By ByteBot
On January 8, Snowflake dropped $1 billion—its largest acquisition ever—to buy Observe, an AI-powered observability platform built on Snowflake from day one. The deal pays a 33% premium over Observe’s July 2025 valuation of $750 million, signaling Snowflake’s aggressive entry into the $50+ billion IT operations management market and putting it on a collision course with Datadog, Dynatrace, and Cisco’s Splunk.
This isn’t another M&A headline. Snowflake is betting that observability—the infrastructure monitoring logs, metrics, and traces across distributed systems—belongs inside the data platform, not as a separate tool. For developers juggling Datadog, Splunk, and Snowflake subscriptions, this could mean tool consolidation and potential cost savings. For the market, it’s vendor lock-in dressed up as convenience.
Why Snowflake Paid $1B for a $750M Company
The 33% premium reveals strategic urgency. Observe isn’t just another observability vendor—it’s built entirely on Snowflake’s data warehouse, making it the only platform that unifies telemetry data (logs, metrics, traces) with business data in a single repository. Traditional observability requires separate infrastructure (Datadog, Dynatrace) that duplicates storage costs and fragments data.
Observe processes 150 million queries daily on Snowflake, ingesting petabytes of data across billions of events. Snowflake Ventures invested in March 2024, meaning this wasn’t a cold acquisition—it was a 2-year partnership building toward integration. Analyst Sanjeev Mohan puts it bluntly: “This is the first time we’re seeing an infrastructure observability vendor acquired by a data company. Observability’s cost problem stems from treating telemetry as special-purpose data requiring specialized infrastructure.”
Observe claims teams can resolve production issues 10x faster using its AI-powered SRE. Citation needed, but the promise is compelling: unified telemetry plus business data means faster debugging without tool-switching. Analyst estimates suggest 60% cost reduction versus traditional observability—if true, that threatens Datadog’s $38 billion empire.
What Observability Actually Means
Observability isn’t monitoring. It’s the three pillars—logs, metrics, traces—that let you understand why a distributed system behaves a certain way, not just that something broke. Logs record events (“User login failed at 10:15 AM”), metrics measure performance (CPU usage, API response time), and traces show request flow across services (“Checkout took 3.2s: 1.5s database, 1.2s payment gateway”).
The observability market is exploding: $28.5 billion in 2025, projected to hit $172.1 billion by 2035 (19.7% CAGR). Microservices, serverless, and AI agents generate exponential telemetry data. Debugging without observability is flying blind—and Snowflake wants to own that market.
Snowflake vs Datadog vs Dynatrace—Who Wins?
Snowflake is entering a market dominated by three giants: Datadog (4.5 stars, 860 Gartner reviews, $38B market cap), Dynatrace (4.6 stars, 1,644 reviews, #1 in Gartner’s Ability to Execute 2025), and Cisco’s Splunk (4.5 stars, 1,207 reviews). The competitive edge? Snowflake customers can consolidate observability plus data storage, while pure-play vendors require separate infrastructure.
However, the market isn’t convinced. Snowflake stock fell 3% on the announcement—investors are skeptical about competing with purpose-built tools. Analyst Sanjeev Mohan’s caution is telling: “Snowflake is charting a new course here, and it remains to be seen how this plays out.” Many teams juggle Splunk for logs, Datadog for metrics, and Dynatrace for APM. Snowflake’s bet is that they’ll trade best-of-breed for platform consolidation.
The strategic question for developers: stick with Datadog’s proven observability or consolidate on Snowflake’s integrated offering? The 60% cost reduction claim is attractive, but vendor lock-in is the elephant in the room. Once telemetry data lives in Snowflake alongside business data, switching becomes painful.
Part of a $1.5B Acquisition Spree
Observe is Snowflake’s fifth acquisition in 2025-2026, totaling over $1.5 billion spent building an “in-database AI” empire. The pattern is clear: Snowflake wants to own the entire AI data stack. Crunchy Data ($250M, June 2025) adds PostgreSQL support for AI agents. TruEra AI (2025) delivers LLM observability for testing and debugging ML models. Datavolo (2025) handles multimodal data pipelines. Neeva (2023) brought generative AI search.
This isn’t an isolated bet—it’s a moat strategy. Once you store data, run AI agents, and monitor everything in Snowflake, switching becomes expensive. For developers, this consolidation could simplify toolchains or trap them in a single vendor’s ecosystem. Pick your poison.
Key Takeaways
- Snowflake’s $1B bet: Observability should be part of the data platform, not a separate tool
- 33% premium signals urgency: Strategic play or panic buying? The market isn’t convinced (3% stock drop)
- Developers face a choice: Tool consolidation (potential 60% savings) vs vendor lock-in
- Market consolidation accelerates: Datadog, Dynatrace face a new threat from unified platforms
- Integration timeline unknown: Regulatory approval pending, pricing model TBD—don’t make platform decisions on vaporware
