contact Splunk support to discuss quota increases or enterprise agreements.

Community insight informed by Forums discussions

What options exist for exporting or migrating data out of Splunk Observability Cloud?

Splunk Observability Cloud provides APIs and export features to retrieve metrics, logs, and traces data. However, there is no native bulk export or migration tool for complete data extraction. Customers typically use the APIs to programmatically export data for backup or migration purposes. For large-scale migrations, coordination with Splunk professional services is recommended.

Community insight informed by Reddit discussions

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