SNOW vs DDOG: Snowflake vs Datadog — Two Ways to Play the Cloud Data Wave
Datadog has been the stronger stock recently — its observability business grows directly with cloud infrastructure expansion and AI model deployments. Snowflake got hit by enterprise cloud optimization spending in 2022-2024 but has a compelling AI data platform story that could drive the next growth leg. Both are consumption-based businesses with exceptional customer retention. The question is which growth driver is more durable from current levels.
Datadog's AI Observability Bet Is Paying Off Faster
As companies deploy AI models at scale, those models need monitoring — performance tracking, error detection, latency measurement, cost optimization. Datadog extended its observability platform to cover AI model monitoring, and it's growing fast. Every company deploying AI in production becomes a potential Datadog customer for LLM observability. This is a direct, immediate revenue driver tied to the AI deployment wave.
Datadog's platform consolidation story is also compelling. Companies running cloud infrastructure want one platform for infrastructure monitoring, application performance management, security, and now AI observability. Datadog integrates all of these. The more products a customer uses, the stickier the relationship. Customers using 4+ Datadog products have 130%+ net revenue retention — they expand spending faster than they churn.
Business Comparison
- Cloud observability: infra, APM, security, logs
- LLM/AI model monitoring — direct AI tailwind
- 25+ integrated products on one platform
- ~10,000+ customers, 130%+ NRR for multi-product users
- Growing 25%+ YoY with improving free cash flow margins
- Cloud data warehouse / data lake platform
- AI data cloud: stores training data, vector embeddings
- Cortex AI: run ML models on Snowflake data
- Multi-cloud (AWS + Azure + GCP in one platform)
- Recovering from cloud cost optimization headwinds
Snowflake's AI Data Cloud Could Trigger the Next Consumption Wave
Snowflake's consumption business was hurt when enterprises aggressively optimized cloud spending. But AI data workloads are fundamentally different from traditional analytics workloads — they don't get optimized away. Training data, inference logs, vector embeddings, and RAG pipelines all generate large, persistent data that needs to live somewhere. Snowflake's bet is that its Data Cloud becomes the preferred home for AI data at enterprise scale.
Cortex AI, Snowflake's managed ML platform, allows companies to run AI models directly on their Snowflake data without moving it. This reduces latency, improves security, and creates more consumption on Snowflake's platform. If every AI workflow generates Snowflake compute consumption, the optimization headwinds reverse and become a new growth tailwind. That's the bull case — and quarterly consumption trends are the data point to watch.
Who Should Buy Which
Technical Signals — What to Watch
- DDOG RSI: Datadog's RSI has been more controlled — it trends between 45 and 70 in bull phases. RSI dips below 40 on market selloffs (not company-specific news) have historically been good medium-term entries.
- SNOW volatility: Snowflake is more volatile around earnings because its consumption model means quarterly numbers are harder to predict. Large earnings gaps in both directions happen regularly. Position sizing matters more with SNOW.
- DDOG key metric: Customer count with 4+ products (tracks platform consolidation) and large customer adds ($1M+ ARR). AI observability is still a small revenue line but growing fast.
- SNOW key metric: Product revenue growth rate (ex-professional services) and the trend in customers with $1M+ spend. Any reacceleration above 25% growth would be a significant positive signal.
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