About
Observe is an AI-powered observability platform designed for engineering and SRE teams that need to troubleshoot complex distributed systems faster and at dramatically lower cost than traditional solutions like Splunk or the ELK Stack. At its core, Observe is built on the O11y Data Lake™ — an open, elastic data lake using Apache Iceberg tables and 10x compression on low-cost cloud storage — and the O11y Context Graph™, which structures logs, metrics, and traces as semantically related entities with incremental views and token indexes for instant correlation. The platform's AI SRE capability (o11y.ai) allows engineers to correlate signals using natural language and surface root causes with actionable fix suggestions, reducing mean time to resolution by up to 3x. Observe supports full-fidelity Application Performance Monitoring with no sampling, Infrastructure Monitoring across cloud and Kubernetes with 400+ integrations, and LLM Observability for AI applications and agentic workflows. Data is ingested via a real-time pipeline with OpenTelemetry support to avoid vendor lock-in, and all telemetry is stored in open formats for maximum reuse. Delivered as fully managed SaaS, Observe targets enterprises and high-growth engineering teams seeking scalable observability at roughly one-third the total cost of ownership of legacy platforms. Snowflake acquired Observe to extend AI-powered observability to its broader customer base.
Key Features
- AI SRE (o11y.ai): An AI-powered site reliability engineer that correlates signals using natural language, surfaces root causes, and suggests actionable fixes to accelerate incident resolution.
- O11y Context Graph™: Structures logs, metrics, and traces as entities with semantic relationships, incremental views, and token indexes for instant search and cross-signal correlation.
- Open O11y Data Lake™: Stores all telemetry in open Apache Iceberg tables with 10x compression on low-cost cloud storage, enabling up to 60% cost reduction vs. legacy observability tools.
- LLM Observability: Provides deep visibility into AI applications and agentic workflows, monitoring AI infrastructure and token usage to optimize performance and control costs.
- Full-Stack Monitoring: Covers log management, application performance monitoring (no sampling), and infrastructure monitoring across cloud and Kubernetes with 400+ pre-built integrations.
Use Cases
- Enterprise SRE teams reducing mean time to resolution on production incidents using AI-assisted root-cause analysis across logs, metrics, and traces.
- Platform engineering teams replacing high-cost legacy tools like Splunk with an open, cost-efficient data lake-based observability solution.
- Cloud-native organizations monitoring Kubernetes and multi-cloud infrastructure with 400+ integrations and real-time dashboards.
- AI/ML engineering teams gaining full observability into LLM-powered applications, tracking token usage, latency, and agentic workflow behavior.
- DevOps teams adopting OpenTelemetry who need a vendor-neutral backend that stores data in open formats for long-term reuse and cost control.
Pros
- Dramatic cost savings: Cuts observability TCO by up to 60% compared to Splunk and similar platforms through open data lake storage and elastic compute.
- AI-accelerated troubleshooting: The AI SRE reduces MTTR by 3x by automatically correlating signals and recommending fixes, saving significant engineering time on complex incidents.
- No vendor lock-in: OpenTelemetry-native ingest and open Iceberg storage formats ensure full data portability and reuse across your stack.
- Unified observability: Logs, metrics, traces, and LLM observability in one integrated platform eliminates tooling silos and context switching.
Cons
- Enterprise-focused pricing: Primarily designed for mid-to-large engineering teams; pricing and feature depth may be excessive for small startups or individual developers.
- Learning curve for context graph: The O11y Context Graph™ model is powerful but introduces new concepts that teams migrating from traditional tools may need time to adopt.
- Snowflake acquisition uncertainty: The recent acquisition by Snowflake may bring product direction changes, pricing adjustments, or integration requirements that affect existing customers.
Frequently Asked Questions
What makes Observe different from Splunk or the ELK Stack?
Observe is built on an open streaming data lake with 10x compression and elastic compute, making it significantly cheaper to operate. It also includes a native AI SRE for automated root-cause analysis and a context graph that structures signals semantically — capabilities not native to Splunk or ELK.
What is the O11y Context Graph™?
The O11y Context Graph™ is Observe's proprietary data model that represents logs, metrics, and traces as typed entities linked by semantic relationships. This enables instant cross-signal correlation, incremental computed views, and high-performance token-based search.
Does Observe support OpenTelemetry?
Yes. Observe natively supports OpenTelemetry for data collection across all signal types, ensuring you avoid vendor lock-in and can reuse your instrumentation across any future observability stack.
Can Observe monitor AI and LLM applications?
Yes. Observe includes dedicated LLM Observability capabilities that provide visibility into AI application behavior, agentic workflows, token usage, and AI infrastructure performance.
Is there a free trial available?
Yes, Observe offers a free trial so teams can evaluate the platform before committing. Enterprise pricing is available by contacting the sales team or booking a demo.
