We’ll cover features, pricing models, and the kind of teams each shines for, so you can confidently pick a winner for your capabilities and requirements. Even if you’ve already spent time researching companies who are Datadog competitors, you’ll be relieved that this post takes things to the next level, showing how competitors to Datadog position their strengths, where competitors of Datadog differ, and what a credible Datadog replacement could look like for your stack.

Comparison table: quick scan for busy people

Ratings and prices change. We’ve linked to official sources so you can confirm current details before you commit.

Product Best For Starting Prices G2 Rating* Key Features Compelling Use Case
SolarWinds Observability Best for hybrid ops From $144 per node or host annually 4.3/5 Full-stack telemetry
AIOps correlations
Hybrid visibility
Simple onboarding		 Unifying hybrid and cloud with built-in AI and actionable context
Dynatrace Best for auto-discovery From $350 per host annually 4.5/5 Automatic instrumentation with OneAgent
Davis-powered root-cause analysis
Topology mapping
Real user monitoring		 Enterprise-grade AI-assisted triage
New Relic Best for dev-led teams From $120 (1st user only) - $1,188 (additional users) annually plus usage fees 4.4/5 Unified Telemetry
Query everywhere
APM traces
Error tracking		 Developer-centric observability at scale
Splunk Observability Best for SRE analytics Quote-based 4.3/5 Metrics with many unique dimensions
No-sample tracing
Alerting
Synthetics		 Deep metrics at scale for SREs
Grafana (Cloud/self-hosted) Best for dashboards From $228 annually plus usage fees 4.5/5 Dashboards
Loki logs
Tempo traces
Prometheus native		 Central visual layer across data sources
Elastic Observability Best for search-driven troubleshooting Usage-based pricing 4.2/5 Elasticsearch storage
Kibana visuals
APM
Machine learning		 Log-heavy use cases on Elastic
Amazon CloudWatch Best for AWS-first Usage-based pricing 4.3/5 AWS native metrics
Logs Insights
Alarms
X-Ray traces		 All-in on AWS workloads
Microsoft Azure Monitor Best for Azure-first Usage-based pricing 4.3/5 Log Analytics
App Insights
Metrics
Alerts		 Azure-centric estates at scale
Google Cloud Operations Best for GCP-first Usage-based pricing 4.3/5 Cloud Monitoring
Cloud Logging
Traces
Service Level Objectives (SLOs)		 GCP workloads with SRE SLOs
IBM Instana Best for microservices From $240 per Managed Virtual Server (Essentials tier) 4.4/5 Auto-discovery
Always-on profiling
End-to-end tracing
Topology		 Rapid service discovery across fleets
LogicMonitor Best for infra pros Quote-based 4.5/5 Device discovery
Cloud metrics
AIOps
Alert tuning		 Unified infra monitoring plus cloud
Zabbix Best for open source From $600 annually (nano tier) 4.4/5 Agent/agentless
Templates
Triggers
Maps		 DIY monitoring with full control
Nagios XI Best for plugins $2,495 one-time for 100 nodes, standard edition 4.5/5 Checks/plugins
Alerting
Reporting
Extensibility		 Classic check-based monitoring
Paessler PRTG Best for SMB networks $2,149 annually for PRTG500 (typically 50 devices) 4.7/5 Sensors model
Auto-discovery
Maps
Notifications		 Easy network-centric monitoring

*As of September 2025.

Top 14 alternatives to Datadog in 2025

1. SolarWinds Observability

What it is

SolarWinds Observability is a full-stack platform to provide unified visibility across apps, services, infrastructure, networks, databases, and digital experience. It’s designed for all types of environments, from on-prem to hybrid estates where workloads span public clouds and your own facilities, to cloud-native set-ups. If you’re juggling tool sprawl, shifting priorities, and on-call rotations with an evolving environment, this is built to help you move faster with your eyes on fewer open tabs.

Who it’s for

Busy IT teams, SREs, DevOps engineers, and platform owners who need practical observability without a six-month rollout. If your world is part legacy, part cloud-native, and all high-stakes, SolarWinds is on your wavelength.

Core Features

Full-stack telemetry

Collect metrics, logs, traces, real user data, network path metrics, and database waits in one place. This shared context helps your different teams see cause and effect across layers, reducing finger-pointing and shortening time to resolution during incidents.

AIOps correlations

Built-in analytics connect related events, anomalies, and changes. You’ll see probable root cause, impacted services, and blast radius in minutes. It helps contain potential alert storms and keeps focus on the few actions that matter.

Hybrid visibility

Follow user transactions across cloud regions, on-prem clusters, WAN links, and SaaS dependencies. Service maps align apps with the infrastructure and networks they rely on, so hybrid paths are no longer blind spots.

Advanced Capabilities

Database deep dive

See waits, execution plans, and query performance by service, release, and timeframe. Spot regressions quickly and tie them to code changes or configuration drift, so you can tune the queries that actually matter.

Network path analysis

Understand hop-by-hop performance across SD-WAN, MPLS, ISP, and cloud edges. When someone is shouting that “the network is slow,” you can show exactly where and why, with latency and loss at each segment.

OpenTelemetry® native

Use open standards for ingestion. Keep data portable, avoid lock-in, and instrument modern stacks with community-backed collectors and SDKs, while still getting opinionated workflows on top.

Further Features

Release correlation

Overlay deploys on errors and golden signals (latency, traffic, errors, and saturation). See whether a spike ties to a new version, feature flag, or infrastructure change, so you can take whatever remedial is needed without delay.

Synthetic monitoring

Run browser and API checks from strategic locations. Validate critical journeys continuously and detect third-party issues before customers reach out to support or social media.

Real user monitoring

Measure real users across browsers and devices. Understand page performance, core web vitals, and the geographies or ISPs contributing to a poor experience.

Integrations and Ecosystem

Cloud and Kubernetes

Ingest native metrics and logs from AWS, Microsoft Azure, Google Cloud, and Kubernetes. Autodiscovery, and topology keep your dynamic environments navigable.

Platforms and middleware

Track queues, caches, and data pipelines. From message brokers to API gateways, see how middle tiers impact end-to-end performance and reliability.

Incident tooling

Connect alert routes to chat, paging, IT service management, and runbooks. Reduce switching between screens and maintain a coordinated response across teams and time zones.

Data openness

Export data when needed, integrate with pipelines, and retain control over how long you keep specific signals. Flexibility helps you fit compliance, cost, and performance goals.

Security-adjacent Visibility

Change intelligence

Correlate config changes, releases, and infrastructure drift with incidents. Knowing what changed is half the battle when things go sideways at 2 a.m.

Compliance support

Tag data by environment, retention class, or application owner. Align with policies while keeping the signal you need for reliable operations and audits.

Efficient collection

Lightweight agents and OpenTelemetry collectors provide flexibility. Choose what to collect, from which hosts, and at what frequency—the right level of detail for each signal and service helps you get actionable visibility without overspending.

Usability and Onboarding

Quick starts

Prebuilt dashboards, alerts, and detectors for common stacks help you see value fast. Start with something useful, then customize as you learn your patterns.

Opinionated defaults

Sensible pragmatic thresholds and templates reduce time spent tuning during setup. Your team can then refine things over time, rather than building everything from scratch on day one.

Learn as you go

Inline tips, context panes, and short videos are there for you when you need them. The goal is confidence, not a certification marathon.

Collaboration and workflows

Shared context

Attach traces, logs, runbooks, and recent changes to incidents. Everyone works from the same picture, so handoffs don’t lose critical clues.

Use cases

Hybrid incident response

Unify visibility across application, infrastructure, network, and database layers. Shorten the mean time to resolve and reduce ticket ping-pong during business-critical incidents.

Cloud migration assurance

Baseline, migrate, and compare performance and cost. Prove your outcomes, find regressions quickly, and iterate based on data rather than hunches.

Cost-performance trade-offs

See where small code or config changes could save significant money without compromising the user experience. Make performance and cost decisions together, for better balanced outcomes.

Support

You’ll find searchable docs, guided onboarding, and a highly-engaged community of practitioners who enthusiastically share templates and dashboards you can adopt in minutes. SolarWinds support plans scale from standard coverage to advanced success offerings with rollout planning, health checks, and best-practice reviews. If you prefer hands-on help, SolarWinds and its partner network can assist with instrumentation, data migration, dashboard design, and alert strategy so your team spends more time delivering and less time cobbling tools together.

Pricing

SolarWinds Observability offers simple tiers across application, infrastructure, log, database, and digital experience monitoring. Start with a fully functional 30-day free trial to explore end-to-end workflows. Scale with usage-based options where it makes sense, and keep predictable tiers for teams that prefer fixed pricing. You can mix capabilities by service or team, so you only pay for what you actually use. See details and FAQs on our pricing page to choose the combination that fits your environment and budgeting approach.

2. Dynatrace

Dynatrace targets large enterprises that want deeply automated, AI-assisted observability. Its OneAgent automates instrumentation across hosts, services, and containers, while the company’s “Davis” causation engine correlates signals for likely root cause. Teams use it for complex microservice estates and real user monitoring across web and mobile. Map-based topology, service flow, and code-level detail help you unpick performance downturns. It’s strong for SRE workflows, although the platform can feel heavy for smaller teams. Pricing typically follows a platform subscription with usage-metered capabilities and a time-limited free trial.

Key features and strengths

  • OneAgent automated instrumentation
  • Davis root-cause analysis
  • Real user and session replay
  • Kubernetes and cloud auto-discovery
  • Enterprise governance controls

Pricing: Platform subscription with usage-metered features; free trial available

Link: Dynatrace

3. New Relic

New Relic leans developer-first with a unified data layer and pervasive query capability. It brings logs, metrics, traces, errors, and profiling together with “SQL-like” New Relic Query Language for fast ad-hoc answers. Engineering teams like the free tier for kicking the tires without jumping through purchasing hoops, and once a decision’s been made, it’s easy to scale up paid users and data consumption as needed. Visualizations and quickstarts make it easy to stand up dashboards, but at scale, keep a close eye on data ingest costs and user seat planning, to avoid nasty surprises. Overall New Relic seems to suit app teams that want to self-serve insights without juggling multiple tools.

Key features and strengths

  • Free tier for exploration
  • New Relic Query Language
  • APM with distributed tracing
  • Error tracking and profiling
  • Quickstarts and dashboards

Pricing: Per-user plus data consumption; free tier available

Link: New Relic

4. Splunk Observability

Splunk Observability focuses on metrics with many unique labels at scale, no-sample tracing, and mature alerting. It’s aimed at site reliability engineers running complex, containerized platforms that need speed and fidelity. If you’re already using Splunk for logging or Security Information and Event Management there’s obviously a lighter lift on vendor management. Expect strong analytics, service maps, and programmatic control. Teams mention a learning curve and the need to plan costs for large environments. A host-based entry point and a free trial are common on-ramps.

Key features and strengths

  • Flexible alerting and SLOs
  • Synthetics and real user monitoring
  • Strong API and automation
  • Tight Splunk ecosystem ties

Pricing: Starts per-host with trial options; enterprise plans available

Link: Splunk Observability

5. Grafana (Cloud or self-hosted)

Grafana is the go-to visualization layer, and today the stack often includes Mimir for horizontally scalable, Prometheus-compatible metrics; Loki for label-driven logs; and Tempo for distributed tracing. Grafana Cloud bundles these as a managed service so you can run observability without maintaining the storage tiers. Self-hosted gives you full control by deploying Grafana with Mimir, Loki, and Tempo yourself. Grafana shines as a central view across many data sources, but for opinionated workflows and built-in correlations, expect to get busy on integration work.

Key features and strengths

  • Beautiful, flexible dashboards
  • Large ecosystem of plugins
  • Free and on-demand tiers
  • Works with many data sources

Pricing: Free tier, pay-as-you-go, and enterprise options

Link: Grafana

6. Elastic Observability

Elastic Observability builds on Elasticsearch storage and Kibana visualization. It unifies logs, metrics, and traces, with built-in machine learning jobs for anomaly detection. Teams already invested in Elastic often expand here for APM and infrastructure monitoring, so the data stays put. Self-managed or Elastic Cloud choices give you deployment flexibility. Overall it’s powerful for log-centric operations and search-heavy workflows.

Key features and strengths

  • Elasticsearch at the core
  • APM, metrics, logs together
  • Machine learning anomalies
  • Flexible ingest pipelines
  • Cloud or self-managed

Pricing: Free and usage-based tiers; enterprise subscriptions available

Link: Elastic Observability

7. Amazon CloudWatch

CloudWatch is the AWS-native option for metrics, logs, traces, and alarms. It integrates tightly with AWS services, Identity and Access Management (IAM), and billing, making it the obvious choice for teams that are fully all-in on AWS. Expect granular controls, managed retention, and alarms with incident hooks. Cross-cloud visibility will require additional tooling. If your architecture lives in AWS (and nothing but), CloudWatch offers straightforward, pay-as-you-go observability.

Key features and strengths

  • Deep AWS service coverage
  • X-Ray traces and service maps
  • Tight IAM and cost integration
  • Native automation hooks

Pricing: Usage-based on AWS bill; in typical AWS style, there’s “trial” as such - just sign up and use

Link: Amazon CloudWatch

8. Microsoft Azure Monitor

Azure Monitor combines metrics, Azure Log Analytics, Application Insights, and alerting into a single Azure-first experience. Use Kusto Query Language (KQL) for powerful analysis and build Workbooks for team-friendly views. If your workloads all run in Azure, this keeps data gravity and billing in one place. Hybrid and multi-cloud visibility are much trickier and will probably require connectors and/or additional third-party tools.

Key features and strengths

  • Azure Log Analytics with KQL
  • Application Insights for APM
  • Alerts, action groups, webhooks
  • Workbooks and dashboards
  • Native policy integration

Pricing: Usage-based, tied to Azure subscription

Link: Azure Monitor

9. Google Cloud Operations

Google Cloud Operations (formerly Stackdriver) provides logging, monitoring, tracing, and error reporting with SLO-centric dashboards. It’s ideal for environments that are big on Google Cloud Platform, and SRE teams practicing error budgets. Expect tight IAM and project-scoped controls, plus strong Kubernetes monitoring for Google Kubernetes Engine (GKE). Cross-provider coverage is possible with agents and exporters, but it’s really not the focus.

Key features and strengths

  • Monitoring, Logging, Trace, Error Reporting
  • SLOs and burn rate out-of-the-box
  • GKE integration
  • IAM-aligned controls and projects
  • Good cost visibility

Pricing: Usage-based within Google Cloud billing

Link: Google Cloud Operations

10. IBM Instana

Instana auto-discovers services, traces requests without sampling, and continuously profiles code. It appeals to teams that want fast time-to-value on microservice discovery and dependency mapping with minimal manual setup. Visuals are clear, and always-on profiling helps with performance issues. Instana fits the needs of the largest enterprises wanting detailed service maps and continuous tracing.

Key features and strengths

  • Automatic discovery and mapping
  • Always-on code profiling
  • High-fidelity distributed tracing
  • Release impact analysis

Pricing: Quote based; trials generally available

Link: IBM Instana

11. LogicMonitor

LogicMonitor focuses on infrastructure monitoring, although network, server, storage, cloud, and applications are all in-scope with strong auto-discovery and templating. Teams like its device-centric model, curated dashboards, and alert tuning. It’s a solid fit for infrastructure operations and managed service providers that want breadth and fast onboarding without building everything from scratch.

Key features and strengths

  • Broad device discovery templates
  • Cloud metrics and services
  • Alert tuning and escalation
  • Role-based views and reports

Pricing: Quote-based; trials are typically available

Link: LogicMonitor

12. Zabbix

Zabbix is a mature open-source platform known for flexibility. It supports both agent-based and agentless monitoring, provides templates for common systems, and offers rich alerting capabilities. It suits teams comfortable with investing time and effort in running their own platform, and who value low costs and flexibility to customize. Community and commercial support options exist, and it integrates well with Prometheus and Grafana for visuals.

Key features and strengths

  • Open source and extensible
  • Agent and agentless options
  • Templates for common tech
  • Scalable proxies and nodes
  • Active community resources

Pricing: Free core; commercial support plans available

Link: Zabbix

13. Nagios

Nagios is an OG open-source monitoring tool, which popularized check-based monitoring in the early 2000s. Nagios Core remains free and flexible, while Nagios XI adds ease-of-use and reports at a price. Teams with specific check needs or established plugin workflows still lean on it, especially for infrastructure and network basics. If you opt for Nagios, plan to invest in plugin management and a scaling strategy as your environment grows.

Key features and strengths

  • Huge plugin ecosystem
  • Check-based monitoring model
  • Alerting and notifications
  • SLA and availability views
  • Community plus commercial support

Pricing: Free Core; commercial XI tiers with trials

Link: Nagios

14. Paessler PRTG

PRTG organizes monitoring around “sensors,” which makes scoping straightforward for networks, servers, and applications. Auto-discovery and out-of-the-box sensors help small to mid-size teams get up and running quickly. Visual maps and mobile apps are popular, and it’s especially handy when you want reliable coverage without complexity.

Key features and strengths

  • Sensor-based licensing clarity
  • Auto-discovery and templates
  • Maps and dashboards
  • Notifications and schedules
  • Remote probes for sites

Pricing: Tiered by sensor counts; free trial offered

Link: Paessler PRTG

What Is Observability Software and What Are Its Common Uses?

Observability software helps you understand the internal state of complex systems by analyzing external outputs – classically metrics, logs, traces, and increasingly also user experience signals. It helps teams detect, troubleshoot, and prevent issues across your whole stack: applications, infrastructure, networks, database, on-premises and in cloud services.

Benefits of using observability software

  • Faster root cause analysis
    Linked metrics, traces, logs, and topology help pinpoint root causes faster and reduce finger-pointing between teams
  • Enhanced performance optimization
    Code-level and query-level insights spotlight hotspots and regressions, ideally before customers or stakeholders notice
  • Improved system reliability
    Consistent visibility and SLO tracking help teams prevent incidents and meet system/network availability and performance goals
  • Proactive incident detection
    Anomaly detection and smart baselines surface issues early, often before end users feel the impact
  • Scalability and flexibility
    Architectures and pricing designed to scale with traffic, services, and data growth without re-platforming
  • AI and automation
    Correlation, enrichment, and suggested actions reduce noise and free people to focus on fixes

Features To Look For in Observability Software

Must-have features

  • Embedded AI and automation for correlation, anomaly detection, and noise reduction. It’s much easier to buy these baked into your observability solution than retrofit them yourself
  • Unified telemetry: if you can access metrics, logs, traces, and user experience in one workflow, you’ll avoid a whole lot of integration effort
  • Observability solutions that embrace open standards (notably OpenTelemetry) will help ensure your observability setup is future-proofed against unexpected change
  • Robust alerting and SLOs integrated with incident tools

Important considerations

  • Deep integrations across your cloud providers, platforms, databases, and network stack
  • Horizontal scalability, cost transparency, and data retention controls that fit your growth
  • Quality documentation, onboarding help, and responsive customer support

Choosing and Implementing the Right Datadog Alternative for You

Your selection process

  • Create a list of must-have features tied to user-facing and business outcomes and policy and compliance needs
  • Form an evaluation team with key stakeholders across Dev, SRE, Ops, Security, and Finance
  • Run and evaluate trials and proofs of concept against pre-agreed success criteria so decisions reflect your reality, not a demo

Setting up your new observability software

  • Define clear goals for the software so everyone knows what “good” looks like
  • Plan the implementation to minimize friction for the team with phased rollout and owners
  • Configure settings and integrations including identity, alert routes, SLOs, and dashboards
  • Document your usage, test your setup, iterate
  • Documentation – create a short internal guide covering alerts, dashboards, naming, runbooks, and who to page when
  • Testing – pilot with a small group, inject synthetic failures, and gather feedback to tune alerts and dashboards
  • Keep optimizing – stay curious, review signals, costs, and outcomes regularly, then tune what you collect and alert on

As of September 2025

Product specifications and other information set forth herein have either been made accessible by suppliers, manufacturers, publications, or gathered from publicly available sources as of the date of this document. Although measures are taken to ensure the accuracy of the information, SolarWinds makes no representations or warranties as to the completeness or accuracy of the information and shall incur no liability for any errors or omissions.