{ "document_type": "investor_presentation", "tools": [ { "name": "getOverview", "description": "Returns the full structure and key highlights of the Datadog February 2026 Investor Presentation, including top-level financial metrics, customer stats, platform summary, and section index.", "params": [], "data": { "title": "Datadog Investor Presentation – February 2026", "company": "Datadog, Inc.", "presentation_date": "February 2026", "summary": "Datadog is the AI-powered observability and security platform for cloud applications. This presentation covers Datadog's platform overview, market opportunity, financial performance through FY25, FY26 guidance, and long-term goals.", "key_metrics": { "ttm_revenue": "$3.43B", "ttm_yoy_growth": "28%", "ttm_non_gaap_operating_margin": "22%", "ttm_free_cash_flow_margin": "27%", "dollar_based_net_retention_rate": "~120%", "cash_and_marketable_securities": "~$4.5B", "total_customers": "~32,700", "customers_100k_plus_arr": "~4,310", "customers_1m_plus_arr": "603", "employees": "~8,100 in 35 countries", "revenue_cagr_fy20_fy25": "38%" }, "sections": [ { "id": "safe_harbor", "title": "Safe Harbor", "page": 2 }, { "id": "platform_overview", "title": "Datadog Platform Overview", "pages": "3–9" }, { "id": "market_opportunity", "title": "Market Opportunity (Cloud, TAM, Multi-Market)", "pages": "10–12" }, { "id": "industry_recognition", "title": "Industry Recognition (Gartner, Forrester)", "pages": "13–15" }, { "id": "customers", "title": "Customer Scale and Logos", "page": 16 }, { "id": "financial_overview", "title": "Financial Overview", "pages": "17–29" }, { "id": "appendix", "title": "Appendix: Non-GAAP Reconciliation", "pages": "30–33" } ], "industry_recognition": [ "Leader – 2025 Gartner Magic Quadrant for Observability Platforms", "Leader – 2025 Gartner Magic Quadrant for Digital Experience Monitoring", "Leader – The Forrester Wave: AIOps Platforms, Q2 2025" ] } }, { "name": "getSection", "description": "Returns detailed content for a specific section or page of the Datadog Investor Presentation. Use section IDs such as 'platform_overview', 'market_opportunity', 'industry_recognition', 'customers', 'financial_overview', 'growth_drivers', 'guidance', 'long_term_goals', 'capital_allocation', 'share_dilution', or 'appendix'.", "params": [ { "name": "section", "type": "string", "description": "Section ID or name. Options: 'platform_overview', 'market_opportunity', 'industry_recognition', 'customers', 'financial_overview', 'growth_drivers', 'guidance', 'long_term_goals', 'capital_allocation', 'share_dilution', 'appendix'", "required": true } ], "data": { "sections": [ { "id": "platform_overview", "title": "Datadog Platform Overview", "pages": "3–9", "content": "Datadog is the AI-powered observability and security platform for cloud applications. It solves complexity arising from diverse technologies, scale, AI model proliferation, release frequency, and team integration. The platform is unified, simple but not simplistic, deployed everywhere, and used by everyone — breaking down silos. It integrates 1,000+ vendor integrations across 22 categories (DATA FLOWS, NOTIFICATIONS, DATABASES, IOT, NETWORKS, SERVERLESS, USER ACTIVITY, SECURITY, DEV TOOLS, VULNERABILITIES, CONFIGURATION, SAAS, CODE, HARDWARE, PLATFORMS, ML MODELS, ATTACKERS, CONTAINERS, APPS, WORKFLOW, DEPLOYMENT, CLOUDS). Founded in 2010, Datadog has expanded from a Real-time Unified Data Platform to 25 products across 8 major areas: Infrastructure, Applications, Data Observability, Logs, Digital Experience, Security, Software Delivery, and Service Management. AI capabilities include Natural Language Querying, Root Cause Analysis, Anomaly Detection, Impact Analysis, Proactive Alerts, Autonomous Investigations, and Bits AI. Shared Platform Services include Dashboards, CoScreen, Teams, Agent, OpenTelemetry, Notebooks, Software Catalog, IDE Plugins, ChatOps, SLOs, and Case Management.", "product_areas": [ { "area": "Infrastructure", "products": [ "Infrastructure Monitoring", "Metrics", "Container Monitoring", "Serverless", "Cloud Network Monitoring", "Network Device Monitoring", "Cloud Cost Management", "Cloudcraft" ] }, { "area": "Applications", "products": [ "Application Performance Monitoring", "Distributed Tracing", "Continuous Profiler", "Universal Service Monitoring", "LLM Observability", "Database Monitoring", "Data Streams Monitoring" ] }, { "area": "Data Observability", "products": [ "Jobs Monitoring", "Quality Monitoring" ] }, { "area": "Logs", "products": [ "Log Management", "Flex Logs", "Observability Pipelines", "Audit Trail", "Log Forwarding" ] }, { "area": "Digital Experience", "products": [ "Synthetics Testing & Monitoring", "Browser Real User Monitoring", "Mobile Real User Monitoring", "Session Replay", "Product Analytics" ] }, { "area": "Security", "products": [ "Cloud Security", "Code Security", "Cloud SIEM", "Sensitive Data Scanner", "Workload Protection", "App and API Protection" ] }, { "area": "Software Delivery", "products": [ "CI Visibility", "Test Optimization", "Continuous Testing", "Error Tracking", "Feature Flags" ] }, { "area": "Service Management", "products": [ "On-Call", "Incident Management", "Event Management", "Workflow Automation", "App Builder", "Bits AI SRE Agent" ] } ] }, { "id": "market_opportunity", "title": "Market Opportunity", "pages": "10–12", "content": "Cloud spend is growing rapidly from ~$0B in 2010 to an estimated ~$1,425B by 2029E, representing ~20%+ of global IT spend. Datadog's core observability market (Gartner Health & Performance Analytics category) is estimated at $28B in 2026E, growing to ~$38B+ by 2029E. Including Security, Software Delivery, Service Management, and Product Analytics, Datadog's total addressable market approaches ~$190B by 2029E.", "cloud_spend_data": "See getMarketData tool for full table.", "observability_tam_data": "See getMarketData tool for full table.", "multi_market_segments": [ "Observability", "Security", "Software Delivery", "Service Management", "Product Analytics" ] }, { "id": "industry_recognition", "title": "Industry Recognition", "pages": "13–15", "content": "Datadog has been recognized as a Leader in three major analyst reports: (1) 2025 Gartner Magic Quadrant for Observability Platforms (published July 7, 2025) — positioned as a Leader alongside Dynatrace, Grafana Labs, New Relic, and Elastic. (2) 2025 Gartner Magic Quadrant for Digital Experience Monitoring (published October 27, 2025) — positioned as a Leader alongside Dynatrace, New Relic, and Catchpoint. (3) The Forrester Wave: AIOps Platforms, Q2 2025 — positioned as a Leader alongside Dynatrace and ScienceLogic." }, { "id": "customers", "title": "Customer Scale", "page": 16, "content": "Datadog scales across ~32,700 global customers as of December 31, 2025, processing trillions of data points per hour. Customers include both enterprises transforming to cloud (e.g., Kroger, Shell, Porsche Informatik, Hitachi, Nikon, Sotheby's, Nasdaq, NTT Docomo) and cloud-native companies (e.g., DoorDash, Zoom, DraftKings, Plaid, Asana, SoFi, Mercado Libre)." }, { "id": "financial_overview", "title": "Financial Overview", "pages": "17–25", "content": "See getFinancials and getRevenueGrowth tools for detailed financial data." }, { "id": "growth_drivers", "title": "Growth Drivers", "page": 19, "content": "Datadog's five growth drivers are: (1) Secular tailwind of digital transformation and cloud migration; (2) Deployment of GenAI and agentic applications driving cloud usage; (3) Growing and retaining customers; (4) Expanding products/use cases for customers; (5) Adding new markets beyond Observability." }, { "id": "guidance", "title": "Financial Outlook (as of February 10, 2026)", "page": 26, "content": "See getGuidance tool for detailed guidance data." }, { "id": "long_term_goals", "title": "Long-Term Margin Goal", "page": 27, "content": "Datadog's long-term non-GAAP operating margin goal is 25%+, as stated at Investor Day on February 12, 2026. Historical operating margins: 2019: -1%, 2020: 11%, 2021: 16%, 2022: 19%, 2023: 23%, 2024: 25%, 2025: 22%. Free cash flow margins: 2019: 0%, 2020: 14%, 2021: 24%, 2022: 21%, 2023: 28%, 2024: 29%, 2025: 27%." }, { "id": "capital_allocation", "title": "Capital Allocation Goals", "page": 28, "content": "Datadog's capital allocation goals are: (1) Generate healthy amounts of FCF; (2) Ensure leadership has flexibility and capacity to invest; (3) Maintain a thoughtful and disciplined acquisition strategy." }, { "id": "share_dilution", "title": "Share Dilution", "page": 29, "content": "Datadog targets net dilution of 2.5–3.0% related to RSUs/PSUs awarded, defined as % of weighted average shares outstanding granted as equity awards (options, RSUs, PSUs, etc.) during the period, net of forfeitures and cancellations." }, { "id": "appendix", "title": "Appendix: Non-GAAP Reconciliation", "pages": "30–33", "content": "See getFinancials tool for full GAAP to Non-GAAP reconciliation tables covering gross profit margin, operating expenses (R&D, S&M, G&A), and operating income/margin for FY20–FY25." } ] } }, { "name": "getFinancials", "description": "Returns Datadog's historical financial data including GAAP and non-GAAP metrics. Query by metric name (e.g., 'revenue', 'gross_margin', 'operating_margin', 'free_cash_flow', 'r&d', 'sales_marketing', 'g&a') or by fiscal year (e.g., 'FY25', 'FY24'). Also returns full GAAP-to-non-GAAP reconciliation tables.", "params": [ { "name": "metric", "type": "string", "description": "Metric to retrieve. Options: 'revenue', 'gross_margin', 'operating_income', 'operating_margin', 'free_cash_flow', 'free_cash_flow_margin', 'r&d', 'sales_marketing', 'g&a', 'all'. Default: 'all'", "required": false }, { "name": "fiscal_year", "type": "string", "description": "Specific fiscal year to filter by (e.g., 'FY20', 'FY21', 'FY22', 'FY23', 'FY24', 'FY25'). Leave blank for all years.", "required": false } ], "data": { "summary_table": { "description": "Non-GAAP financial summary FY20–FY25", "metrics": [ { "metric": "Revenue ($M)", "FY20": 603, "FY21": 1029, "FY22": 1675, "FY23": 2128, "FY24": 2684, "FY25": 3427 }, { "metric": "YoY Growth (%)", "FY20": "66%", "FY21": "70%", "FY22": "63%", "FY23": "27%", "FY24": "26%", "FY25": "28%" }, { "metric": "Non-GAAP Gross Margin", "FY20": "79%", "FY21": "78%", "FY22": "80%", "FY23": "82%", "FY24": "82%", "FY25": "81%" }, { "metric": "Non-GAAP R&D Margin", "FY20": "29%", "FY21": "30%", "FY22": "30%", "FY23": "30%", "FY24": "28%", "FY25": "30%" }, { "metric": "Non-GAAP S&M Margin", "FY20": "31%", "FY21": "25%", "FY22": "25%", "FY23": "24%", "FY24": "23%", "FY25": "23%" }, { "metric": "Non-GAAP G&A Margin", "FY20": "8%", "FY21": "7%", "FY22": "6%", "FY23": "6%", "FY24": "5%", "FY25": "5%" }, { "metric": "Non-GAAP Operating Income ($M)", "FY20": 64, "FY21": 165, "FY22": 326, "FY23": 490, "FY24": 674, "FY25": 768 }, { "metric": "Non-GAAP Operating Margin", "FY20": "11%", "FY21": "16%", "FY22": "19%", "FY23": "23%", "FY24": "25%", "FY25": "22%" }, { "metric": "Free Cash Flow ($M)", "FY20": 83, "FY21": 251, "FY22": 354, "FY23": 598, "FY24": 775, "FY25": 915 }, { "metric": "Free Cash Flow Margin", "FY20": "14%", "FY21": "24%", "FY22": "21%", "FY23": "28%", "FY24": "29%", "FY25": "27%" } ] }, "gaap_to_non_gaap_reconciliation": { "gross_profit_000s": { "columns": [ "FY20", "FY21", "FY22", "FY23", "FY24", "FY25" ], "revenue": [ 603466, 1028784, 1675100, 2128359, 2684275, 3427158 ], "gaap_gross_profit": [ 473269, 794539, 1328357, 1718451, 2168744, 2740201 ], "gaap_gross_margin": [ "78%", "77%", "79%", "81%", "81%", "80%" ], "add_sbc_in_cogs": [ 1794, 4565, 10827, 17578, 26221, 29729 ], "add_amortization_acquired_intangibles": [ 943, 3792, 6750, 8041, 5642, 5428 ], "add_employer_payroll_taxes": [ 187, 345, 266, 364, 446, 695 ], "non_gaap_gross_profit": [ 476193, 803241, 1346200, 1744434, 2201053, 2776053 ], "non_gaap_gross_margin": [ "79%", "78%", "80%", "82%", "82%", "81%" ] }, "operating_expenses_000s": { "r&d": { "gaap_expense": [ 210626, 419769, 752351, 962447, 1152703, 1548451 ], "gaap_pct_revenue": [ "35%", "41%", "45%", "45%", "43%", "45%" ], "less_sbc": [ 38008, 101942, 237120, 313096, 363301, 469526 ], "less_employer_payroll_taxes": [ 2836, 8143, 10384, 21449, 31134, 40183 ], "non_gaap_expense": [ 172511, 309684, 504847, 627902, 758268, 1038742 ], "non_gaap_pct_revenue": [ "29%", "30%", "30%", "30%", "28%", "30%" ] }, "sales_and_marketing": { "gaap_expense": [ 213660, 299497, 495288, 609276, 756605, 956423 ], "gaap_pct_revenue": [ "35%", "29%", "30%", "29%", "28%", "28%" ], "less_sbc": [ 20467, 35035, 76735, 101937, 122079, 156472 ], "less_amortization_acquired_intangibles": [ 0, 600, 825, 825, 825, 945 ], "less_employer_payroll_taxes": [ 3756, 6349, 2766, 5917, 4694, 5923 ], "non_gaap_expense": [ 189886, 257513, 414962, 500597, 629007, 793083 ], "non_gaap_pct_revenue": [ "31%", "25%", "25%", "24%", "23%", "23%" ] }, "general_and_administrative": { "gaap_expense": [ 62756, 94429, 139413, 180192, 205152, 279700 ], "gaap_pct_revenue": [ "10%", "9%", "8%", "8%", "8%", "8%" ], "less_sbc": [ 14105, 22195, 38472, 49689, 58735, 94944 ], "less_employer_payroll_taxes": [ 839, 1248, 830, 4811, 6852, 6999 ], "less_ma_transaction_costs": [ 0, 0, 0, 0, 0, 1574 ], "non_gaap_expense": [ 50195, 70986, 100111, 125692, 139565, 177757 ], "non_gaap_pct_revenue": [ "8%", "7%", "6%", "6%", "5%", "5%" ] } }, "operating_income_000s": { "gaap_operating_income_loss": [ -13773, -19156, -58695, -33464, 54284, -44373 ], "gaap_operating_margin": [ "-2%", "-2%", "-4%", "-2%", "2%", "-1%" ], "add_sbc": [ 74374, 163737, 363154, 482300, 570336, 750671 ], "add_amortization_acquired_intangibles": [ 943, 4392, 7575, 8866, 6467, 6373 ], "add_employer_payroll_taxes": [ 7618, 16085, 14246, 32541, 43126, 53800 ], "add_ma_transaction_costs": [ 0, 0, 0, 0, 0, 1574 ], "non_gaap_operating_income": [ 63601, 165058, 326280, 490243, 674213, 768046 ], "non_gaap_operating_margin": [ "11%", "16%", "19%", "23%", "25%", "22%" ] } }, "quarterly_revenue": { "description": "Quarterly revenue $M from 1Q23 to 4Q25", "data": [ { "quarter": "1Q23", "revenue_m": 482 }, { "quarter": "2Q23", "revenue_m": 509 }, { "quarter": "3Q23", "revenue_m": 548 }, { "quarter": "4Q23", "revenue_m": 590 }, { "quarter": "1Q24", "revenue_m": 611 }, { "quarter": "2Q24", "revenue_m": 645 }, { "quarter": "3Q24", "revenue_m": 690 }, { "quarter": "4Q24", "revenue_m": 738 }, { "quarter": "1Q25", "revenue_m": 762 }, { "quarter": "2Q25", "revenue_m": 827 }, { "quarter": "3Q25", "revenue_m": 886 }, { "quarter": "4Q25", "revenue_m": 953 } ] } } }, { "name": "getGuidance", "description": "Returns Datadog's forward-looking financial guidance for 1Q26 and FY26, as issued on February 10, 2026. Includes revenue, non-GAAP operating income, non-GAAP EPS, and diluted share count guidance.", "params": [], "data": { "guidance_date": "February 10, 2026", "note": "Forward-looking statements. See Safe Harbor for important assumptions.", "1Q26": { "revenue_range_m": { "low": 951, "high": 961 }, "non_gaap_operating_income_range_m": { "low": 195, "high": 205 }, "non_gaap_eps_range": { "low": 0.49, "high": 0.51 }, "weighted_avg_diluted_shares_approx_m": 367 }, "FY26": { "revenue_range_m": { "low": 4060, "high": 4100 }, "non_gaap_operating_income_range_m": { "low": 840, "high": 880 }, "non_gaap_eps_range": { "low": 2.08, "high": 2.16 }, "weighted_avg_diluted_shares_approx_m": 372 }, "long_term_goals": { "non_gaap_operating_margin_goal": "25%+", "goal_stated_at": "Investor Day, February 12, 2026", "target_net_dilution_rsu_psu": "2.5–3.0% of weighted average shares outstanding" } } }, { "name": "getCustomerMetrics", "description": "Returns Datadog's customer growth, ARR tier breakdown, multi-product adoption rates, and net/gross retention data. Use this to answer questions about customer count, $100K+ or $1M+ ARR customers, product adoption breadth, or retention rates.", "params": [ { "name": "metric_type", "type": "string", "description": "Type of customer metric. Options: 'total_customers', 'arr_tiers', 'multi_product_adoption', 'retention', 'all'. Default: 'all'", "required": false } ], "data": { "total_customers_by_year": [ { "fiscal_year": "FY20", "total_customers": 14200 }, { "fiscal_year": "FY21", "total_customers": 18800 }, { "fiscal_year": "FY22", "total_customers": 23200 }, { "fiscal_year": "FY23", "total_customers": 27300 }, { "fiscal_year": "FY24", "total_customers": 30000 }, { "fiscal_year": "FY25", "total_customers": 32700 } ], "arr_tier_customers": { "as_of": "December 31, 2025", "data": [ { "fiscal_year": "FY20", "customers_1m_plus": 101, "customers_100k_plus": 1253, "pct_total_arr_from_100k_plus": "78%" }, { "fiscal_year": "FY21", "customers_1m_plus": 216, "customers_100k_plus": 2010, "pct_total_arr_from_100k_plus": "83%" }, { "fiscal_year": "FY22", "customers_1m_plus": 317, "customers_100k_plus": 2780, "pct_total_arr_from_100k_plus": "85%" }, { "fiscal_year": "FY23", "customers_1m_plus": 396, "customers_100k_plus": 3190, "pct_total_arr_from_100k_plus": "86%" }, { "fiscal_year": "FY24", "customers_1m_plus": 462, "customers_100k_plus": 3610, "pct_total_arr_from_100k_plus": "88%" }, { "fiscal_year": "FY25", "customers_1m_plus": 603, "customers_100k_plus": 4310, "pct_total_arr_from_100k_plus": "90%" } ] }, "multi_product_adoption": { "description": "Percentage of customers using N or more products", "data": [ { "year": 2020, "2_plus": "72%", "4_plus": "22%", "6_plus": "3%", "8_plus": null, "10_plus": null }, { "year": 2021, "2_plus": "78%", "4_plus": "33%", "6_plus": "10%", "8_plus": "1%", "10_plus": null }, { "year": 2022, "2_plus": "81%", "4_plus": "42%", "6_plus": "18%", "8_plus": "6%", "10_plus": "2%" }, { "year": 2023, "2_plus": "83%", "4_plus": "47%", "6_plus": "22%", "8_plus": "9%", "10_plus": "3%" }, { "year": 2024, "2_plus": "83%", "4_plus": "50%", "6_plus": "26%", "8_plus": "12%", "10_plus": "6%" }, { "year": 2025, "2_plus": "84%", "4_plus": "55%", "6_plus": "33%", "8_plus": "18%", "10_plus": "9%" } ] }, "retention_rates": { "as_of": "December 31, 2025", "dollar_based_ttm_net_retention_rate": "~120%", "dollar_based_gross_retention_rate": "mid-to-high 90%s", "note": "Dollar-based gross retention rate is calculated as the weighted average of trailing 12-month point-in-time gross retention rates. ARR attrition is based on customers who downgraded, requested cancellation, or sent formal termination notice." } } }, { "name": "getMarketData", "description": "Returns Datadog's total addressable market (TAM) data including cloud spend forecasts, observability market size (Gartner Health & Performance Analytics), and multi-market TAM expansion across Observability, Security, Software Delivery, Service Management, and Product Analytics.", "params": [ { "name": "market", "type": "string", "description": "Market segment to retrieve. Options: 'cloud_spend', 'observability_tam', 'multi_market_tam', 'all'. Default: 'all'", "required": false } ], "data": { "cloud_spend": { "source": "Gartner Forecast: Public Cloud Services, Worldwide (multiple updates 2012–2025)", "description": "Global public cloud spend and as % of global IT spend", "data": [ { "year": 2010, "cloud_spend_b": 0, "pct_global_it_spend": "~0%" }, { "year": 2015, "cloud_spend_b": 100, "pct_global_it_spend": "~5%" }, { "year": 2018, "cloud_spend_b": 200, "pct_global_it_spend": "~8%" }, { "year": 2019, "cloud_spend_b": 250, "pct_global_it_spend": "~9%" }, { "year": 2020, "cloud_spend_b": 300, "pct_global_it_spend": "~10%" }, { "year": 2021, "cloud_spend_b": 350, "pct_global_it_spend": "~11%" }, { "year": 2022, "cloud_spend_b": 475, "pct_global_it_spend": "~13%" }, { "year": 2023, "cloud_spend_b": 525, "pct_global_it_spend": "~14%" }, { "year": 2024, "cloud_spend_b": 650, "pct_global_it_spend": "~16%" }, { "year": "2025E", "cloud_spend_b": 800, "pct_global_it_spend": "~18%" }, { "year": "2026E", "cloud_spend_b": 875, "pct_global_it_spend": "~19%" }, { "year": "2027E", "cloud_spend_b": 1050, "pct_global_it_spend": "~20%" }, { "year": "2028E", "cloud_spend_b": 1275, "pct_global_it_spend": "~21%" }, { "year": "2029E", "cloud_spend_b": 1425, "pct_global_it_spend": "~22%" } ] }, "observability_tam": { "source": "Gartner Forecast: Enterprise Infrastructure Software, Worldwide (4Q22–4Q25 Updates) – Health & Performance Analytics category", "description": "Datadog's core observability market TAM", "highlight": "$28B in 2026E", "data": [ { "year": 2020, "market_size_b": 13 }, { "year": 2021, "market_size_b": 15 }, { "year": 2022, "market_size_b": 17 }, { "year": 2023, "market_size_b": 19 }, { "year": 2024, "market_size_b": 22 }, { "year": "2025E", "market_size_b": 25 }, { "year": "2026E", "market_size_b": 28 }, { "year": "2027E", "market_size_b": 31 }, { "year": "2028E", "market_size_b": 35 }, { "year": "2029E", "market_size_b": 38 } ] }, "multi_market_tam": { "source": "Gartner Forecast: Enterprise Infrastructure Software, Enterprise Application Software, and Information Security, Worldwide (multiple updates)", "description": "Total addressable market across all Datadog product areas, approaching ~$190B by 2029E", "segments": [ { "name": "Observability", "products": [ "Infrastructure Monitoring", "APM", "Log Management", "Synthetics", "RUM", "Network Monitoring", "LLM Observability", "AI Agents Console" ] }, { "name": "Security", "products": [ "Cloud Security", "Code Security", "Cloud SIEM", "Data Security", "Bits AI Security Agent" ] }, { "name": "Software Delivery", "products": [ "CI Visibility", "Test Optimization", "Continuous Testing", "Bits AI Dev Agent", "Datadog MCP Server" ] }, { "name": "Service Management", "products": [ "OnCall", "Incident Management", "Workflow Automation", "Bits AI SRE Agent" ] }, { "name": "Product Analytics", "products": [] } ], "total_tam_2029E_approx_b": 190 } } }, { "name": "getProductTimeline", "description": "Returns the history of Datadog's product innovation and launch timeline from 2010 to 2025, including which products were introduced each year. Use this to answer questions about when specific products launched, how the platform evolved, or what was added in a given year.", "params": [ { "name": "year", "type": "string", "description": "Filter by year (e.g., '2020', '2025'). Leave blank to return all years.", "required": false } ], "data": { "timeline": [ { "year": 2010, "products_launched": [ "Real-Time Unified Data Platform" ], "milestone": "Founded Datadog to break down silos" }, { "year": 2012, "products_launched": [ "Infrastructure Monitoring (Hosts, Clouds, VMs, Containers, Processes, IoT)" ] }, { "year": 2017, "products_launched": [ "Application Performance Monitoring (APM)", "Distributed Tracing" ] }, { "year": 2018, "products_launched": [ "Log Management", "Watchdog Alerts" ] }, { "year": 2019, "products_launched": [ "Serverless Monitoring", "Synthetic Monitoring", "Cloud Network Monitoring", "Cloud SIEM" ] }, { "year": 2020, "products_launched": [ "Continuous Profiler", "Incident Management", "Real User Monitoring", "Error Tracking" ] }, { "year": 2021, "products_launched": [ "Sensitive Data Scanner", "Session Replay", "Network Device Monitoring", "Cloud Security", "Database Monitoring", "CI Visibility", "Watchdog Insights" ] }, { "year": 2022, "products_launched": [ "Application Security Threat Management", "Watchdog Log Anomaly Detection", "Watchdog Root Cause Analysis", "Datadog Audit Trail", "Observability Pipelines", "Software Catalog", "Continuous Testing", "Cloud Cost Management", "Universal Service Monitoring" ] }, { "year": 2023, "products_launched": [ "Streamlined APM Onboarding", "RUM Heatmaps", "Software Composition Analysis", "Data Streams Monitoring", "Resource Catalog", "Remote Configuration", "Datadog Teams", "Workflow Automation", "Cloud SIEM Investigator", "Cloud Infrastructure Entitlement Management", "Mobile App Testing", "Cloud Vulnerability Management", "Dynamic Instrumentation" ] }, { "year": 2024, "products_launched": [ "RUM Feature Flag Tracking", "Kubernetes Security Posture Management", "Event Management", "Bits AI for Incident Management", "Error Tracking for Logs", "Case Management", "CSM Agentless Scanning", "Infrastructure-as-Code Remediation", "Flex Logs", "Data Jobs Monitoring", "LLM Observability", "App Builder", "App & API Protection", "Serverless Step Functions", "Monitoring for Oracle Cloud", "Fleet Automation" ] }, { "year": 2025, "products_launched": [ "On-Call", "CCM Cloud Cost Recommendations", "Nested Queries", "Reference Tables", "DORA Metrics", "Code Security", "RUM without Limits", "Product Analytics", "Log Workspaces", "Network Path", "Kubernetes Autoscaling", "Datadog Distribution of OTel Collector", "SIEM Content Anomaly Detection", "Private Action Runner", "Internal Developer Portal", "DDSQL Editor", "LLM Experiments", "Updog.ai", "Cross-Org Visibility", "Custom LLM-as-Judge", "Storage Management", "Secret Scanning", "Bits AI SRE Agent" ] } ], "total_products_as_of_fy25": 25, "platform_ai_capabilities": [ "Natural Language Querying", "Root Cause Analysis", "Anomaly Detection", "Impact Analysis", "Proactive Alerts", "Autonomous Investigations", "Bits AI" ] } } ], "verify": { "manifest_url": "https://febyeji.github.io/feat-surface/verify.json", "how": "Fetch verify.json, find this document by ID, compare SHA-256 hashes of webmcp/*.json files." } }