Observe by Snowflake Reviews & Product Details

What I like about Observe is the potential to make troubleshooting easier while keeping costs down. The AI capabilities are interesting, but I think the real value is how quickly teams can identify and resolve issues without having to jump between multiple tools. Of course, that depends on how well it performs in practice. Review collected by and hosted on G2.com.

One thing I don't particularly like about Observe is the learning curve. I understand that observability is inherently complex, but if you're building a tool that's supposed to make troubleshooting easier, why does it take so much effort to get comfortable using it? The whole point should be to reduce complexity, not introduce another layer of it. I shouldn't need to spend hours understanding how the platform organizes and connects data before I can start getting meaningful answers. AI is great sure, but I question how useful they really are if users still need significant technical knowledge to validate what the AI is telling them. For me, a good observability platform shouldn't just be powerful; it should make complicated information easier to understand and act on. Otherwise, we're just moving the complexity from one place to another and calling it innovation. Review collected by and hosted on G2.com.

It makes it easier to turn large amounts of data into clear, actionable insights. I’ve used it before to explore and analyze data, and I particularly liked how intuitive it was to navigate through information, identify patterns, and quickly get to the root of an issue. It helped me spend less time searching through data and more time understanding what was actually happening. Review collected by and hosted on G2.com.

Some workflows and configurations can take time to understand. I also found that navigating larger or more complex datasets could sometimes feel less straightforward than expected. Review collected by and hosted on G2.com.

Observe by Snowflake is an AI-powered observability platform built natively on the Snowflake AI Data Cloud to unify logs, metrics, and traces.

Key Capabilities

• AI SRE: Automates troubleshooting, root-cause analysis, and agentic workflows using natural language via the UI, CLI, and MCP Server.

• Observability Context Graph: Maps service semantics and relationships to reason about complex distributed systems and AI agents. Review collected by and hosted on G2.com.

While Observe by Snowflake provides cost savings and deep data integration, users and industry analysis highlight several drawbacks, limitations, and growing pains with the platform:

1. High Tech Debt and Internal Scaling Pains

Following its acquisition by Snowflake, the platform has faced standard post-acquisition integration hurdles. According to employee reviews on Blind, internal scaling has led to significant technical debt and legacy design issues that engineering teams are actively trying to resolve.

2. Rigid UX Focused Strictly on Observability

A common critique revolves around the scope of its interface. On Reddit, opinions are mixed regarding its core architecture. Review collected by and hosted on G2.com.

The clean interface and having logs, metrics, and traces in one place. Queries stay fast on large data volumes, and the AI-assisted troubleshooting surfaces likely causes quickly, so we spend less time digging during incidents. Review collected by and hosted on G2.com.

The learning curve for its query language can be steep at first, and some integrations take extra setup to get the data shaped the way you want. Pricing can also be hard to predict as data volumes grow, so it takes some monitoring to keep costs in check. Review collected by and hosted on G2.com.

Instead of forcing you to hunt through disconnected silos of logs, metrics, and traces, Observe links them into a relational model. It maps technical telemetry to actual business entities like shopping carts, user sessions, or customer accounts. This turns troubleshooting from a frustrating text-search problem into an instant graph-traversal exercise Review collected by and hosted on G2.com.

While the UI relies heavily on a streamlined visual layout, users note that unlocking the platform's advanced workflows, deep customizations, and unique metrics still requires writing SQL and manual data engineering. Teams transitioning from traditional, point-and-click tools like Datadog often face a steep learning curve to grasp how to manually format and tune the platform's dataset models. Review collected by and hosted on G2.com.

To start, Observe's support team is far beyond any other vendor that I have worked with to date, which includes other top-ranked APM/Logging/Monitoring solutions on G2. They make communication easy; they are very responsive and dedicated to ensuring you are successful with the product.

Observe treats all your custom/bespoke data coming in from arbitrary data sources or 3rd party tooling the same as other more common data sources such as Kubernetes or AWS. All of your data sources and resources feel just as integrated and native to Observe as any other. If you send over data they've never seen, you aren't handicapped until they support it, and you don't need to submit a feature request to have them integrate your data source in a way that feels native.

Within Observe, you can easily model your resources across the various platforms you use. Those platforms and resources can span across EC2 instances, Kubernetes Pods, Terraform runs, CI/CD Builds, JIRA tickets, GitHub commits, and anything else relevant to you. You can then create links/connections between all of these resources and their event streams/logs.

Imagine a scenario where you see an access denied exception when accessing AWS resources in your application logs. From your app logs, you can jump to the Kubernetes Pod for that service and see the IAM Role attached to the pod. From the IAM Role, you review the attached IAM Policy and the CloudTrail events associated with that role to see more details on the failed request and what permissions you need to add to the policy. You can further investigate the change history of the policy, jumping to the Terraform run that last updated the policy, the GitHub commit that triggered the Terraform run, and the JIRA story associated with the code change.

You can genuinely connect everything in Observe, not just logs and not just your typical platforms, and do so in a way that represents your architecture and workflows. Review collected by and hosted on G2.com.

My only potential dislike is that they aren't a full-blown APM (yet?). However, as mentioned above, they provide a lot of value beyond what an APM can provide. The foundation of the product seems so solid and flexible that I would not be surprised if they make good progress closing that gap, enough to allow us to dump our APM and solely use Observe. Review collected by and hosted on G2.com.

After setting up simple collectors, you now have access to all of your data. You don't need to truncate or parse your logs until they get to the server. You can easily create worksheets and parse your data out. Their team is super knowledgeable and always able to help. Review collected by and hosted on G2.com.

The custom language takes a little getting used to, but you can also use right-click utils to do most. Review collected by and hosted on G2.com.