Cloud software has been a lesson in patience. When customers optimize spend, the best platforms do not necessarily lose relevance, they just lose momentum.
This setup is about what happens when optimization ends and usage becomes growth again, because that is when the market typically stops arguing about valuation and starts paying up for durability.
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What Just Happened
Datadog (NASDAQ: DDOG) is trading around $116, down modestly over the past year but well below prior highs.
The stock is in an awkward spot: still widely viewed as a gold-standard observability platform, yet priced like investors want proof that usage and seat expansion are re-accelerating.
Recent analyst activity captures the mood. A few firms have trimmed price targets after a strong run earlier in the cycle, but most keep positive ratings.
One cut that stood out was Cantor Fitzgerald, which lowered its target while maintaining an overweight stance. Scotiabank also reduced its target but continued to frame Datadog as a top-tier, cloud-native standard, with AI adoption as a meaningful tailwind.
The message is consistent: the platform looks strong, but the stock is being valued with less generosity until the growth cadence looks cleaner.
The other important piece is positioning. Datadog is one of those companies that can look expensive when growth decelerates, then look cheap in hindsight when usage rebounds.
That is why this is a February 24 setup: you either get a narrative shift back toward usage-driven acceleration, or you get confirmation that the recovery is slower and the multiple should stay contained.
What Datadog Actually Does
Datadog sells observability and increasingly security as a unified platform.
At the simplest level, it helps companies answer three questions in real time:
Is my system working?
If not, where did it break?
How do I fix it quickly and prevent the next incident?
That sounds basic until you remember the modern stack is chaos: cloud infrastructure, containers, microservices, APIs, databases, third-party services, and now AI workloads. Datadog’s value is that it pulls signals from across that stack into one place.
A few core components matter for this setup:
A single dashboard for multiple signals
Infrastructure monitoring, application performance, logs, and other telemetry land in one platform. That consolidation is not just convenience. It is budget logic. If IT departments are trying to reduce vendor sprawl, a broader platform often wins.
Watchdog and automation
Datadog’s AI engine, Watchdog, is designed to detect anomalies, alert teams, and help with root-cause analysis. In practice, anything that reduces mean-time-to-resolution becomes valuable when systems are complex and downtime is expensive.
Usage-based economics
This is the part investors should not ignore. Datadog’s business tends to scale with customer usage. When customers run more workloads, ship more logs, and monitor more services, Datadog often benefits.
That makes the stock more cyclical than people assume. Not cyclical like manufacturing, but cyclical like cloud activity and workload intensity.
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Why The Stock Has Been Stuck In Neutral
Datadog can be a great company and still be a frustrating stock when the market is in an optimization phase. A few reasons:
1) Usage compression looks like growth deceleration
When customers optimize cloud spend, they often do it by cutting waste: fewer instances, fewer redundant services, tighter log retention. That can slow usage-driven revenue growth even if Datadog is still embedded.
This is the key nuance: optimization hurts the slope, not necessarily the stickiness.
2) Large deal cycles can get longer
As budgets tighten, approvals take longer and expansions get staged. Observability is important, but procurement still behaves like procurement.
3) The multiple is the battleground
You shared a valuation snapshot that shows Datadog can look rich on traditional metrics. Even if the company is executing, the market can refuse to pay up until growth re-accelerates.
So the stock can move sideways even while the platform continues to compound customers, products, and relevance.
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The Bull Case
1) Consolidation is still a structural tailwind
Morgan Stanley’s framing is sensible: IT departments prefer platforms that reduce tool sprawl. Datadog’s expanding product suite makes it easier to consolidate monitoring into fewer vendors.
If consolidation continues, Datadog can gain share even in a slower macro. Share gain plus a macro recovery is when these stocks tend to rip.
2) AI workloads are a real usage tailwind
AI does not just increase compute. It often increases monitoring intensity: more services, more telemetry, more performance tuning, and higher consequences when systems fail.
Datadog has introduced monitoring tools geared toward AI applications, and the platform is positioned to benefit when AI-driven workloads become normal rather than experimental.
3) Third-party validation still matters
You cited Gartner and Forrester consistently placing Datadog as a leader across observability, digital experience monitoring, and AI for IT operations. Whether you love analyst firms or not, enterprise buyers do use these frameworks as a permission slip.
Leadership positioning helps the sales motion when budgets loosen.
4) The rebound can happen before the headlines turn
Usage-based businesses often inflect when customer optimization slows. The market tends to re-rate before the financial statements look perfect. If management commentary signals that optimization is fading and expansions are returning, the stock can move quickly.
5) Street targets imply a wide upside band
Even after target trims, you shared that the median target implies meaningful upside from current levels. That matters less as a price predictor and more as a sentiment indicator: the Street still expects a normalization in growth and multiple over time.
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The Bear Case
1) Optimization can last longer than you want
If customers remain cautious and keep tuning down usage, Datadog’s growth can stay muted. In that scenario, the stock can remain range-bound while investors wait for a clear re-acceleration.
2) Security expansion is not automatic
Datadog’s platform is broadening into security, which can deepen account penetration, but security deal cycles can be more complex and more competitive. If security growth is choppy, the market may discount the expansion narrative.
3) Valuation is still sensitive to macro
High-multiple software tends to get hit when rates rise or when risk appetite fades. Even with solid execution, DDOG can be pulled around by macro-driven multiple compression.
4) Expectation risk around AI
AI is a real tailwind, but it is also a narrative that can get priced in too early. If AI monitoring demand grows slower than investors expect, the stock can get punished for normal adoption curves.
What I’d Watch Next
If you want to keep this practical, focus on a short checklist that ties directly to the usage rebound thesis:
Usage indicators: commentary on log volumes, workload expansion, and customer optimization behavior
Large customer expansions: are big customers adding more modules and scaling spend, or staying cautious
AI monitoring traction: not hype, but measurable adoption signals and attach rates
Platform consolidation: evidence that customers are replacing point tools with Datadog modules
Guidance tone: are they guiding like a company defending growth, or like a company seeing demand normalize
My Take
This is a classic platform setup where the business quality is not really in dispute, but the market wants a cleaner growth slope before it pays a premium multiple again.
Datadog’s best trait is also its near-term risk: usage-based economics. When workloads scale, the model works in your favor. When customers optimize, the same model can look sluggish.
The February 24 edition is essentially a bet on whether the optimization chapter is ending.
If management signals that usage is stabilizing and expansions are returning, the stock has room to re-rate because sentiment is still cautious despite strong platform positioning.
If optimization persists, you can still own a great platform, but you should expect a slower, more grinding path.
Action Recap
✅ What this is: a leading observability platform with usage-driven upside when workloads scale
✅ What to watch: usage trends, expansion behavior, AI monitoring traction, guidance tone
⚠️ Main risks: prolonged optimization, valuation sensitivity, security expansion friction
🧭 Mindset: re-acceleration setup, not a straight-line story
That’s all for today. Thank you for reading. If you have any feedback, please reply to this email.
Best Regards,
— Adam Garcia
Elite Trade Club
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