Observe raised $115 million in a Series B financing announced on March 27, 2024. Sutter Hill Ventures led the round, with participation from Snowflake Ventures, existing investors Madrona and Capital One Ventures. Snowflake’s individual investment was not disclosed. The deal mattered because Observe was built on Snowflake, linking the database company’s data-cloud strategy with the fast-growing observability market.
The story has since moved beyond venture funding: Snowflake announced its intent to acquire Observe in January 2026, and by May 2026 described the business as Observe by Snowflake.
What Observe raised in March 2024
Observe, headquartered in San Mateo, California, announced a $115 million Series B on March 27, 2024. Sutter Hill Ventures led the financing. Snowflake Ventures, Madrona and Capital One Ventures also participated, according to contemporary reporting and financing coverage.
Observe said it planned to use the capital to expand research and development, increase sales and go-to-market capacity, grow its North American presence and continue scaling the business. CEO Jeremy Burton led the company at the time. The company also reported strong fiscal-year growth metrics, including 171% annual recurring-revenue growth and 194% total-contract-value growth; those figures were company-reported rather than independently audited benchmarks. Contemporary reporting identified customers including Topgolf, Reveal, F5, Linedata, AuditBoard and Edgio.
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It is important not to describe this as “Snowflake investing $115 million.” The $115 million was the total round. Snowflake Ventures’ contribution was not publicly disclosed.
Why Snowflake invested
This was a strategic investment, not simply a financial bet on another software startup. Observe was designed to run on Snowflake and use the platform’s separation of storage and compute to handle large volumes of telemetry.
That gave Snowflake several potential advantages:
- A purpose-built observability layer for Snowflake environments.
- Better visibility into applications, data pipelines and Snowflake workloads.
- A way to address monitoring for AI applications, distributed systems and data products.
- A potential route to keep observability data, analysis and governance within the Snowflake ecosystem.
Contemporary reporting said the companies expected Observe to develop dashboards and visualizations for Snowflake environments, including applications running on Snowpark Container Services. Observe’s own explanation of its architecture also emphasized that observability data could benefit from Snowflake’s scalable storage and on-demand compute model. Read the funding coverage and Observe’s explanation of its Snowflake foundation.
What “data cloud observability” means
Observability is the practice of using system outputs to understand what is happening inside applications and infrastructure. Its main signals include:
- Logs: event records and diagnostic messages.
- Metrics: numerical measurements tracked over time.
- Traces: the path of a request across distributed services.
- APM: application performance monitoring.
- Infrastructure and security telemetry: data about hosts, containers, networks and security events.
Observe’s thesis is that these signals should not remain in separate tools. Its platform presents telemetry in a connected data model—described by the company as a Data Graph and later as a Context Graph—so an engineer can move from an alert to related services, traces, logs, infrastructure changes and business context.
This is different from using “data observability” solely to mean data quality, freshness, lineage or schema monitoring. Observe’s scope is broader: application and infrastructure observability, data-pipeline monitoring, Snowflake workload visibility and, increasingly, AI-related observability. These categories overlap, but they are not identical.
How Observe’s architecture differs from legacy monitoring
The company’s approach can be summarized as operating observability like a data platform rather than as a collection of isolated monitoring products.
Unified telemetry storage
Logs, metrics, traces, application data and infrastructure information can be retained and correlated in one environment. That aims to reduce the manual handoffs involved in investigating an incident across separate logging, APM and infrastructure tools.
Storage and compute separation
Snowflake’s architecture separates stored data from the compute used to query it. In theory, this allows a customer to retain substantial volumes of telemetry and run analysis when needed instead of forcing every signal into a fixed, tightly coupled monitoring appliance.
Observe has positioned the platform for high-volume telemetry and long retention. It later said that traces were not downsampled by default and could be retained for 13 months. Those are product claims that may depend on contract, configuration or edition; buyers should confirm the current terms directly.
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OpenTelemetry support
Observe supports OpenTelemetry-based instrumentation, which can reduce reliance on proprietary collection agents and make it easier to standardize how applications emit telemetry. Open standards improve portability, but they do not eliminate commercial or operational dependence on the platform that stores and analyzes the data.
Common investigation model
The practical objective is to let an engineer start with an alert, identify the affected service, inspect related traces and logs, review a deployment or infrastructure change, and connect the technical event to customer or business impact without changing products repeatedly.
The problem Observe is targeting
In a fragmented environment, an incident investigation may require an engineer to:
- Find an alert in one monitoring system.
- Search logs in another.
- Query metrics elsewhere.
- Inspect traces in a separate APM product.
- Manually correlate deployments, infrastructure changes and customer impact.
Every handoff can slow diagnosis and increase the amount of telemetry that must be copied, indexed or retained in different systems. Rising telemetry volumes also make cost a central issue. Companies must balance long retention and forensic value against ingestion, storage and query expense.
Observe argues that a Snowflake-based model can make high-volume retention and analysis more economical. That is a positioning claim, not proof that Observe is always cheaper than Datadog, Splunk or another incumbent. Actual costs depend on ingestion, retained storage, query compute, data movement, retention policy, account configuration, commitments, discounts and support.
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A useful total-cost model is:
total cost = ingestion + retained storage + investigation compute + data transfer + platform commitments + user or feature charges + support.
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| Option | Typical strength | Trade-off relative to Observe’s thesis |
|---|---|---|
| Datadog | Broad commercial suite, mature SaaS workflows and extensive integrations | Multiple products and high telemetry volumes can make costs difficult to forecast |
| Splunk | Deep log analytics, security capabilities and enterprise footprint | Platform complexity and consolidation decisions may be significant |
| New Relic | Application performance monitoring and developer-oriented workflows | Its architecture and packaging differ from a Snowflake-centered data model |
| Grafana ecosystem | Open-source flexibility, integrations and control over the stack | Self-managed deployments can shift scaling and operational work to the buyer |
| Snowflake Trail | Built-in visibility for Snowflake AI, applications, pipelines and infrastructure | May not replace a full third-party observability suite across every environment |
| Observe by Snowflake | Unified telemetry model and Snowflake-native architecture | Greater dependence on Snowflake and potentially complex consumption-cost planning |
Snowflake’s current observability page lists Observe, Datadog and Grafana integrations and separately positions Snowflake Trail as built-in observability. That suggests Snowflake is pursuing a broader ecosystem rather than claiming every customer must use a single tool. See Snowflake’s current observability overview.
Who should evaluate Observe by Snowflake?
Observe is most likely to appeal to organizations that already use Snowflake, generate substantial telemetry, need cross-signal investigation and want technical and business data governed in a related environment. Relevant use cases include distributed applications, Kubernetes, data pipelines, Snowflake queries, Snowpark workloads, Snowpark Container Services, security telemetry and AI or machine-learning applications.
It may be a weaker fit for a company that does not use Snowflake, requires a simple predictable per-host or per-user price, operates across environments where platform neutrality is essential, or wants to avoid additional data-platform administration.
Before selecting it, a buyer should ask:
- How much telemetry is ingested daily and during peak incidents?
- What proportion is logs, metrics and traces?
- How long must raw telemetry be retained?
- Is sampling acceptable for production investigation or compliance?
- What are the expected storage and query-compute costs?
- Can existing Snowflake credits be applied, and under what commercial terms?
- Which cloud regions and data-residency controls are supported?
- How complete are OpenTelemetry propagation and integrations for the current stack?
- Can telemetry be exported in usable formats if the company later changes platforms?
- How much migration work is required from Datadog, Splunk, Grafana or another incumbent?
Risks and failure modes
A unified platform does not remove implementation risk. Customers should test for:
- Unexpected storage growth from retaining raw telemetry.
- Runaway query compute during investigations.
- Duplicate instrumentation and double ingestion.
- High-cardinality labels that increase cost or degrade performance.
- Missing correlation IDs across services.
- Incomplete OpenTelemetry context propagation.
- Snowflake-region or data-governance incompatibilities.
- Difficulty exporting data despite standards-based ingestion.
- Confusing Snowflake-workload monitoring with full-stack visibility across external systems.
- Overestimating AI-generated diagnoses or treating them as automatic remediation.
A proof of concept should measure diagnosis time, query latency, data completeness, retention cost, incident-time compute behavior and migration effort using the buyer’s own telemetry. Free trials or credits are not reliable indicators of production economics.
What happened after the $115 million round?
Observe announced a further financing and its Project Voyager AI-powered observability launch in September 2024, describing that financing as a $145 million Series B. A June 2024 Observe post referred to a $125 million Series B. Because the company’s public references use $115 million, $125 million and $145 million descriptions for 2024 financing events, the figures should not be added together or presented as a reconciled cumulative total without a definitive company or filing-based explanation.
The larger strategic development came on January 8, 2026, when Snowflake announced its intent to acquire Observe. Snowflake said the combination would bring Observe’s AI-powered observability, Context Graph and telemetry architecture into the Snowflake AI Data Cloud. That announcement described the transaction as subject to regulatory and customary closing conditions and did not disclose financial terms. Read Snowflake’s acquisition announcement.
By May 5, 2026, Snowflake referred to the product as Observe by Snowflake and said Observe had joined Snowflake three months earlier. Snowflake also said customers could use existing Snowflake credits toward Observe usage without limitation. Buyers should still confirm current contract terms, regional availability and consumption rules before treating credits as a complete price comparison. See the later product update.
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The investment’s broader meaning
The 2024 financing was significant less because it added another observability vendor than because it showed Snowflake’s interest in making observability part of the data-cloud platform itself. The strategic logic is straightforward: applications, pipelines, AI systems and business data increasingly operate together, so monitoring them may require joining operational telemetry with data and business context.
Whether that becomes a decisive advantage depends on execution, integrations, cost transparency and how well the product serves customers that are not fully standardized on Snowflake. The funding round was the early signal; the later acquisition and Observe by Snowflake positioning are the more important facts for anyone evaluating the market today.
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