Datadog Online Service

A Datadog Online Service is an online application performance monitoring platform that can be used to create datadog monitoring solutions (that support server monitoring tasks, application performance monitoring tasks, and security monitoring tasks).

AKA: Datadog APM Platform, Datadog Observability Platform, Datadog Monitoring Solution.
Context:
- It can typically monitor Datadog Infrastructure Metrics from datadog agents installed across server environments, cloud environments, and container environments.
- It can typically collect Datadog Performance Data through datadog integrations with over 750 different technology systems and cloud services.
- It can typically visualize Datadog Monitoring Information through datadog dashboards that combine metric data, log data, and trace data in real-time.
- It can typically alert Datadog System Administrators about system anomalies using datadog alerting rules and datadog notification channels.
- It can typically analyze Datadog Log Data through datadog log management systems that automatically collect and analyze log information for troubleshooting purposes.
- ...
- It can often secure Datadog Cloud Environments through datadog security capabilities like datadog agentless scanning, datadog cloud security management, and datadog code security.
- It can often troubleshoot Datadog Application Issues using datadog distributed tracing that tracks request paths across microservice architectures.
- It can often optimize Datadog AI Applications through datadog LLM observability that monitors AI model performance, AI model drift, and AI infrastructure utilization.
- It can often manage Datadog Incident Response through datadog on-call systems that provide incident management and incident escalation.
- It can often remediate Datadog Kubernetes Issues using datadog kubernetes active remediation with curated guidance and automated fixes.
- ...
- It can range from being a Basic Datadog Online Platform to being an Enterprise Datadog Online Platform, depending on its datadog subscription tier.
- It can range from being a Datadog Infrastructure Monitoring Platform to being a Datadog Full-Stack Observability Platform, depending on its datadog capability scope.
- It can range from being a Traditional Datadog Online Platform to being an AI-Enhanced Datadog Online Platform, depending on its datadog AI capability implementation.
- ...
- It can integrate with Datadog Cloud Service Providers for datadog cloud monitoring, including datadog AWS integrations, datadog Azure integrations, and datadog Google Cloud integrations.
- It can connect to Datadog DevOps Tools for datadog development workflow monitoring, including datadog CI/CD integrations and datadog container orchestration integrations.
- It can support Datadog Database Systems for datadog database monitoring, including datadog SQL database integrations and datadog NoSQL database integrations.
- ...
Examples:
- Datadog Platform Versions, such as:
  - Datadog Platform (2022-2025), with datadog AI observability, datadog cloud security, and datadog kubernetes management.
  - Datadog Platform (2019-2021), with expanded datadog APM capabilities and datadog network monitoring.
  - Datadog Platform (2015-2018), with datadog infrastructure monitoring and initial datadog application monitoring.
  - Datadog Platform (2010-2014), with core datadog server monitoring and early datadog cloud integrations.
- Datadog Capability Domains, such as:
- ...
Counter-Examples:
- Dynatrace, which uses a different agent architecture and focuses more on automatic dependency discovery than datadog manual integration.
- New Relic, which emphasizes developer-centric observability rather than datadog operations-focused monitoring.
- AppDynamics, which specializes in business transaction monitoring instead of datadog full-stack observability.
- Prometheus, which is an open-source monitoring system rather than a datadog commercial SaaS platform.
- Grafana, which is primarily a visualization platform that can connect to multiple data sources rather than a datadog integrated monitoring solution.
See: StatsD, IT Infrastructure Monitoring Platform, Network Monitoring System, Cloud Computing Observability, System Monitoring Tool, Graphite Platform, Application Performance Management, Cloud Security Platform.

References

https://www.datadoghq.com/
Metric Types: http://github.com/etsy/statsd/blob/master/docs/metric_types.md
DataDog StatsD: http://docs.datadoghq.com/guides/dogstatsd/
StatsD implementations: https://github.com/etsy/statsd/wiki

2025-03-18

Perplexity.ai
- Question: What is the Datadog Platform release history and capabilities evolution?
- Answer: Datadog has transformed from a cloud infrastructure monitoring startup to a comprehensive observability and security platform. Founded in 2010, the company has expanded its capabilities significantly through continuous innovation, strategic releases, and product expansion.
  - Founding and Early Development:
    - Origins and Vision:
      - Founded in New York City in 2010 by Olivier Pomel and Alexis Lê-Quôc, who previously worked together at Wireless Generation.
      - Established with a vision to reduce friction between developer and systems administration teams.
      - Created a cloud infrastructure monitoring service featuring dashboards, alerting capabilities, and metrics visualizations.
    - Initial Growth and Funding:
      - 2010: Seed Funding from NYC Seed, Contour Venture Partners, IA Ventures, and others.
      - 2012: $6.2 million Series A round co-led by Index Ventures and RTP Ventures.
      - 2014: $15 million Series B round led by OpenView Venture Partners.
      - 2015: $31 million Series C round led by Index Ventures.
      - 2016: $94.5 million Series D round led by ICONIQ Capital.
  - Technical Evolution and Platform Development:
    - Technology Foundation:
      - Initially built with a Python-based agent forked from code created for Server Density in 2009.
      - Transitioned to a Go-based agent with the release of version 6.0.0 on February 28, 2018.
      - Developed a robust backend using various open and closed source technologies including D3, Apache Cassandra, Kafka, and PostgreSQL.
    - Expanding Cloud Support:
      - 2014: Expanded support to include AWS, Microsoft Azure, Google Cloud Platform, and Red Hat OpenShift.
      - As of October 2024, the platform supports over 750 integrations.
  - Product Capabilities and Platform Expansion:
    - Core Monitoring Capabilities:
      - Infrastructure Monitoring: Enables teams to gain visibility into servers, containers, and cloud resources.
      - Application Performance Monitoring: Introduced in 2016, offering full-stack monitoring solutions.
      - Network Performance Monitoring: Provides insights into network traffic and communication between services.
      - Serverless Monitoring: Extends Observability to serverless computing environments.
      - Log Management: Automatically collects and analyzes logs for troubleshooting and Performance Analysis.
    - Security Capabilities:
      - Cloud Security Management: Introduced Agentless Scanning in 2024, enabling security teams to monitor vulnerabilities within hosts, containers, and serverless functions.
      - Data Security: Helps teams identify and classify sensitive data at risk in cloud environments, such as Amazon S3 buckets.
      - Code Security (IAST): A production-ready Interactive Applications Security Testing solution.
      - Security Inbox: Launched in August 2023 to help engineers prioritize security issues.
    - Artificial Intelligence and Observability:
      - LLM Observability: Made generally available in June 2024, helping businesses monitor, improve, and secure generative AI applications.
      - AI Stack Integrations: Developed integrations across the AI Technology Stack including infrastructure (NVIDIA, CoreWeave, AWS, Azure, Google Cloud), Embeddings Management (Weaviate, Pinecone, Airbyte), and Model Serving (Torchserve, VertexAI, Amazon Sagemaker).
      - Model Monitoring: Provides visibility into Model Catalog, Model Performance, and Model Drift.
  - Recent Releases and Platform Evolution:
    - Agent Updates:
      - March 4, 2025: Released Agent 7.63.3 with security improvements.
      - February 28, 2025: Released Agent 7.63.2 with various updates and fixes.
      - February 26, 2025: Released Agent 7.63.1 with bug fixes.
    - New Product Launches:
      - December 2024: Unveiled a modern approach to Cloud SIEM, delivering risk-based insights, scalability, Cost Efficiency, and Real Time Detection.
      - November 2024: Introduced Kubernetes Active Remediation to provide curated guidance and End To End Management of Kubernetes environments.
      - June 2024: Launched multiple new capabilities including Datadog On-Call for observability-enriched paging and Unified Incident Management.
      - May 2024: Released IT Event Management to enhance AIOps capabilities.
  - Market Position and Industry Recognition:
    - Public Offering and Valuation:
      - Initial public offering on the Nasdaq Exchange on September 19, 2019.
      - Sold 24 million shares, raising $648 million and achieving a valuation of $8.7 billion.
      - Prior to going public, Datadog rejected an Acquisition Offer of over $7 billion from Cisco.
    - Industry Recognition:
      - Named a Leader in the 2023 Gartner Magic Quadrant for Application Performance Monitoring And Observability.
      - In 2020, recognized as a Gartner Peer Insights Customers Choice for IT Infrastructure Monitoring Tools.
      - The IAST Security Solution achieved 100% accuracy in OWASP Benchmark testing.
- Citations:

[1] https://en.wikipedia.org/wiki/Datadog
[2] https://github.com/DataDog/ddprof/releases
[3] https://www.techtarget.com/searchitoperations/definition/Datadog
[4] https://docs.konghq.com/hub/kong-inc/datadog/changelog/
[5] https://canvasbusinessmodel.com/blogs/brief-history/datadog-brief-history
[6] https://investors.datadoghq.com/news-releases/news-release-details/datadogs-platform-expands-support-monitoring-and-troubleshooting
[7] https://docs.datadoghq.com/agent/
[8] https://investors.datadoghq.com/news-releases
[9] https://github.com/DataDog/datadog-agent/releases
[10] https://ddtrace.readthedocs.io/en/stable/releasenotes.html
[11] https://investors.datadoghq.com/news-releases/news-release-details/datadog-launches-cloud-and-application-security-capabilities
[12] https://airflow.apache.org/docs/apache-airflow-providers-datadog/stable/changelog.html
[13] https://www.youtube.com/watch?v=YmJcbAI_OCg
[14] https://www.datadoghq.com/about/latest-news/press-releases/datadog-launches-new-data-cloud-and-application-security-capabilities-covering-development-and-production-environments/
[15] https://github.com/DataDog/datadog-agent/blob/main/CHANGELOG.rst
[16] https://ddtrace.readthedocs.io/en/v1.7.0/release_notes.html
[17] https://ddtrace.readthedocs.io/en/v1.16.0/release_notes.html
[18] https://fivetran.com/docs/logs/external-logs/datadog/changelog
[19] https://docs.datadoghq.com/agent/guide/version_differences/
[20] https://docs.datadoghq.com/dashboards/guide/version_history/
[21] https://docs.datadoghq.com

2023

chat
- Datadog: Datadog is a cloud-based APM system that provides real-time monitoring, distributed tracing, and log management for applications and infrastructure. It includes metrics and traces visualization, anomaly detection, and integrations with over 400 technologies. Datadog also provides APIs for custom integrations and supports collaboration and communication features.

2019

(Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Datadog Retrieved:2019-3-12.
- Datadog is a monitoring service for cloud-scale applications, providing monitoring of servers, databases, tools, and services, through a SaaS-based data analytics platform.

2016

https://www.datadoghq.com/
- QUOTE: With turn-key integrations, Datadog seamlessly aggregates metrics and events across the full devops stack:
  - SaaS and Cloud providers
  - Automation tools
  - Monitoring and instrumentation
  - Source control and bug tracking
  - Databases and common server components
  - All listed integrations are supported by Datadog

2016b

(Wikipedia, 2016) ⇒ http://en.wikipedia.org/wiki/datadog#Technology Retrieved:2016-1-6.
- Datadog uses an open-source agent ^[1] written in Python for gathering metrics and events. Its backend is built using a number of open and closed source technologies including D3, Apache, Cassandra, Kafka, PostgreSQL, etc. ^[2] In 2014, Datadog support was broadened to multiple cloud service providers including Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform and Red Hat OpenShift. The company added OpenStack support as well. ^[3]

↑ GitHub, "Datadog / dd-agent." Retrieved February 20, 2015.
↑ Hakka Labs, Alexis Lê-Quôc, "Realtime Data Analytics at Datadog." January 9, 2014. Retrieved February 20, 2015
↑ SDxCentral, Erin Moriarty, "Monitoring Service Datadog Gets $31M in Funding." January 29, 2015. Retrieved February 6, 2015.

[1] GitHub, "Datadog / dd-agent." Retrieved February 20, 2015.

[2] Hakka Labs, Alexis Lê-Quôc, "Realtime Data Analytics at Datadog." January 9, 2014. Retrieved February 20, 2015

[3] SDxCentral, Erin Moriarty, "Monitoring Service Datadog Gets $31M in Funding." January 29, 2015. Retrieved February 6, 2015.

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