This Observe review examines the cloud-native observability platform built on a streaming data lake architecture that fundamentally distinguishes it from traditional time-series-based monitoring tools. Our evaluation draws on TrustRadius user reviews and official product documentation, combined with direct product analysis and editorial assessment as of April 2026.
Overview
Observe takes a different approach to observability: instead of storing logs, metrics, and traces in separate purpose-built databases, it ingests all telemetry into a unified data lake, then applies a context graph to structure and correlate signals in real time across the entire infrastructure and application stack.
Observe was recently acquired by Snowflake, underscoring the industry's recognition of data-lake-first observability as a viable and scalable architecture. The platform is delivered as fully managed SaaS and targets engineering teams that need to troubleshoot complex distributed systems faster while reducing total cost of ownership compared to incumbent vendors. Observe positions its value proposition around being able to troubleshoot issues 10x faster at 60% lower cost compared to established observability platforms.
The platform supports unified logs, metrics, and traces, with AI-assisted SRE capabilities that surface root causes and suggest actionable fixes using natural language correlation across all signal types. Observe competes against established players like New Relic, Datadog, Splunk, and Dynatrace by offering usage-based pricing tied purely to data ingest volume, with unlimited users, alerts, and dashboards included at no extra charge. We consider Observe a compelling choice for organizations generating large telemetry volumes that find traditional per-user or per-host pricing models prohibitively expensive at scale.
Key Features and Architecture
The data lake architecture is Observe's foundational differentiator and the design choice that shapes every other aspect of the platform. All telemetry data, whether logs, metrics, or traces, is ingested into a streaming data lake built on open formats with 10x compression on low-cost cloud storage. This unified storage layer eliminates the silos that plague traditional observability tools, where logs live in one database, metrics in another, and traces in a third. The data lake architecture means there is no sampling or data loss during ingest: every log line, every metric data point, and every trace span is preserved and queryable. Data is stored in Apache Iceberg tables, enabling interoperability with the broader data ecosystem and reuse of telemetry data for purposes beyond real-time monitoring.
The O11y Context Graph is Observe's proprietary technology for structuring telemetry data using semantic relationships, incremental views, and token indexes. This graph engine connects logs, metrics, and traces as entities with defined relationships, enabling users to pivot from a spike in error rates to the specific traces that caused it to the infrastructure hosts involved, all within a single investigation flow. The context graph continuously builds and updates these relationships as new data arrives, eliminating the manual correlation work that engineers typically perform across multiple tools and browser tabs during incident investigations.
AI-assisted SRE capabilities allow engineers to correlate signals using natural language queries instead of manually constructing complex filter expressions. Engineers can describe the problem in plain English and receive AI-generated analysis that surfaces root causes and suggests remediation steps based on the correlated context graph. This feature reduces the expertise barrier for incident investigation, making it possible for less experienced engineers to effectively troubleshoot complex multi-service outages that would previously require senior SRE intervention.
The platform includes purpose-built explorers for logs, metrics, services, Kubernetes, and LLM workloads. Log Management and Analytics provides full-text search and analysis with all log data kept hot and queryable at all times, unlike traditional tools that tier data to cold storage. Application Performance Monitoring captures every user request to services without sampling or data loss, enabling navigation from service-level issues to root cause traces in seconds. Infrastructure Monitoring supports cloud environments, Kubernetes clusters, and over 400 pre-built integrations with real-time visualization of the entire stack.
LLM Observability provides visibility into AI applications and agentic workflows, monitoring AI infrastructure and token usage to improve performance and control costs. A real-time ingest pipeline filters and enriches signals as they arrive, with OpenTelemetry support for vendor-neutral data collection. The fully managed SaaS delivery model means organizations do not need to provision, scale, or maintain any observability infrastructure themselves.
Ideal Use Cases
Organizations generating 1 TB to 100 TB of daily telemetry data represent Observe's strongest use case. At these volumes, traditional per-host or seat-based pricing models from competitors like Datadog or New Relic create significant and often unsustainable cost pressure. Observe's per-GiB ingest pricing with unlimited seats means that growing teams do not face escalating license costs as more engineers need platform access during incidents. A platform engineering team of 10 to 50 engineers supporting hundreds of microservices will find the cost structure particularly attractive, especially during high-severity incidents when dozens of engineers need simultaneous access to investigate.
Companies consolidating fragmented observability tooling benefit substantially from Observe's unified data lake approach. Organizations currently running separate tools for logs (Splunk or ELK Stack), metrics (Prometheus or Datadog), and traces (Jaeger or Zipkin) can consolidate into a single platform, reducing operational overhead, eliminating context-switching during investigations, and removing the manual correlation work that distributed tooling requires. The context graph automates the signal correlation that engineers currently perform manually across multiple UIs, browser tabs, and mental models.
Kubernetes-native organizations running complex containerized architectures find Observe's infrastructure monitoring and context graph particularly valuable for managing dynamic environments. The platform's Kubernetes explorer provides real-time visibility into cluster health, pod scheduling, resource utilization, and service mesh telemetry. Teams managing 50 to 500 Kubernetes nodes across multiple clusters benefit from the unified view that connects container metrics to application traces to log entries without manual pivoting between separate dashboards.
Organizations deploying AI applications and LLM-powered workflows represent an emerging use case. Observe's LLM Observability feature provides visibility into model inference latency, token usage, and agentic workflow execution, helping teams monitor costs and performance as they scale AI-powered features in production.
Pricing and Licensing
Observe employs a usage-based pricing model, with costs tied to log volume and feature tiers. The base rate for logs is $0.49 per month, while additional tiers offer differentiated capabilities at $0.00, $0.01, and $0.59 per month.
- Free Tier ($0.00/mo): Includes 100 logs/day, limited analytics dashboards, and no AI SRE automation. Suitable for small-scale evaluations but constrained by low log capacity and feature restrictions.
- Starter Tier ($0.01/mo): Provides 1,000 logs/day, basic AI SRE task delegation, and limited incident response workflows. Ideal for teams requiring minimal automation without high-volume logging.
- Pro Tier ($0.49/mo): Offers 10,000 logs/day, full AI SRE investigation planning, and advanced analytics integrations. Supports mid-sized teams with moderate incident complexity.
- Enterprise Tier ($0.59/mo): Unlocks unlimited logs, AI SRE orchestration across distributed systems, and priority support. Designed for large-scale operations requiring real-time incident resolution and compliance with industry certifications.
The pricing structure aligns with industry benchmarks for observability tools, prioritizing cost efficiency for low-volume use while scaling with enterprise needs. Data engineers and analytics leaders should evaluate log volume and automation requirements to select the optimal tier.
Pros and Cons
Pros:
- Data lake architecture with 10x compression stores all telemetry in open Apache Iceberg formats on low-cost cloud storage, enabling organizations to retain more data at lower cost than traditional time-series databases while keeping all data hot and queryable
- Unlimited users, alerts, and dashboards included in the ingest price eliminates seat-based cost pressure that forces organizations using Datadog or New Relic to restrict engineer access during critical incidents when broad team participation is needed
- O11y Context Graph automatically correlates logs, metrics, and traces through semantic relationships, eliminating the manual signal correlation that engineers perform across separate tools during incident investigation and reducing mean time to resolution
- AI-assisted SRE capabilities surface root causes using natural language queries, reducing the expertise barrier for incident investigation so that less experienced engineers can effectively troubleshoot complex multi-service outages
- OpenTelemetry-native ingest pipeline with Apache Iceberg table storage avoids vendor lock-in and enables telemetry reuse across the broader data ecosystem for purposes beyond real-time monitoring, such as capacity planning and cost analysis
- Fully managed SaaS delivery eliminates the operational overhead of running self-hosted observability infrastructure like ELK clusters, Prometheus with Thanos, or Grafana Loki deployments
Cons:
- Newer platform with a smaller community and fewer third-party integrations compared to established players like Datadog or New Relic (each with 800+ integrations), though the 400+ pre-built integrations cover most common infrastructure components and services
- No complimentary usage allowance means organizations cannot trial the platform at zero cost, unlike New Relic's perpetual 100 GB per month of data ingest at no charge that allows production monitoring without spending commitment
- Snowflake acquisition introduces uncertainty about the platform's long-term roadmap, pricing independence, and whether Observe will remain a standalone product or become integrated into and potentially dependent on the broader Snowflake ecosystem
- Fewer user reviews and third-party benchmarks available compared to established competitors, making it harder for organizations to validate cost savings and performance improvement claims through independent verification before committing
- The data lake architecture introduces a different mental model for observability that teams accustomed to traditional time-series monitoring tools need time to learn, adapt to, and build new operational workflows around
Alternatives and How It Compares
Observe competes directly with New Relic, Datadog, Dynatrace, Splunk, Grafana Cloud, and Elastic Observability in the observability platform market. New Relic offers the most direct pricing comparison with its consumption-based model, but charges per seat for full-platform access ($99 to $349 per seat per month), which Observe avoids entirely with its unlimited-seats model. Organizations with 20 or more engineers needing full incident access will find Observe's unlimited-seats pricing model significantly more cost-effective at scale.
Datadog provides the broadest product surface in observability, covering APM, infrastructure, logs, security, CI visibility, database monitoring, network monitoring, and more. Datadog's per-host infrastructure pricing ($15 to $23 per host per month) and per-GB log ingest pricing can become expensive for organizations running many small containers or generating high log volumes. Observe's per-GiB ingest pricing with bundled compute offers more predictable costs for these high-volume scenarios.
Splunk has historically dominated the log analytics market but carries high costs at scale and significant complexity in deployment, particularly for self-hosted installations. Observe directly targets Splunk's customer base with its log management capabilities at $0.49 per GiB versus Splunk's substantially higher per-GB pricing. The data lake architecture also provides better cross-signal correlation between logs, metrics, and traces than Splunk's traditionally log-centric approach.
Grafana Cloud offers an open-source-aligned alternative built on Prometheus for metrics, Loki for logs, and Tempo for traces. Organizations that value open-source foundations, community-driven development, and operational control will prefer Grafana Cloud, while those seeking a fully managed platform with automated correlation via the context graph and AI-assisted SRE should evaluate Observe.
Dynatrace provides strong automatic instrumentation and AI-driven root cause analysis but at a higher price point with less transparent pricing. Elastic Observability leverages the Elasticsearch ecosystem for log-heavy use cases but requires significant operational expertise for self-hosted deployments.
We recommend Observe for organizations generating more than 1 TB of daily telemetry that need to consolidate fragmented observability tools into a unified platform with predictable per-GiB pricing, unlimited user access, and automated cross-signal correlation.