Observe Inc.
Collaborative observability investigation platform acquired by Snowflake to deliver AI-powered observability at enterprise scale
Observe Inc. is built on Snowflake's data platform — enabling organizations to store unlimited observability data at data-warehouse economics and query it with sub-second latency, collapsing the cost barrier that forces engineers to choose between telemetry retention and investigative depth.
SWOT Analysis
- Snowflake-native architecture delivers long-term retention at 10x lower cost than time-series observability stores
- Correlated datasets link metrics, logs, traces, and events in a unified investigation workspace
- OPAL (Observe Processing and Analysis Language) enables flexible data transformation pipelines
- No data sampling — full fidelity telemetry at Snowflake storage economics
- Vendor-agnostic data model accepts OpenTelemetry, Prometheus, and any custom format
- Snowflake's enterprise customer base provides natural distribution channel
- Security analytics extension co-locating observability and security telemetry on Snowflake
- FinOps-adjacent use case as observability retention becomes a managed cost optimization
- OpenTelemetry ecosystem growth creating standard telemetry pipelines into Observe
- Snowflake dependency means cost and architecture tied to customer's Snowflake contract
- Query latency for complex investigations can be higher than in-memory time-series databases
- Brand recognition very limited vs. Datadog, Elastic, or Honeycomb
- OPAL learning curve for data transformation is steeper than out-of-the-box dashboards
- Chronosphere and Honeycomb competing in cloud-native observability with similar cost philosophies
- Snowflake building native observability analytics reducing the need for Observe layer
- Grafana Cloud and Elastic offering similar long-retention observability at competitive prices
- Budget pressure at data-savvy organizations building custom Snowflake observability pipelines
User Sentiment
Synthesized from G2, Gartner Peer Insights, and analyst review data.
- Long-term retention without sampling — investigations reach back 90 days without extra cost
- Correlated dataset view links metrics, logs, and traces without manual pivot between tools
- Snowflake economics make previously unaffordable telemetry retention feasible
- OPAL pipeline flexibility enables custom data enrichment and filtering at ingestion time
- OPAL pipeline authoring has a steep learning curve for teams expecting turnkey dashboards
- Limited out-of-the-box detection content vs. Datadog or New Relic
- Brand recognition challenges in enterprise evaluations — unfamiliar to many procurement teams
Pricing & TCO
Analyst-synthesized pricing signals — directional only, contact vendor for current terms.
Typical ACV (Mid-Enterprise)
$75K–$400K
Market Segments
Deployment
Key Cost Drivers
- Snowflake compute credits consumed by Observe queries
- Telemetry data volume stored in Snowflake under the Observe data model
- OPAL pipeline complexity and processing compute usage
Observe's cost is additive to existing Snowflake contracts — the Snowflake economics make long-term retention significantly cheaper than dedicated observability stores, delivering strong ROI for high-retention use cases.
Full comparisonCustomer Profile
Typical segments
Typical buyer
Platform Engineer or Staff SRE at a data-driven organization already using Snowflake
- 1Long-term observability data retention at data-warehouse economics replacing expensive TSDB
- 2Correlated investigation combining metrics, logs, and traces for complex distributed system debugging
- 3Security + observability unified on Snowflake for cross-functional data engineering teams
Future Focus Areas
AI investigation assistant leveraging long-term data for pattern-based anomaly correlation
Expanded security analytics co-located with observability telemetry on Snowflake
Native OpenTelemetry collector integration for frictionless telemetry pipeline setup
Snowflake Data Cloud marketplace distribution expanding reach through Snowflake's partner ecosystem