Dynamic Alternative Stack

Best alternatives to Datadog

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

Swipe alternatives in Focus ModeCompare head-to-head in VS Mode
G

Grafana Stack

Alternative to Datadog

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 Datadog to Grafana Stack when they prefer a more flexible, self-managed observability approach and want to reduce reliance on a fully managed proprietary platform.

SOC2GDPR

Migration Playbook

  1. Export metrics data from Datadog using the Datadog API in JSON format, focusing on key metrics such as CPU usage, memory, and application-specific metrics. Map Datadog metric names and tags to Prometheus-compatible metric names and labels. Import the transformed metrics into Prometheus using the Prometheus remote write API or by converting JSON to Prometheus exposition format and scraping via Prometheus exporters.
  2. Export logs from Datadog in JSON format via the Datadog Logs API, ensuring to include timestamp, log level, message, and any relevant tags or attributes. Map Datadog log fields to Loki's log entry format, including labels for filtering and querying. Push the logs into Loki using the Loki HTTP API or by configuring Promtail to ingest the exported logs after transformation.
  3. Export tracing data from Datadog in OpenTelemetry or Jaeger format using Datadog's tracing API or export tools. Map Datadog trace attributes and spans to the OpenTelemetry or Jaeger data model compatible with Tempo. Import the tracing data into Tempo by sending the traces via Tempo's ingestion API or by using the OpenTelemetry Collector configured to receive Datadog traces and forward them to Tempo.

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 Datadog

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 Datadog to Dynatrace when they want deeper automated root-cause analysis and dependency mapping for complex enterprise environments.

SOC2GDPRISO 27001

Migration Playbook

  1. Export monitoring dashboards and alert configurations from Datadog using the Datadog API in JSON format. Map Datadog dashboard widgets and alert conditions to Dynatrace dashboard tiles and problem detection rules, ensuring metrics and thresholds correspond to Dynatrace's data model. Import the JSON configurations into Dynatrace via the Dynatrace API or UI, recreating dashboards and alerts in the target environment.
  2. Extract logs and traces data from Datadog by exporting logs in JSON or CSV format and traces via Datadog's trace export API. Map Datadog log attributes and trace spans to Dynatrace log events and PurePath traces, aligning fields such as timestamps, service names, and error statuses. Ingest the exported logs and traces into Dynatrace using the Dynatrace Log Monitoring API and OneAgent for trace ingestion to maintain continuity in observability data.
  3. Migrate infrastructure and host monitoring settings by exporting host tags, custom metrics, and integrations from Datadog using the Datadog API in JSON format. Map Datadog host tags and custom metrics to Dynatrace entities and custom metrics, configuring equivalent integrations and monitoring plugins. Import these configurations into Dynatrace through the API or configuration UI, and deploy Dynatrace OneAgent on monitored hosts to enable full-stack monitoring.

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 Datadog

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 Datadog to Elastic Observability when they need stronger search-based analytics and more deployment flexibility across self-managed and cloud environments.

SOC2GDPR

Migration Playbook

  1. Export logs, metrics, and traces from Datadog using the Datadog API in JSON format. Map Datadog's log attributes (timestamp, message, host, service) to Elastic Common Schema (ECS) fields to ensure compatibility with Elastic Observability.
  2. Transform the exported data to align with Elastic's ECS format, adjusting field names and data types as needed. Use Logstash or Beats to ingest the transformed data into the Elastic Stack, targeting the appropriate indices for logs, metrics, and traces.
  3. Configure Elastic Observability dashboards and alerts by importing saved objects via the Kibana API. Validate data ingestion and visualization accuracy, then decommission Datadog data pipelines to complete the migration.

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
N

New Relic

Alternative to Datadog

SubscriptionEnterpriseCloudPublic APIWebhooksPluginsSDK
GitHubJiraSlackAWSAzureGoogle

Best for

Enterprises and mid-sized companies needing comprehensive observability with strong analytics.

Cost

Subscription-based pricing with tiered plans suitable for various organization sizes.

Summary

New Relic is a cloud-based observability platform that provides real-time insights into application performance, infrastructure, and customer experience.

Why Switch

Teams switch from Datadog to New Relic when they want a developer-friendly full-stack observability platform with a free tier for evaluation and gradual scaling.

SOC2GDPRISO 27001

Migration Playbook

  1. Export monitoring data from Datadog using the Datadog API to retrieve metrics, logs, and traces in JSON format. Specifically, use the Metrics API to export time series data, the Logs API for log events, and the Traces API for distributed tracing data.
  2. Map Datadog data fields to New Relic's data model: convert Datadog metric names and tags to New Relic metric names and attributes; transform log fields to New Relic log attributes ensuring timestamp, message, and severity are correctly mapped; and map trace spans and attributes to New Relic's distributed tracing format. Prepare the data in the format accepted by New Relic's ingestion APIs.
  3. Import the transformed data into New Relic using New Relic's Ingest APIs: use the Metrics API to send metric data, the Logs API for log ingestion, and the Trace API for distributed tracing data. Authenticate using New Relic API keys and verify data ingestion through New Relic's UI and API endpoints.

Pros

  • 🟢User-friendly interface with customizable dashboards
  • 🟢Strong telemetry data collection and analytics
  • 🟢Wide range of integrations including cloud providers and developer tools

Cons

  • 🔴Can become costly at scale
  • 🔴Some users report steep learning curve for advanced features

0 builders switched

Go to New Relic
S

Splunk Observability Cloud

Alternative to Datadog

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 Datadog to Splunk Observability Cloud when they already use Splunk and want tighter integration across enterprise monitoring and analytics workflows.

SOC2GDPR

Migration Playbook

  1. Export metrics, traces, and logs from Datadog using the Datadog API in JSON format. Specifically, use the Metrics API to export time series data, the Traces API for distributed tracing data, and the Logs API for log events. Map Datadog metric names and tags to Splunk Observability Cloud's metric naming conventions and tag structures to ensure consistency.
  2. Transform the exported data to match Splunk Observability Cloud's ingestion schema. For metrics, convert Datadog's JSON payloads into the Splunk OpenTelemetry Collector format. For traces, map Datadog trace IDs, spans, and attributes to the OpenTelemetry trace model supported by Splunk. For logs, reformat Datadog log entries to Splunk's log event schema, ensuring timestamp and severity fields are correctly mapped.
  3. Import the transformed data into Splunk Observability Cloud using the Splunk OpenTelemetry Collector or the Splunk Observability Cloud API endpoints. Configure the collector to ingest metrics, traces, and logs into the appropriate Splunk Observability Cloud datasets. Validate data integrity and completeness by comparing key metrics and trace counts between Datadog and Splunk after migration.

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

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

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

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

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

Explore more

Related B2B SaaS tools

Other catalog hubs tagged with B2B SaaS.