Datadog's business model is a consumption-based SaaS architecture that combines the retention advantages of subscription contracts with the revenue upside of usage-based pricing — a structure that has proven particularly well-suited to the observability market, where customers' infrastructure scale and monitoring needs grow in direct proportion to their cloud investment and application complexity. The fundamental unit of Datadog's commercial model is the host — a server, container instance, or cloud resource that has the Datadog agent installed and is actively sending data to the platform. Customers are billed based on the number of hosts they monitor, the volume of custom metrics they send, the volume of logs they ingest and index, the number of application performance monitoring spans they trace, and the volume of data processed by each additional product they use. This consumption architecture means that Datadog's revenue from any given customer grows automatically as that customer's infrastructure scales — a company that starts on Datadog with 100 hosts and grows to 1,000 hosts over two years will have increased its Datadog spend tenfold without any additional sales intervention. The customer journey typically begins with the free tier or a trial account initiated by an individual engineer or small team. The Datadog agent — the software component installed on each monitored host that collects and forwards metrics, traces, and logs — is open-source and trivially easy to install, with support for every major operating environment including Linux, Windows, macOS, Docker, Kubernetes, and all major cloud providers' managed services. This zero-friction adoption path allows Datadog to establish a presence within enterprise accounts before any formal procurement process occurs, generating usage data that demonstrates value and creates organizational dependency before the question of budget is raised. The enterprise sales motion engages after self-service adoption has established a foothold. Datadog's account executives approach customers who have grown beyond the free tier or who have reached usage levels that would benefit from volume pricing negotiation, proposing annual commitment contracts that lock in minimum spend levels in exchange for discounted pricing. Enterprise customers with large infrastructure footprints and multiple product usage are offered enterprise license agreements that cover multiple Datadog products at negotiated rates, providing pricing certainty for the customer and revenue predictability for Datadog. The multi-product expansion model is the most commercially important driver of Datadog's revenue growth. The company tracks the percentage of its customers using various numbers of products — and the data demonstrates a clear relationship between product count and contract size. Customers using only one Datadog product generate significantly lower average revenue per customer than customers using four or more products, and the expansion from one to multiple products drives a step-change in the total spend that customers make on the platform. Datadog actively designs its product portfolio and pricing to incentivize multi-product adoption: customers who purchase infrastructure monitoring are offered application performance monitoring at discounted rates that make adding the second product economically compelling relative to maintaining a separate APM vendor, and the same logic extends through logs, security, synthetic monitoring, and each subsequent product. The security product line represents the most strategically significant recent expansion of the business model. Datadog Security — encompassing Cloud Security Management, Application Security Management, and Cloud SIEM (security information and event management) — addresses a market that has historically been served by separate vendors (Palo Alto Networks, CrowdStrike, Splunk) with significantly higher revenue per customer than observability tools. The rationale for security convergence with observability is compelling: security investigations require the same infrastructure, application, and log data that observability monitoring collects, and correlating security events with application behavior and infrastructure changes requires the same unified data model that Datadog built for operational monitoring. By extending the platform into security, Datadog can increase its total addressable market and average revenue per customer simultaneously — and customers who already have Datadog agents deployed across their infrastructure can activate security capabilities without additional agent installation overhead.