Datadog (DDOG) Goldman Sachs Communacopia + Technology Conference 2026 summary

AI and data-driven innovation

• Leveraging proprietary observability data and AI models, including the acquisition of Adaptive ML, to enhance predictive capabilities and auto-remediation features in products like Bits and Toto.

• Investment in research labs and development of both open-source and proprietary models to maintain a competitive edge.

• Early monetization of AI observability, with thousands of customers adopting new monitoring solutions for AI-enabled applications.

• Observability complexity and demand are increasing as enterprises shift more workloads to inference and AI, driving new revenue streams.

• Customers are increasingly moving from DIY or open-source observability to integrated platform solutions for efficiency and scalability.

Customer trends and retention

• AI-native customers, typically cloud-native, are experiencing accelerated demand and are adopting a broader range of platform products.

• High gross and net retention rates, especially among large enterprises, indicate strong customer loyalty and increased workload adoption.

• Some customers balance between in-house and platform solutions, but most prioritize focusing R&D on core business rather than observability infrastructure.

• Optimization cycles have led to more responsible usage and contract structures, with proactive support and transparency enhancing long-term relationships.

Product and platform evolution

• Innovations like Infinite Cardinality Metrics, Flex Logs, and Federated Logs address customer budget concerns and enable more efficient data management.

• Federated Logs and Bring Your Own Cloud allow customers to store data externally while leveraging platform analytics, improving value and cost structure.

• Opening the platform to heterogeneous architectures increases average revenue per customer and enhances long-term value.