Brand Intelligence Graphcompany
Company Overview
About Datadog
Datadog is a cloud-native monitoring and security platform founded in 2010 by Olivier Pomel and Alexis Lê-Quôc, headquartered in New York City. The company went public on Nasdaq (DDOG) in September 2019 and has grown to serve over 29,000 customers as of FY2024, generating $2.68 billion in annual recurring revenue, representing approximately 26% year-over-year growth. Datadog's platform spans infrastructure monitoring, application performance management (APM), log management, security monitoring, and AI observability, positioning it as the unified observability stack for cloud-scale engineering teams.
Business Model & Competitive Advantage
The company's technical moat lies in its unified data platform that ingests metrics, traces, and logs from over 750 integrations across cloud providers, containers, databases, and third-party services. Datadog's agent-based architecture provides low-overhead telemetry collection, while its Notebooks and Dashboards enable collaboration across development and operations teams. The 2023-2025 expansion into LLM Observability—monitoring AI model latency, token costs, and hallucination rates—positions Datadog uniquely as enterprises instrument generative AI workloads at scale.
Competitive Landscape 2025–2026
By 2025-2026, Datadog competes in a consolidating observability market against Dynatrace (DT), Grafana Labs, and Cisco's Splunk (acquired March 2024 for $28B). The CrowdStrike global outage in July 2024 reinforced demand for resilient, multi-vendor monitoring. Datadog's platformization strategy—expanding to 20+ products with cross-sell upsell motions—mirrors Palo Alto Networks' approach, driving net dollar retention consistently above 115%. With AI infrastructure monitoring becoming a critical workload, Datadog is positioned to capture spend as enterprises scale GPU clusters and LLM inference pipelines in 2025-2026.
The Datadog Story
Founders
Recent Activity
View all →Learn how Datadog Work Management helps you coordinate human and AI agent–driven work while preserving context, ownership, and activity across tools.
Summing individual wins overstates true impact. See two accurate methods, holdouts and Datadog’s Cumulative Impact, and how to choose between them.
Trace AWS Lambda durable executions across invocations to investigate operations, retries, failures, waits, and function status in Datadog.
***Changed***: * Drop the `[tool.black]` block from `ddev/pyproject.toml` and the matching code path in `ddev meta scripts update-python-config` (formatting is fully handled by ruff). ([#23588](https://github.com/DataDog/integrations-core/pull/23588)) * `ddev release branch tag` now accepts `--release/-r`, `--ref`, `--rc N`, and `--yes/-y`, and prompts to confirm when run on a release branch without `--release`. Existing non-interactive callers that piped a single `y` need to pass `--yes` or one extra confirmation. ([#23860](https://github.com/DataDog/integrations-core/pull/23860)) * Bump `datadog-checks-dev` pin to `>=39.0,<41` to track the v39 release. ([#24110](https://github.com/DataDog/integrations-core/pull/24110)) * Bump `datadog-checks-dev` pin to `>=40.0,<42` to track the v40 release. ([#24842](https://github.com/DataDog/integrations-core/pull/24842)) ***Added***: * Add TaskTestRunner processor for the CI dispatcher. ([#23518](https://github.com/DataDog/integrations-core/pull/
Learn about monitoring the end-to-end health and performance of modern data pipelines.
### Bug Fixes - internal: Fixes an issue on Python 3.9 through 3.12.3 where applications can crash when background worker threads clean up thread-local or context-local data. - profiling: Fixes potential process memory growth when a stack profiling sampling cycle exits before completing a sample. - profiling: Fixes potential process memory growth in asyncio and gevent workloads after stack profiling sampling cycles exit before completion. - profiling: A rare crash happening when using gevent has been fixed.
### Bug Fixes - profiling: Fixes potential process memory growth when a stack profiling sampling cycle exits before completing a sample. - profiling: Fixes potential process memory growth in asyncio and gevent workloads after stack profiling sampling cycles exit before completion. - Fixed an issue that could have caused the internal symbol DB component to restart in nested fork child processes, potentially leading to high memory usage. - LLM Observability: Resolves an issue where OpenRouter requests made through the LiteLLM integration produced no span when the OpenAI integration was also enabled. - profiling: A rare crash happening when using gevent has been fixed.
### Bug Fixes - anthropic: Fixes an issue where spans were not exported when streamed responses were consumed using `get_final_message()` or `get_final_text()`. - code origin for spans: fix a potential memory leak triggered by having code origin for span explicitly disabled via environment variables. - internal: Fixes an issue on Python 3.9 through 3.12.3 where applications can crash when background worker threads clean up thread-local or context-local data. - snowflake: Fixes an issue where traced cursor calls fail when `command` is passed as a keyword argument to `execute` or `executemany`. - profiling: Fixes potential process memory growth when a stack profiling sampling cycle exits before completing a sample. - profiling: Fixes potential process memory growth in asyncio and gevent workloads after stack profiling sampling cycles exit before completion. - Fixed an issue that could have caused the internal symbol DB component to restart in nested fork child processes, potentially lead
### Bug Fixes - snowflake: Fixes an issue where traced cursor calls fail when `command` is passed as a keyword argument to `execute` or `executemany`. - profiling: A rare crash happening when using gevent has been fixed.
<!-- changelog --> ### Added * Tracing: Integrations: Add support for `redis-rb` 6.0/RESP3. (#6142) * Tracing: Native trace export now supports structured span metadata. (#6130) ### Changed * AppSec: Native trace export now encodes supported structured span metadata values without Ruby MessagePack encoding. (#6132) * Tracing: Native trace export now writes structured span metadata directly through libdatadog. (#6133) ### Fixed * Profiling: Fix a rare crash (`SIGSEGV`) in the profiler that could occur when sampling a thread during `Thread.new`. (#6173) * Profiling: Fix possible crash in `sample_thread()` when `rb_id2str()` returns `Qfalse`. (#6138) * Tracing: Prevent runtime configuration telemetry from being rejected when header tags or sampling rules are updated remotely. (#6126)
## What's Changed * feat(data-pipeline)!: add flush_and_close to the trace buffer by @brettlangdon in https://github.com/DataDog/libdatadog/pull/2313 * feat(common): allocation-free tag validation and parsing by @morrisonlevi in https://github.com/DataDog/libdatadog/pull/2329 * test: skip/shorten slow miri jobs by @morrisonlevi in https://github.com/DataDog/libdatadog/pull/2331 * ci(bin_tests): batch prebuild cargo builds per profile by @ekump in https://github.com/DataDog/libdatadog/pull/2335 * feat(gotter)!: add DT_HASH fallback, relocation type guard, and hook_symbol by @gyuheon0h in https://github.com/DataDog/libdatadog/pull/2297 * chore(release): merge release branch to main (#2332) by @dd-octo-sts[bot] in https://github.com/DataDog/libdatadog/pull/2336 * ci(crashtracker): fix test_receiver_uploads_partial_report_on_timeout test on macOS by @ekump in https://github.com/DataDog/libdatadog/pull/2341 * feat(sampling): OTel consistent-probability rv/th derivation (APMAPI-2181) by
## Summary This is a technical release and is otherwise identical to the 3.51.0 release ## Changes [Changes since 3.50.0](https://github.com/DataDog/dd-trace-dotnet/compare/v3.50.0...v3.51.1)
Company Timeline
Major milestones in Datadog's journey
Leadership Team
Meet the leaders behind Datadog
Olivier Pomel
Olivier Pomel is Co-Founder and CEO of Datadog, serving in this role since June 2010. He met co-founder Alexis Lê-Quôc as an undergraduate at École Centrale Paris and worked with him for nine years at Wireless Generation before founding Datadog. Under his leadership, Datadog has grown from a startup to a publicly-traded S&P 500 company with over $2.6 billion in annual revenue.
Alexis Lê-Quôc
Alexis Lê-Quôc serves as Co-Founder and Chief Technology Officer, overseeing Datadog's technology vision and product development since 2010. He is responsible for the platform's technical architecture and innovation roadmap, including the development of AI-powered observability capabilities and the time-series foundation model TOTO.
David Obstler
David Obstler has served as Chief Financial Officer since October 2018, bringing more than three decades of operational finance experience. He previously served as CFO of TravelClick and has been instrumental in guiding Datadog through its IPO and subsequent growth as a public company.
Adam Blitzer
Adam Blitzer serves as Chief Operating Officer, overseeing Datadog's global operations, go-to-market strategy, and customer success initiatives. He brings extensive experience scaling high-growth SaaS companies.
Amit Agarwal
Amit Agarwal serves as President of Datadog, working closely with the CEO to drive company strategy, product vision, and market expansion initiatives across global markets.
Ami Vora
Ami Vora brings over 20 years of product experience to her role as Chief Product Officer. She previously served as Chief Product Officer at Faire and has been instrumental in expanding Datadog's product portfolio and accelerating innovation.
Sean Walters
Sean Walters serves as Chief Revenue Officer, leading Datadog's worldwide sales organization and revenue growth strategy. He oversees customer acquisition, expansion, and retention across enterprise and commercial segments.
Sara Varni
Sara Varni serves as Chief Marketing Officer with over 15 years of marketing leadership experience. She previously served as CMO at Attentive and Twilio, bringing deep expertise in developer-focused marketing and brand building.
Kerry Acocella
Kerry Acocella serves as Executive Vice President, General Counsel, and Secretary, overseeing all legal affairs, compliance, corporate governance, and regulatory matters for Datadog globally.
Emilio Escobar
Emilio Escobar serves as Chief Information Security Officer, responsible for protecting Datadog's infrastructure, data, and customer information. He oversees the company's security strategy and ensures platform security meets the highest industry standards.
Key Differentiators
Market Leader
Datadog is recognized as a market leader in the DevOps sector, demonstrating strong industry presence and customer trust.
Enterprise Scale
With $2.68B in revenue, Datadog operates at enterprise scale with proven market validation.
Top 3 Ranked
Ranked #2 in the DevOps category, consistently recognized for excellence.
Frequently Asked Questions
Estimated Visibility Trend (Beta)
Simulated 8-week rolling score
Based on estimated brand signals. Historical tracking coming soon.
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