Commercial Account Executive

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.

Resilient demand meets a higher-rate test. Cloud growth has the cleaner evidence; consumer demand still has to prove it can carry the thesis. Friday's tape offered a useful test of the market's growth narrative. The S&P 500 proxy SPY rose 0.85% and the Nasdaq 100 proxy QQQ rose 0.87% at the 16:00 ET close, while the small-cap proxy IWM gained 0.41%.[[1]] That was a constructive session, but not a clean all-clear: the macro backdrop still contains a higher-rate hurdle, and the companies in this scope are exposed to very different versions of "resilient demand." The thesis is plausible - but not one trade. The working hypothesis is that earnings growth and durable demand can support DDOG, SNOW, RH, WSM, ETH, LZB, LESL and TPX over the next year. The evidence so far is strongest where demand is recurring, measurable and tied to operating usage. It is less settled where demand depends on housing turnover, financing costs or discretionary big-ticket purchases. A better framing is a three-part test: | Segment | What supports the thesis | What could break it | | Cloud and observability: DDOG, SNOW | Usage, customer expansion and AI-related workloads can compound revenue | Optimization, concentration, or slower consumption can compress growth | | Home and lifestyle: RH, WSM, LZB, LESL, TPX | Brand strength, new collections and eventual housing normalization | High mortgage rates, weak sentiment, promotions and balance-sheet pressure | | ETH | A market proxy for digital-asset risk appetite and liquidity | Volatility, liquidity reversals and limited direct comparability with operating companies | The table is a research framework, not a ranking. The symbols do not share the same business model, accounting drivers or risk profile. Software has the cleaner operating evidence. Datadog's Q2 2026 release reported revenue of $1.12 billion, up 36% year over year, and about 4,720 customers with at least $100,000 in annual recurring revenue, versus about 3,850 a year earlier.[[2]] That is the kind of evidence that can support the resilient-demand case: growth is visible in both the income statement and customer distribution. But even DDOG's transcript summary carried an important qualification: full-year guidance implied roughly 30% revenue growth, with conservatism related to usage reduction from its largest customer.[[2]] In usage-based software, a large customer's optimization is not a footnote; it is a reminder that strong aggregate growth can coexist with lumpy consumption. Snowflake belongs in the same conversation, but not as a duplicate of Datadog. The relevant question is whether data workloads and AI projects translate into sustained consumption rather than temporary experimentation. The September 2026 filing search result confirms a recent material event for SNOW, but the source available in this pass did not provide enough verified operating detail to make a precise growth claim.[[2]] That is a coverage limit, not evidence for or against the thesis. Consumer demand is the harder confirmation. The consumer-facing names require a different standard. A strong brand or new product launch can help, but the market ultimately needs to see traffic, comparable sales, margin discipline and cash conversion hold together while rates remain restrictive. RH illustrates the tension. A current earnings summary reported fiscal Q2 revenue of $922.2 million, up 2.6% year over year, with results above the high end of company guidance.[[3]] Yet another current report described the stock as falling despite a raised outlook and highlighted roughly $5 billion of debt as a continuing concern.[[3]] The lesson is not that guidance is irrelevant; it is that operating improvement may need to outrun financing and valuation concerns before the equity fully responds. WSM was the strongest mover among the clearly current consumer names in this scope, gaining 1.11% at the 16:00 ET close. RH was essentially flat on the day, while LZB fell 0.71%.[[1]] Those one-day moves do not establish a trend, but the dispersion is informative: the market is not treating every "resilient consumer" story as interchangeable. The quote feed also contains a data-quality warning. TPX returned a last quote dated February 26, 2025 rather than a current September 2026 print, so it should not be used for a current relative-performance conclusion. LESL's extended print was $0.5198 at 19:30 ET, up 3.55% versus its 16:00 ET close, while its regular-session close was $0.502.[[1]] The difference between a close and an after-hours print matters, particularly for a low-priced security. Macro: supportive growth, uncomfortable rates. The latest macro snapshot, through August 2026, showed 4.1% unemployment, 2.1% real GDP growth, 3.3% CPI inflation and a 3.63% federal-funds rate. The 10-year Treasury yield was 4.83%, the yield curve was positively sloped by 0.40 percentage points, and high-yield credit spreads were 2.71%.[[4]] That is not a recessionary snapshot, and tight credit spreads are consistent with continued risk tolerance. But a 4.83% 10-year yield is still a meaningful discount-rate test for long-duration software and a financing headwind for housing-linked discretionary demand. Friday's news flow sharpened that tension. Reuters reported that August consumer prices accelerated as gasoline costs rebounded, while the market still saw stocks and bonds rally during the session.[[5]] Separate reporting attributed the equity rebound in part to easing oil prices and renewed enthusiasm around strong AI-infrastructure demand.[[3]] In other words, investors appeared willing to look through a hotter inflation impulse when the growth and energy signals improved - but that tolerance is conditional. What would confirm or weaken the hypothesis? Evidence that would confirm it: * DDOG and SNOW show sustained customer expansion and usage growth, not only AI-project headlines. * RH, WSM and the furniture names maintain comparable-sales momentum without relying excessively on promotions. * Margins and cash flow improve alongside revenue, especially where debt or freight costs matter. * The 10-year yield stabilizes or falls without a corresponding deterioration in earnings expectations. Evidence that would weaken it: * Cloud customers broadly optimize workloads, causing consumption growth to decelerate faster than reported seat or customer counts. * Housing-sensitive demand remains postponed as high financing costs suppress large purchases. * Inflation re-accelerates through energy and services, forcing rates higher while consumer sentiment remains fragile. The macro snapshot put sentiment at 55.2, down 10.53% year over year despite a monthly rebound.[[4]] * Companies deliver revenue growth but miss on margin, free cash flow or balance-sheet targets. What to watch next. * The next software reports: separate recurring workload growth from one-time AI enthusiasm and monitor large-customer concentration. * Comparable sales and inventory: for WSM, RH, LZB, LESL and TPX, watch whether demand is organic, promotional or delayed. * Rates and oil together: the most important macro combination may be a higher 10-year yield plus renewed energy inflation, not either variable in isolation. * Market breadth beneath the indexes: Friday's index gains were constructive, but the thesis needs participation beyond a narrow group of large growth winners. * Data freshness: TPX requires a refreshed quote before any current performance comparison; ETH should be treated as a digital-asset risk indicator rather than an operating-company peer. The base case is neither "growth wins" nor "rates win." It is that recurring software demand has the clearest path to validating the hypothesis, while consumer and housing-linked names need more evidence that resilient demand can survive a still-expensive cost of capital.

What is it like to work in Datadog's AI Research Lab? Datadog Chief Scientist Ameet Talwalkar explains how the AI Research Lab solves complex AI problems and turns research into production systems. Published on Sep. 09, 2026 Credit: Datadog What makes working at Datadog's AI Research Lab unique? According to Chief Scientist Ameet Talwalkar, who leads the AI Research Lab, it's the fact that his team at the cloud-based observability and security platform is building real things to solve real problems. "We're doing cutting-edge AI, but focused on real translational impact and working with engineering and product teams to actually get our research over the wall and into production," Talwalkar said in a Datadog video. What Is Datadog's AI Research Lab? Datadog's AI Research Lab develops AI models, benchmarks and agent capabil. Datadog's AI Research Lab develops AI models, benchmarks and agent capabilities designed to advance observability and solve real-world production problems. What does Datadog's AI Research Lab work on? ARFBench, which stands for "Anomaly Reasoning Framework Benchmark," is a time series question-answering benchmark released by Datadog in April 2026. Built from Datadog's own production telemetry and internal incidents, the open benchmark gives researchers a real-world way to evaluate AI systems designed for observability and incident response. What does Datadog do? Datadog is a monitoring and security platform for cloud applications. Engineers responding to outages need to interpret complex time-series data to answer questions like: When did this anomaly start? Which metrics changed? What could be causing this problem? With ARFBench, incident-response agents with these reasoning capabilities can be tested to see if they're good at these tasks. "These time series question-answering tasks are essential for engineers, and present challenging and necessary tasks for SRE models and agents to perform," Talwalkar and his co-authors wrote in a blog post. Ultimately, ARFBench will help engineers build and evaluate AI that helps engineers diagnose incidents faster, reduce the time to resolution and make AI-assisted observability more reliable. But for Datadog's AI Research Lab, the release of ARFBench was just the beginning. In May, Talwalkar and his peers announced Toto 2.0, a family of open-weights time-series foundation models ranging from 4 million to 2.5 billion parameters. Toto 2.0, whose models improve predictability as they scale, brings Datadog closer to AI-powered observability that can predict system behavior - not just detect problems after they occur. And according to Datadog's blog, Toto 2.0's largest versions now lead the benchmarks Datadog tested for observability and general-purpose forecasting. "Toto 2.0 is the first model family for which simply making the model bigger reliably makes it better," Talwalkar said in a LinkedIn post. With Toto 2.0's release, Datadog's AI Research Lab showed that it can build AI models for more than just observability - it can also compete with broad, state-of-the-art forecasting models. "Our longer-term goal is to develop a full-fledged world model for observability, extending to all telemetry types, unlocking capabilities such as proactive incident detection, root cause analysis, counterfactual analysis, simulation and agent training," Talwalkar and his co-authors said in a Datadog blog post about Toto 2.0's release. ARFBench and Toto 2.0 show that Datadog's AI Research Lab isn't just adding AI features to observability; the team is building AI that could understand the state and behavior of increasingly complex software systems. For AI research scientists and engineers, working at Datadog means contributing to foundational AI research and working on direct applications to real production systems. "The things that you're building are the direct models or the direct agents that are going into production," Talwalkar said in a video about building Datadog's AI Research Lab. "What sets Datadog apart and what gets me really excited is that we're building real things to solve real problems. I'm very excited to see where we go." What is it like to work in AI Research at Datadog? AI researchers and engineers at Datadog work on foundational AI problems with applications to real production systems, collaborating with engineering and product teams to move research into production. If helping invent AI that determines what next-generation observability looks like sounds like your kind of challenge, check out Datadog's open roles. Frequently asked questions. Datadog is a monitoring and security platform for cloud applications. What is Datadog's AI Research Lab? Datadog's AI Research Lab develops AI models, benchmarks and agent capabilities designed to advance observability and solve real-world production problems. The team works with engineering and product teams to move research into production systems. What is ARFBench? ARFBench, short for Anomaly Reasoning Framework Benchmark, is a time-series question-answering benchmark built from Datadog's production telemetry and internal incidents. It helps researchers evaluate AI systems designed to reason about observability and incident-response tasks such as identifying when anomalies begin, which metrics changed and what may be causing a problem. What is Toto 2.0? Toto 2.0 is a family of open-weights time-series foundation models ranging from 4 million to 2.5 billion parameters. The models are designed to improve forecasting as they scale and move Datadog closer to AI-powered observability that can predict system behavior, not just detect problems after they occur.