What is observability software?

Observability software collects and analyzes telemetry data from applications and infrastructure to help teams understand system behavior and diagnose issues. It aggregates logs, metrics, and distributed traces into unified platforms that provide visibility across cloud-native, hybrid, and on-premise environments.

What is the difference between monitoring and observability?

Monitoring tracks predefined metrics and alerts when thresholds are exceeded, answering "is the system working?" Observability provides deeper context by correlating logs, metrics, and traces to answer "why is the system behaving this way?" Observability platforms let teams explore unknown issues without predefined dashboards.

What features should I look for in observability software?

Essential features include distributed tracing for microservices, centralized log management, metrics collection, customizable dashboards, and intelligent alerting. Advanced capabilities to consider include AIOps for anomaly detection, automatic service dependency mapping, OpenTelemetry support for vendor-neutral data collection, and integration with incident management tools.

What is OpenTelemetry and why does it matter?

OpenTelemetry is an open standard for collecting telemetry data from applications. It prevents vendor lock-in by using consistent APIs and data formats across different observability platforms. Most modern platforms including Datadog, New Relic, Dynatrace, and Grafana support OpenTelemetry, making it easier to switch vendors or use multiple tools simultaneously without reinstrumenting your code.

Enterprise Software

Top 8 Observability Software with Pricing and Feature Comparison

Sena Sezer

updated on Oct 4, 2025

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Observability platforms promise complete visibility across distributed systems, but selecting the right one is hard when every vendor claims they do everything.

We analyzed the top 8 observability software by looking at their documented capabilities, public pricing, verified customer reviews, and enterprise reference cases.

Pricing for best observability software

** Ratings are based on Capterra, Gartner, and G2. The vendors are listed according to their rating.

** The number of workforce is gathered from the companies’ LinkedIn pages.

Feature comparison of observability software

Vendor profiles

1. Solarwinds Observability

Solarwinds Observability is a hybrid infrastructure monitoring platform available as both SaaS and self-hosted deployments, providing network flow analysis and database performance insights across on-premise and cloud environments.

Key Capabilities:

NetFlow traffic analysis monitors network bandwidth usage and traffic patterns between services at the network protocol level
Hybrid infrastructure monitoring provides visibility across on-premise data centers and cloud environments including AWS, Azure, and Google Cloud Platform
Database performance monitoring includes query-level insights for SQL Server, Oracle, MySQL, PostgreSQL, and cloud database services
Pre-configured monitoring templates reduce setup time for common infrastructure components and application architectures

Best for: Organizations operating hybrid infrastructure deployments requiring network-level visibility alongside application observability

2. Datadog

Datadog is an observability software that integrates security monitoring, cloud cost tracking, and application performance data in a single interface.

Key Capabilities:

Security monitoring analyzes observability telemetry to identify threats, vulnerabilities, and attack patterns in real-time
Real user monitoring captures frontend performance metrics across web browsers and mobile applications
Cloud cost monitoring tracks infrastructure spending across providers with correlation to performance metrics for optimization analysis
Workflow automation executes remediation actions automatically based on alert conditions and predefined playbooks

Best for: Teams requiring integrated security and cost visibility across their observability workflow

3. New Relic

New Relic is an AI-powered observability platform that scans applications for vulnerabilities and integrates observability data directly into developer workflows.

Key Capabilities:

Vulnerability management scans application dependencies and runtime environments for security issues within the observability software
Applied Intelligence uses machine learning to detect anomalies, group related incidents, and reduce alert noise
Agentic AI integrations connect observability data with ServiceNow, GitHub Copilot, and Amazon Q Business for automated task management
Business observability correlates technical performance with revenue metrics and user engagement data

Best for: Development teams wanting AI-powered insights integrated with their existing development workflows

4. Dynatrace

Dynatrace is an automated observability software that uses Davis AI to map service dependencies and identify root causes without manual configuration.

Key Capabilities:

Automatic dependency mapping discovers service relationships and infrastructure topology without configuration requirements
Davis AI analyzes causation patterns across metrics and traces to identify root causes of performance degradations
Cloud Security Posture Management (CSPM) identifies misconfigurations and compliance violations across multi-cloud environments
Live Debugger provides real-time code-level diagnostics including variable values and method execution paths in production

Best for: Enterprises requiring automated problem detection with minimal manual configuration overhead

5. Better Stack

Better Stack is a unified observability tool that combines log management, uptime monitoring, incident management, and on-call scheduling for development teams.

Key Capabilities:

Incident management combines alerting with on-call scheduling, escalation workflows, and post-incident analysis in a unified interface
On-call scheduling automates rotation management with mobile applications for incident response
Status pages generate public uptime reporting directly from observability data without requiring separate status page services
Screenshot capture automatically records browser state when frontend errors occur for visual debugging context

Best for: Small to medium teams wanting unified observability and incident response without managing multiple specialized tools

6. Grafana Labs

Grafana Labs is an observability platform built on open-source foundations that offers flexible query languages and cost-optimized storage for metrics, logs, and traces.

Key Capabilities:

Query language flexibility supports PromQL for metrics, LogQL for logs, and TraceQL for traces with consistent syntax patterns
Plugin ecosystem provides over 150 community-built data source integrations and visualization options
Open-source foundation allows self-hosted deployment with migration paths to managed services
Cost-optimized storage uses Loki for logs and Tempo for traces with compression designed for observability workloads

Best for: Teams with existing Prometheus deployments or organizations requiring open-source flexibility with commercial support options

7. Splunk Observability

Splunk Observability is an enterprise observability platform that captures every transaction without sampling to provide complete visibility into distributed systems.

Key Capabilities:

No-sample tracing captures every transaction instead of sampling subsets for complete visibility into rare performance issues
Service Bureau enables multi-tenant deployments where different business units maintain isolated observability environments
Real-time streaming processes telemetry immediately without batch indexing delays common in log-centric platforms
Business workflows map technical services to business processes for impact analysis during outages

Best for: Enterprises with high transaction volumes requiring complete trace capture for performance analysis

8. Elastic Observability

Elastic Observability is a unified search platform that allows teams to query logs, metrics, and traces together with self-hosted deployment options for regulated industries.

Key Capabilities:

Unified search applies Elasticsearch query language across logs, metrics, and traces in a single interface
Machine learning detects anomalies in time-series data and generates forecasts based on historical patterns
SIEM capabilities analyze security events alongside application performance data within the same platform
Self-managed deployment provides control over data storage location and retention policies for regulated industries

Best for: Organizations with data sovereignty requirements or teams already using Elasticsearch for other applications

Shared features of observability software

All platforms provide these core observability capabilities.

Log aggregation: Centralized collection and search across application and infrastructure logs
Metrics collection: Time-series data gathering for system and application performance
Distributed tracing: Request tracking across microservices architectures
Alerting: Threshold and anomaly-based notification systems
Dashboards: Customizable visualization interfaces for observability data
API access: Programmatic interfaces for data retrieval and configuration
Multi-cloud support: Monitoring across AWS, Azure, and Google Cloud Platform
Kubernetes monitoring: Native support for containerized workload observability
Data retention: Configurable storage periods for historical analysis
Role-based access control: Granular permissions management for teams
Third-party integrations: Connections to development and operations tools

How to Choose Observability Software?

Infrastructure architecture: Hybrid environments with on-premise and cloud resources benefit from platforms supporting network flow analysis. Cloud-native applications on Kubernetes work well with platforms offering container-native monitoring and OpenTelemetry support.

Team size and structure: Smaller teams often prefer unified platforms combining observability with incident management to reduce tool sprawl. Larger organizations may integrate specialized tools for different functions across multiple teams.

Existing technology stack: Organizations using Elasticsearch, Prometheus, or specific cloud providers should evaluate platforms with strong integrations for their existing infrastructure. Migration costs and learning curves matter for established environments.

Budget and pricing model: Consumption-based pricing scales with usage but can be unpredictable during traffic spikes. Per-host or per-user models provide more predictable costs but may be expensive for dynamic infrastructure with frequent autoscaling.

Compliance requirements: Regulated industries with data sovereignty requirements should prioritize platforms offering self-managed deployment options or region-specific data storage to meet regulatory standards.

Security integration needs: Teams managing security and operations together benefit from platforms integrating vulnerability detection and threat monitoring with performance observability data in unified workflows.