Observe

Observe is a cloud-native observability platform built on a streaming data lake architecture that combines logs, metrics, traces, and AI-powered troubleshooting in a single system. In this Observe review, we examine how the platform's O11y Context Graph and AI SRE capabilities deliver faster incident resolution at lower cost compared to traditional observability tools like Datadog, Splunk, and New Relic.

Overview

Observe was founded in 2017 by former Splunk engineers and is headquartered in San Francisco. The platform takes a fundamentally different approach to observability: instead of separate databases for logs, metrics, and traces, Observe stores all telemetry data in an open data lake using Apache Iceberg tables on low-cost cloud storage (S3, GCS). A semantic layer called the O11y Context Graph structures this data into entities and relationships, enabling cross-signal correlation without the cost of maintaining multiple specialized databases.

The company claims customers achieve up to 60% lower observability costs, 3x faster mean time to resolution (MTTR), and 10x data compression compared to traditional platforms. Observe is delivered as fully managed SaaS with a free tier available for evaluation. The platform supports OpenTelemetry for data collection, avoiding vendor lock-in on the ingestion side.

Key Features and Architecture

O11y Data Lake

All telemetry — logs, metrics, traces, and events — is stored in Apache Iceberg tables on cloud object storage with 10x compression. This architecture decouples storage from compute, allowing elastic scaling of query capacity independently from data volume. Unlike Datadog or Splunk where storage costs scale linearly with data volume, Observe's object storage backend keeps costs low even at petabyte scale.

O11y Context Graph

The Context Graph structures raw telemetry into entities (services, hosts, containers, pods) with semantic relationships between them. Incremental views and token indexes enable sub-second search across billions of log lines. When investigating an incident, users can pivot from a failing service to its underlying pods, host metrics, and related traces without writing complex queries.

AI SRE

Observe's AI SRE agent uses natural language to help engineers investigate incidents. Users describe symptoms in plain English, and the AI correlates signals across logs, metrics, and traces to surface probable root causes with suggested fixes. This reduces the expertise required to troubleshoot complex distributed systems and accelerates MTTR for on-call engineers.

Log Management and Analytics

All log data is kept hot (queryable) without tiering to cold storage, eliminating the rehydration delays common in Splunk and Elasticsearch-based systems. Users can search and analyze logs at full resolution without sampling, with costs claimed to be a fraction of traditional log management platforms.

Application Performance Monitoring (APM)

Observe captures every user request without sampling or data loss, providing full-fidelity distributed traces. Users can navigate from service-level latency dashboards to individual trace spans to identify bottlenecks. The APM integrates with the Context Graph, so traces are automatically linked to the infrastructure they run on.

Infrastructure Monitoring

The platform collects metrics from cloud providers (AWS, GCP, Azure), Kubernetes clusters, and 400+ pre-built integrations. Real-time dashboards visualize the entire infrastructure stack, and anomaly detection alerts on metric deviations before they impact users.

LLM Observability

A newer capability for monitoring AI applications and agentic workflows. Teams can track token usage, latency, error rates, and costs across LLM-powered services — addressing the growing need for observability in generative AI deployments.

Ideal Use Cases

High-Scale SaaS Companies

Organizations generating terabytes of telemetry daily benefit most from Observe's data lake economics. Companies paying $500,000+/year for Datadog or Splunk can potentially cut costs by 60% while maintaining or improving query performance and incident resolution speed.

Kubernetes-Native Teams

Teams running hundreds of microservices on Kubernetes use Observe's Context Graph to automatically map relationships between services, pods, nodes, and namespaces. The Kubernetes Explorer provides a purpose-built interface for navigating cluster health and troubleshooting pod-level issues.

Organizations Consolidating Observability Tools

Companies running separate tools for logs (Splunk), metrics (Prometheus/Grafana), and traces (Jaeger) can consolidate into Observe's unified platform. This eliminates the operational overhead of maintaining multiple systems and enables cross-signal correlation that siloed tools cannot provide.

AI/ML Platform Teams

Teams deploying LLM-powered applications use Observe's LLM Observability to monitor model performance, token costs, and agentic workflow reliability — a use case that traditional observability platforms are only beginning to address.

Pricing and Licensing

Observe offers a free tier and usage-based pricing. Based on publicly available information:

For context, observability platform pricing benchmarks: Datadog Log Management costs $0.10/GB ingested + $1.70/million log events/month, Splunk Cloud starts at ~$150/GB/day indexed, and New Relic charges $0.35/GB ingested above the free 100GB/month. Observe's data lake architecture is designed to be significantly cheaper at high volumes due to object storage economics and 10x compression.

A mid-sized SaaS company ingesting 500GB/day of logs, metrics, and traces might spend $50,000–$100,000/year on Observe versus $150,000–$300,000/year on Datadog or Splunk for equivalent data volumes.

Pros and Cons

Pros

• Up to 60% lower cost — data lake on object storage with 10x compression dramatically reduces storage costs at scale
• All data always hot — no cold storage tiers or rehydration delays; every log line is queryable in real time
• AI SRE for natural language troubleshooting — reduces MTTR by surfacing root causes and suggested fixes through conversational investigation
• OpenTelemetry-native — avoids vendor lock-in on data collection; telemetry stored in open Iceberg format for reuse
• Unified platform — logs, metrics, traces, infrastructure, APM, and LLM observability in one system eliminates tool sprawl
• 400+ pre-built integrations — covers AWS, GCP, Azure, Kubernetes, databases, and common application frameworks

Cons

• Smaller market presence — less established than Datadog, Splunk, or New Relic; fewer community resources, tutorials, and third-party integrations
• Enterprise pricing opacity — while a free tier exists, detailed pricing for Professional and Enterprise tiers requires sales engagement
• Newer platform — fewer years of production hardening compared to Splunk (20+ years) or Datadog (10+ years); potential edge cases at extreme scale
• Learning curve for data lake concepts — teams accustomed to traditional log search (Splunk SPL, Elasticsearch KQL) may need time to adapt to Observe's graph-based navigation model
• Limited on-premises option — SaaS-only delivery may not suit organizations with strict data residency or air-gapped requirements

Alternatives and How It Compares

Datadog

Datadog is the market leader in cloud observability with the broadest feature set: APM, logs, metrics, synthetics, security, CI visibility, and 750+ integrations. Datadog pricing is higher — log management alone costs $0.10/GB ingested plus $1.70/million events — and costs can escalate quickly at scale. Observe's data lake architecture offers significant cost advantages for high-volume environments, while Datadog provides a more mature ecosystem and broader feature coverage.

Splunk

Splunk is the legacy leader in log management, widely deployed in enterprises and government. Splunk Cloud pricing starts at ~$150/GB/day indexed, making it one of the most expensive options. Observe directly targets Splunk customers with its "better observability at a fraction of the cost" positioning. Splunk's advantage is its mature SPL query language, extensive app ecosystem, and deep security (SIEM) capabilities that Observe doesn't match.

New Relic

New Relic offers a generous free tier (100GB/month) and consumption-based pricing at $0.35/GB above the free allowance. New Relic's pricing model is more transparent than Observe's, and its full-stack observability platform is well-established. Observe differentiates with its data lake architecture and AI SRE, while New Relic offers broader out-of-the-box dashboards and a larger user community.

Grafana Cloud (Loki + Mimir + Tempo)

Grafana Cloud combines open-source components — Loki for logs, Mimir for metrics, Tempo for traces — into a managed platform starting at $0.50/GB for logs. It's the most cost-effective managed option for teams already using Grafana dashboards. However, Grafana Cloud lacks Observe's unified data lake and AI SRE capabilities, and correlating across Loki/Mimir/Tempo requires more manual effort than Observe's Context Graph.

Honeycomb

Honeycomb pioneered the "observability" movement with its high-cardinality event-based approach. It excels at debugging complex distributed systems through BubbleUp analysis and query-driven investigation. Honeycomb is strong for APM-focused teams but has weaker log management and infrastructure monitoring compared to Observe's unified platform.