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Datadog, Inc.

DDOG

10 stories

$99B Market cap · 2026-10-21

@convequity

Convequity on X

As AI agents take over more software work, a SaaS vendor's future comes down to how much of the interface, workflow and data layers it keeps. Ahead of this quarter's software earnings, we will watch how far management is willing to change products and business models to keep that work, because strong defenses today can erode if a company refuses to change. Our SaaS Survival Scorecard evaluates the structural resilience to expanding agentic work, vendor adaptiveness, and what the market is pricing in. Route 3: customers keep working in the vendor's interface, workflows and data as agents do more of the tasks. Route 2: customers move to an agent interface such as ChatGPT or Claude but still rely on the vendor's workflows and data. At the weakest end, only the data layer remains. Route 1: customers replace the vendor, using AI to build and maintain all three layers themselves. Our view: leading security platforms like $PANW have the stronger Route 3 case, since analysts investigate inside them. Datadog ($DDOG) faces more Route 2 risk as developers work inside coding agents and call on it from there. Deeper dive available at Convequity.
@BrokenMoats 4 clicks

Broken Moats on X

Quickly back to very attractive levels to put back on short exposure on the software sector after the manic move in Q3 and digestion in September before the sudden crash up in the past 10 days..... AI wins software is in trouble - faster disruption more competition, pricing pressure AI trade is over, software is going to catch down as the market will tank and this won't sit that out (the are not cheap) Disruption and pricing is the tailwind, now valuation is too. Rates don't hurt either as up until recently these were considered long duration assets Recent results were status quo for most (no real acceleration) and pitch AI cohort growth as growing xx% but its not uplifting the total growth they are just changing what they charge for to show the street what it wants to hear) -- that phase is over, valuations are in many cases at year and multi year highs. SBC still a problem. Now comes renewals in the back of this year and thats where the issues will start to emerge on retention and pricing. An thats day 1 they will snowball overtime for the vast majority of these companies. Plenty of names to choose from: $PAYC $DOCU $TEAM $DDOG $PCTY $GDDY $RNG
@convequity 1 click

Convequity on X

Software multiples have fallen further since our January report. As of 7 Oct 2026, 238 companies in our all-software watchlist have EV/Sales (NTM) data on Koyfin. The median is 3.1x, down from 3.7x in January. The average is 5.4x, down from 6.2x. The 25th percentile is 1.8x, down from 1.9x. The 75th percentile is 5.5x, down from 6.1x. 191 of the 238 (80%) trade below our 6.7x private equity floor. 113 trade below 3x. The Bessemer Cloud Index shows the same pattern. Its median is 4.7x, down from 5.1x in January. 46 of its 68 companies (68%) trade below 6.7x. The cheapest are $CXM at 0.9x, $SPT at 1.1x, $MNDY at 1.7x and $WIX at 1.8x. In our watchlist, only 21 companies trade above 10x. The most expensive are $PLTR at 44.5x, $CRWD at 42.4x, $NET at 38.6x, $PANW at 24.2x, $DDOG at 19.5x and $SNOW at 16.3x. Growth still earns a higher multiple. The 44 names expected to grow revenue 20%+ a year over 3 years have a median of 5.2x. The 85 names expected to grow under 10% have a median of 2.6x. Even for the faster growers, the 5.2x median is below the floor. The market is paying up for very few names. We think that is right. Many software companies have slowing growth, high costs and no clear AI strategy. We would not buy the sector, or a cloud index, on valuation alone. In software, we focus on the few companies rebuilding their products around AI, and our SaaS Survival Scorecard tests key SaaS names against the pressure from AI agents.
@convequity

Convequity on X

$PANW and $DDOG both put something real inside the customer's systems that an AI lab cannot recreate by calling SaaS APIs. Palo Alto's install is still harder to replace with AI. 1. Datadog's agents mostly sit outside the traffic and watch production. A bad agent version rarely takes the business down the way a bad firewall rule can. 2. Palo Alto's firewalls and SASE cloud gateways sit inside the traffic and decide what to allow, deny, decrypt or inspect. A wrong allow or deny hits live traffic and live machines. 3. Many Datadog users start the day in a coding agent and open Datadog when something looks wrong. Many Palo Alto users start in its console and stay there through alerts, triage and response. For them, an outside AI agent is an add-on. Cybersecurity work splits two ways on AI risk. Work done before code ships ("shift-left") or from outside the traffic, such as scanning code, checking cloud settings and sorting collected alerts, gives answers that are easy to check. AI agents are already taking over more of it. Work in live traffic and on live machines ("shift-right") means judging whether an attacker is still inside and whether blocking them is safe. That is more ambiguous and mistakes cost more, so enterprises move slower and demand earned trust. Palo Alto is not risk-free. In our view its exposure is the easily checked detection and vulnerability work inside its platform, which AI agents can take over sooner. The threat is not an AI-native firewall startup.
@convequity

Convequity on X

Exploring SaaS survival in the agentic era — $PANW vs $DDOG. Both sit on critical telemetry. Both expose MCP. On paper they look alike. They aren’t. Model labs have a real reason to build their own observability collectors. Coding agents write and ship code; to get better they need production feedback. Own the host telemetry, close the loop. That same incentive is much weaker for cybersecurity endpoint agents. Finding beacons and writing better code are different jobs. Building a Falcon- or Cortex-class sensor estate is years of trust, labeled attacks, and compliance — not a natural side quest for a model lab. Then there’s the screen. Datadog doesn’t own where developers live. They already live in Cursor / Claude Code / Copilot. So those agents pull Datadog in over MCP. The data plane stays sticky; the UI gets shared. Palo Alto usually does own where the SOC lives. The shift starts in the security console — cases, playbooks, response. In a PANW shop, Cortex is the main workplace. Claude can sit on top in a multi-vendor stack. That’s the risk but not the base case. Same idea — agents need the pipes. What’s different is who tries to own the collector, and who already owns the desk. Thoughts welcome. Full Part 3 write-up for Convequity subscribers is on its way.
@negligible_cap

Negligible Capital on X

*PALO ALTO'S ARORA EXPLORED ACQUIRING DATADOG: INFORMATION $DDOG currently a $80B company "Palo Alto Networks CEO Nikesh Arora has never shied away from deals, acquiring more than 40 companies in the eight years since he took the helm of the world’s biggest standalone cybersecurity firm. But over the past 18 months, Arora’s appetite for acquisitions has reached a new fever pitch as he expanded the company’s products to handle new AI threats as well as capture more revenue from existing customers. For instance, he explored acquiring Datadog, which sells software that helps businesses identify security threats and monitor how cloud applications perform, and Okta, which helps companies ensure workers have access to the right systems, according to two people with knowledge of the situation."
@convequity

Convequity on X

Agentic AI is changing the question for every SaaS name. The old one was “how big is the market?” The new one: when agents do the work, does the vendor get bypassed, survive as plumbing, or own the stack? We built a scorecard for that split — and ran the first three deep dives: • $PLTR — Route 3 ownership bet (ontology / multi−domain ops). Structure elite; valuation still has to earn it • $DDOG — Route 2 plumbing agents need. Host telemetry is hard to absorb casually; coding agents + first-party collect is the watch item. Multiple roughly fair • $NET — purest agent-volume rails. Unabsorbable network footprint; physics caps the model layer; ~40x prices in a lot Condensed public version below. Full Part 2 + series for Convequity subscribers.

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