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Observe

Observe by Snowflake is an AI-powered observability platform for logs, metrics and traces. It keeps telemetry on a Snowflake lakehouse, links signals through its Observability Context Graph and adds an AI SRE for incident investigation.

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Type
Observability Platform

By Egor Burlakov · Edited · Source dates below

Buying snapshot

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Decision pointWhat Observe states

Data storage

Telemetry Lakehouse Foundation built on Snowflake, with compute-storage separation; data stays hot, so there is no archiving, storage tiering or rehydrating cold data

Snowflake account

Available standalone; no separate Snowflake purchase is required. Customers can apply Snowflake credits toward Observe usage.

Data collection

Metrics across cloud, Kubernetes, containers and serverless from 400+ pre-built integrations; logs in structured, semi-structured or unstructured formats

What you get with Observe

Unlimited users, alerts, dashboards and data sources; data encryption in transit and at rest; customer success and technical support

Free trial

Sign up at account.observeinc.com; no credit card required, provisioned immediately

Our take

Observe fits SRE and DevOps teams that want logs, metrics and traces in one place and can predict their raw telemetry volume well enough to commit to it. If you cannot forecast ingest volume, expect to spend time on capacity sizing with the vendor before you sign.

Evaluate Observe

Popular comparisons

See all 7 Observe comparisons

Where it stands out

  • Signals arrive already linked. The Observability Context Graph maps relationships across logs, metrics and traces, and extends to code and business context. Engineers move from traces to correlated logs, metrics and infrastructure data instead of pivoting between disconnected dashboards.

  • AI SRE does investigation work. It investigates incidents, finds root causes and suggests fixes from natural-language questions, using context from the Context Graph. You can use it in the Observe UI or from Claude or Cursor through the CLI or MCP Server.

  • Open formats reduce lock-in. Observe ingests OpenTelemetry natively, so existing OTel instrumentation and collectors work. It supports Apache Iceberg, so stored data stays readable by any Iceberg-compatible engine.

  • Full traces, no log tiering. Observe keeps every trace without sampling or data loss and lets you search logs without worrying about indexing, data tiering or retention limits. It also generates unlimited log-derived metrics at no extra cost.

  • AI application coverage is on the way. Agent Observability is marked as coming soon. It is described as tracing AI requests from prompt to response across multi-step agentic workflows and analyzing token consumption.

Product screenshot

Observe product interface
Product UI source

Trade-offs to weigh

  • Size the commitment on raw data. The subscription counts uncompressed telemetry ingested, so plan the commitment on raw volume, not on what ends up in storage.

  • Long log history is its own cost line. Observe markets log search without retention limits, but the Logs rate comes with 30-day retention; keeping data longer is priced as retention per GiB per month.

  • Consolidation savings are the vendor's claim. Observe says one platform for all three signal types can often remove the need for several observability tools. Check that against the tools and volumes you run today.

Pricing and plan conditions

Observe sells a subscription priced on a committed volume of uncompressed telemetry ingested. If you go over the committed ingestion volume, Observe will never send an overage bill; it will work with you to right-size capacity. Multi-year and volume-based discounts are available through a custom quote. These rates are for Observe by Snowflake; Snowflake's own pricing is on snowflake.com/pricing.

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Observe by Snowflake published usage rates

SignalPublished priceWhat it includes

Logs

Starting at $0.49/GiB

Compute included, unlimited users, 30-day retention

Metrics

Starting at $0.008/DPM

Compute included, unlimited users, 13-month retention

Traces

Starting at $0.59/GiB

Compute included, unlimited users, 30-day retention

Retention

$0.01 / GiB per month

Unlimited retention

Public signals

About these signals

Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.

8 PyPI weekly downloads0 Hacker News matching stories, 90d0 vulnerabilities across 1 package

See all signals from 3 sources
Source
Signals
Last updated
PyPI
Weekly downloads:8↑3
November 9, 2026
Hacker News
Matching stories, 90d:0
November 9, 2026
OSV
Package vulnerabilities:0 vulnerabilitiesacross 1 package

PyPI · observe-http-sender@1.3.3

November 9, 2026

Alternatives to Observe

The reviewed substitutes for Observe among the observability platforms, and what would make each one the better answer.

Direct alternatives

Reviewed substitutes: products bought for the same job, where a team picks one.

Dynatrace
Observe and Dynatrace both ingest traces, metrics and logs, alert on them and support root-cause analysis. Observe stores all telemetry in a data lake linked by a context graph and focuses on lower cost at high volume, while Dynatrace combines monitoring with AI-driven analysis, automation and application security in one platform.Applies to: Choosing the observability backend that will receive OpenTelemetry data and carry on-call.
Grafana Cloud
Observe and Grafana Cloud are managed observability services for metrics, logs and traces. Grafana Cloud assembles open-source tools behind Grafana dashboards, while Observe is a proprietary platform that keeps all telemetry in one data lake with a context graph to speed up correlation.Applies to: Choosing between these two for the apm observability decision.
AppDynamics
Observe and AppDynamics both help teams troubleshoot applications by connecting signals across the stack. Observe stores logs, metrics and traces in a data lake with a context graph for fast search and correlation, while AppDynamics is Cisco's APM platform built around business transaction tracing.Applies to: Choosing between these two for the apm observability decision.
Better Stack
Observe and Better Stack each offer an AI SRE that helps engineers find the cause of incidents faster. Observe is built on a data lake and context graph for searching and correlating large volumes of telemetry, while Better Stack bundles logs, traces, on-call, incident management and status pages in one service.Applies to: Choosing between these two for the apm observability decision.
Uptrace
Observe and Uptrace both keep large amounts of telemetry at low cost so teams can search and correlate it. Uptrace uses OpenTelemetry and ClickHouse and can be self-hosted, while Observe is a commercial cloud platform built on a data lake with a context graph and an AI SRE.Applies to: Choosing between these two for the apm observability decision.

Other approaches

A different approach to the same problem. Each substitutes only for the workload named beside it.

Elastic Observability
Elastic Observability is chosen instead of Observe for open-source-based observability programs that need published monthly plan options. **Elastic Observability** is an open-source-based observability offering focused on storing more telemetry, troubleshooting faster, and applying agentic AI to operational data.Applies to: For teams that need an observability platform organized around open-source foundations and published plan tiers, we recommend Elastic Observability over Observe.
Splunk
Splunk and Observe both collect, search and alert on logs and monitoring data, and teams move those workloads from one to the other. Splunk indexes machine data into its own searchable repository; Observe stores logs, metrics and traces in a streaming data lake and competes on lower cost at high volume.Applies to: Storing, searching and alerting on logs and monitoring data. Splunk stands in where existing Splunk searches and dashboards decide; Observe stands in where telemetry volume makes storage cost the deciding factor.
Datadog
Datadog is an alternative to Observe for cloud-scale infrastructure, application, and log monitoring workloads. **Datadog** is a cloud-scale monitoring and observability platform for infrastructure, applications, and logs, designed for IT, development, and operations teams running applications at scale.
New Relic
New Relic is preferred over Observe for application performance management workloads requiring code-level diagnostics across cloud, datacenter, or hybrid environments. **New Relic** is an AI-powered observability platform that correlates telemetry across an organization’s stack to isolate root causes and reduce MTTR.
Explore all Observe alternatives →

Sources