Mid Market Account Executive

PayMongo taps Datadog to boost payments reliability. Thu, 24th Sep 2026 (Today) PayMongo has adopted Datadog's platform to unify observability and security across its payments operation, helping the Southeast Asian payments company cut incident response times. The Philippines-based company says the shift has enabled it to resolve 75 per cent of incidents before they affect merchants. Its mean time to restore has fallen from hours to minutes after bringing monitoring, alerting and security workflows onto one system. The move addresses a core operational challenge for the fintech group as it scales in a regulated sector. Engineers had been working across separate tools, while security coverage varied across the organisation, making it harder to trace problems quickly across the payments stack. PayMongo runs its business entirely in the cloud and processes billions of dollars in monthly payment volume. It provides businesses with services to accept payments, access capital and manage finances through a single platform, serving tens of thousands of merchants across Southeast Asia. The unified setup gives teams visibility across the full payments flow and lets them correlate application, infrastructure and security signals more quickly. It has also changed how PayMongo handles outages involving payment partners: the system can detect disruptions in near real time, notify merchants and automatically pause affected payment methods. These measures are intended to limit failed checkouts and reduce abandoned carts, a key metric for merchants that depend on continuous payment acceptance. In payments, even brief interruptions can immediately affect sales and customer trust, particularly during peak shopping periods. Jose Dalino, Chief Product and Technology Officer at PayMongo, said transaction reliability had become a defining issue for the company as volumes increased. "Every second of downtime means real money lost for the tens of thousands of businesses that rely on us to collect payments from their customers. Datadog was chosen to bring observability and security together in one place, addressing engineering teams' need for unified visibility and for reliability to scale alongside the business," Dalino said. Operational shift Beyond incident response, the consolidation has changed how PayMongo's engineering and security teams work together. By placing observability and security data on one platform, staff can view affected services and transactions in the same workflow when a threat or failure emerges. This shared view is particularly important as the company's attack surface expands with more merchants, more payment methods and a larger engineering organisation. In a payments business, that combination can increase operational complexity as well as exposure to cyber threats. Datadog's cloud security tools combine signals from endpoint protection, identity systems and network activity with application and infrastructure data. According to PayMongo, this has improved how quickly teams can move from detection to investigation and then to remediation. The shift also extends into software development. Automated code scanning now helps identify vulnerabilities before they reach production, a growing concern as AI-generated code becomes more common in development workflows. Dalino said the combined view of system performance and security had altered internal processes. "Having security and observability on the same platform has fundamentally changed how our teams work. We can detect, investigate, and respond to threats faster because we have the full picture in one place," Dalino said. He also linked the changes to the company's merchant relationships and its role during periods of heavy transaction demand. "Through our usage of Datadog, our customers have increased their confidence in us. We've become their partner of choice for major sales events where near-zero downtime is non-negotiable. Reliability is what earns trust in payments. Datadog gives us the visibility to deliver on that promise - at any scale, at any time," Dalino said. Regional growth PayMongo operates in a market where digital payments infrastructure has become increasingly important to small and mid-sized businesses seeking to sell online and manage cash flow through a single provider. As fintech groups broaden their services in Southeast Asia, reliability and security are becoming more important competitive factors alongside pricing and product range. For cloud-based payment platforms, the need to monitor the full transaction path has also grown as companies rely on multiple third-party providers and APIs. An outage at one partner can quickly affect checkout completion rates if merchants and customers are not alerted fast enough. Datadog said PayMongo's use of a single observability and security system shows how financial technology groups are trying to simplify operations while handling larger transaction volumes. Adrian Towsey, Vice President of Commercial Sales for Asia-Pacific and Japan at Datadog, said payments companies faced little tolerance for failure. "As a financial operating system in a highly regulated sector, uptime, reliability, and security are non-negotiable. By unifying observability and security, PayMongo gains the visibility and control needed to scale with confidence. This allows it to maintain merchant trust, while driving broader business adoption of digital infrastructure, and redefining how businesses across the region access financial power," Towsey said.

Datadog brings AI to both ends of the user journey with Journey Monitoring and Bits Testing. Datadog | Published 24 Sept 2026 The capabilities combine to give teams a complete and vital view of the user journey - from pre-release testing to production monitoring Datadog, Inc. (NASDAQ: DDOG), the leading AI-powered observability and security platform, today showcased new features of its Digital Experience Monitoring (DEM) suite: Journey Monitoring and Bits Testing. As part of the DEM suite, these features help teams monitor whether users can complete business-critical flows like checkout, sign-in and onboarding, and verify those flows keep working as their applications change. Organisations today struggle to answer two questions in their user journeys: "Did the user succeed in their journey?" and "Does the user journey work consistently, even as the application changes?" With Journey Monitoring and Bits Testing, Datadog is helping teams answer those questions. Journey Monitoring puts conversion, traffic and availability for each journey into a single view so teams can instantly see when and why a conversion dropped, and know if the journey was successful. Bits Testing builds coverage from a plain-language prompt and adds goal-based testing - a Synthetic test type that verifies an outcome instead of replaying a script - so tests survive redesigns and automatically cover AI features whose output changes each run to ensure that user journeys still work even as the product changes underneath them. "We don't think of AI as a feature you add to a product. We think of it as what should be running underneath everything - infrastructure, security and now the user journey. Journey Monitoring and Bits Testing apply that idea end-to-end to find the journeys that matter and use AI to keep testing them as the product changes," said Yanbing Li, Chief Product Officer at Datadog. Journey Monitoring gives teams: * One hub for every critical user journey with data from Real User Monitoring, Synthetic Monitoring and Product Analytics available in a single view, so engineering and product teams see the same picture. * Ongoing analysis of real user traffic to detect the high-volume paths users take to the same outcome, so critical flows are ready to monitor before anyone files a support ticket. * Color-coded health indicators and SLO breach badges to see what's at risk before investigating. Because journeys are interconnected, the journey map shows how an issue in one journey can affect those upstream and downstream of it. Bits Testing gives teams: * Coverage that scripted tests structurally can't provide. Goal-based testing verifies the outcome itself, not a script, to discover high-value paths to a plain-language goal and flags exactly where code breaks. Because it rediscovers paths at runtime, it adapts automatically as a product changes, making it especially suited to non-deterministic AI features. * AI-native test creation as Bits Testing turns any plain-language prompt into a complete test suite spanning browser, API, network and goal-based tests, so coverage keeps pace with every release without a line of code. "Datadog provides visibility and operational control as AI creates complexity. We do so from two directions: agentic capabilities like Bits Testing that continuously test journeys, and those like Journey Monitoring that autonomously discover and observe the most critical customer paths," said Li. Datadog is showcasing these new capabilities ahead of its presence at Datadog Summit San Francisco (Sept 23) and Tech Week Singapore (Sept 29-30), where Datadog executives will be on-site and available for media briefings.

How CubeAPM plans to take on Datadog and New Relic in the AI observability race. CubeAPM is building an observability platform to challenge global players such as Datadog and New Relic with a lower-cost, self-hosted architecture. As enterprises generate more telemetry and face rising monitoring costs, CubeAPM is betting that predictable pricing and infrastructure efficiency will make enterprise switch to its platform compelling. Founded by Trainman's Vineet Chirania and former BharatPe CTO Vijay Aggarwal, CubeAPM serves 50 enterprise customers, is clocking $1.5 Mn in ARR and has remained profitable since inception. When Vineet Chirania was scaling his train-ticket booking startup, Trainman, a decade ago, he encountered a problem that would eventually inspire his next venture. His team relied on Datadog, an application monitoring tool, to track the performance of its software systems. Then one day, the startup received an unexpectedly high bill after one of its developers started feeding additional custom metrics into Datadog. Over at BharatPe, senior engineering executive Vijay Aggarwal was facing a similar challenge with another observability giant, New Relic.