Prometheus is the best choice for teams that want full control over their metrics monitoring infrastructure with zero licensing costs. Observe is the better fit for organizations that need unified observability across logs, metrics, and traces with AI-powered troubleshooting and managed operations.
| Feature | Prometheus | Observe |
|---|---|---|
| Deployment Model | Self-hosted open-source server written in Go with independent operation and local storage | Fully managed SaaS platform built on a streaming data lake architecture |
| Pricing | Free and open source | Logs at $0.49, other tiers at $0.00, $0.01, $0.59 |
| Data Collection | HTTP pull-based model with native Kubernetes service discovery and push gateway option | Real-time ingest pipeline with OpenTelemetry collection to avoid vendor lock-in |
| Query Language | PromQL purpose-built for dimensional time series data querying, correlation, and transformation | Visual explorers for logs, metrics, services, Kubernetes, and LLM observability workflows |
| AI Capabilities | No built-in AI features; relies on community integrations and manual rule-based alerting | AI SRE agent that surfaces root causes, correlates signals, and suggests actionable fixes |
| Best For | Cloud-native teams needing flexible open-source metrics monitoring with Kubernetes integration | Teams seeking unified observability across logs, APM, and infrastructure at reduced cost |
| Feature | Prometheus | Observe |
|---|---|---|
| Data Collection & Ingestion | ||
| Metrics Collection Method | Pull-based HTTP scraping model with configurable intervals and push gateway for batch jobs | Real-time ingest pipeline accepting logs, metrics, and traces via OpenTelemetry collectors |
| Service Discovery | Native Kubernetes service discovery plus static configuration and DNS-based discovery | 400+ pre-built integrations for cloud, Kubernetes, and infrastructure data sources |
| Data Formats | Prometheus exposition format with multi-dimensional labels as key-value pairs | Open formats stored in Iceberg tables with 10x compression on low-cost cloud storage |
| Querying & Analysis | ||
| Query Language | PromQL for querying, correlating, and transforming dimensional time series data | Visual explorers with natural language correlation through the AI SRE agent |
| Data Model | Multi-dimensional data model where time series are identified by metric name and key-value pairs | O11y Context Graph structuring logs, metrics, and traces as entities with semantic relationships |
| Search Performance | Local time series database optimized for recent data with configurable retention | Token indexes and incremental views on the O11y Context Graph for fast correlation |
| Alerting & Incident Response | ||
| Alerting System | PromQL-based alerting rules with separate Alertmanager handling notifications and silencing | AI SRE builds investigation plans and delegates tasks to agents for automated triage |
| Root Cause Analysis | Manual investigation using PromQL queries and dashboard correlation across metrics | AI SRE formulates investigation plans, surfaces root causes, and suggests actionable fixes |
| Incident Workflow | Integration with external tools like Grafana and PagerDuty for incident management | Chat-based root cause analysis with investigation summaries stored for future reference |
| Platform & Operations | ||
| Deployment | Self-hosted Go binary with independent servers relying only on local storage | Fully managed SaaS requiring no infrastructure management or capacity planning |
| Scalability | Federation with hierarchical and horizontal modes for multi-cluster architectures | Elastic compute on streaming data lake designed for scale without bottlenecks |
| Ecosystem | 63,000+ GitHub stars, CNCF graduated project, hundreds of community integrations | Unified platform integrating logs, APM, infrastructure monitoring, and LLM observability |
| Observability Scope | ||
| Metrics Monitoring | Core strength with dimensional time series collection, storage, and querying | Infrastructure metrics from cloud, Kubernetes, and 400+ pre-built integrations |
| Log Management | Not included natively; requires external tools like Loki for log aggregation | Built-in log management with search and analytics at scale without retention constraints |
| APM / Tracing | Not included natively; requires external tools like Jaeger or Tempo for tracing | Full APM capturing every user request without sampling for service-level root cause analysis |
Metrics Collection Method
Service Discovery
Data Formats
Query Language
Data Model
Search Performance
Alerting System
Root Cause Analysis
Incident Workflow
Deployment
Scalability
Ecosystem
Metrics Monitoring
Log Management
APM / Tracing
Prometheus is the best choice for teams that want full control over their metrics monitoring infrastructure with zero licensing costs. Observe is the better fit for organizations that need unified observability across logs, metrics, and traces with AI-powered troubleshooting and managed operations.
Choose Prometheus if:
We recommend Prometheus for cloud-native engineering teams that prioritize open-source flexibility and already have Kubernetes expertise. Prometheus excels when your primary need is metrics monitoring and you have the operational capacity to self-host. Its PromQL query language, massive community ecosystem with 63,000+ GitHub stars, and CNCF graduated status make it the industry standard for metrics collection in containerized environments.
Choose Observe if:
We recommend Observe for organizations that need a single platform covering logs, APM, and infrastructure monitoring without managing observability infrastructure. Observe stands out with its AI SRE agent that automates root cause analysis and its streaming data lake architecture that claims to cut observability costs by up to 60%. The usage-based pricing starting at $0.49/GB for logs with unlimited users makes it accessible for growing teams.
This verdict is based on general use cases. Your specific requirements, existing tech stack, and team expertise should guide your final decision.
Yes, Prometheus is completely free and open source under the Apache 2.0 license. There are no licensing fees, user limits, or feature gates. You can deploy it in production environments without any cost for the software itself. The main expenses come from the infrastructure you run it on, such as compute and storage for your Prometheus servers. As a CNCF graduated project, Prometheus benefits from open governance and long-term community support.
Observe's AI SRE acts as an automated investigation agent during incidents. When an issue is detected, the AI SRE formulates an investigation plan, delegates tasks to specialized agents, and presents results to the on-call engineer. It correlates signals across logs, metrics, and traces using natural language queries, surfaces root causes, and suggests actionable fixes. The system also maintains chat-based summaries of each investigation so teams can reference past incidents when similar problems occur.
Prometheus is purpose-built for metrics collection and time series data. It does not include native log management or distributed tracing capabilities. To build a full observability stack around Prometheus, teams typically add Grafana Loki for logs and Jaeger or Grafana Tempo for traces. This modular approach gives you flexibility to choose best-of-breed tools for each signal type, but it does require managing multiple systems and their integrations.
Prometheus stores time series data locally on disk with configurable retention periods, typically set by time or storage size limits. Long-term storage requires external solutions like Thanos or Cortex. Observe offers 30-day retention on standard plans and 13-month retention on higher tiers, with data stored in its open data lake using Iceberg tables with 10x compression. Observe's managed approach eliminates the need to configure and maintain separate long-term storage infrastructure.