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Cybersecurity Information SecurityTop 10 Best Observability Software of 2026
Compare observability software options for teams, covering key tradeoffs and use cases with tools like New Relic, Dynatrace, and Datadog.
How we ranked these tools
Vendors set the topic, but our editors set the inclusion criteria and the evaluation framework before research starts.
Search agents gather the source set and the review record, including transcriptions of video reviews and podcasts.
Framework: Features 40% · Ease 30% · Value 30%
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New Relic is the best fit for enterprise teams that need correlated APM and infrastructure visibility because service and trace correlation links incidents to span context and dependency views, while Grafana is a strong alternative for teams going dashboard-as-code since it pairs API-driven dashboard management with flexible alert routing.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
New Relic
Service and trace correlation drives incident views that connect span context to logs and infrastructure dependencies.
Best for: Fits when teams need correlated APM and infrastructure visibility with automation and governance controls.
Dynatrace
Editor pickAutomated root-cause analysis links distributed traces to dependency topology and deployment change context for incident prioritization.
Best for: Fits when SRE teams need correlated incident context across apps and hosts with automation-friendly configuration.
Datadog
Editor pickTrace-to-log correlation with unified investigation timelines across services speeds root-cause analysis during incidents.
Best for: Fits when teams need correlated APM, logs, and infrastructure telemetry with strong governance controls.
Comparison Table
New Relic
enterpriseTelemetry platform combining APM, infrastructure monitoring, logs, and digital experience analytics.
Service and trace correlation drives incident views that connect span context to logs and infrastructure dependencies.
New Relic’s data model centers on entities like services, hosts, and processes, then links those entities to traces and logs so dashboards and incidents share context. The platform also exposes automation and extensibility through APIs and configurable alerting workflows that can be wired into on-call and remediation processes. Integration depth is strong when teams already run New Relic agents for APM and infrastructure monitoring or when they stream OpenTelemetry into New Relic using OTLP ingestion. Governance improves at scale with role-based access controls and audit logging for administrative actions.
A tradeoff appears when cardinality growth comes from high-cardinality attributes in traces and logs, because alert and dashboard usability degrades if labels are unbounded. A strong usage situation is incident triage for backend services where trace context and service dependency views reduce time-to-root-cause across multiple tiers.
- +Cross-signal incident views link services, traces, and logs for faster triage
- +APIs and automation support provisioning workflows for dashboards and alert policies
- +Entity model connects infrastructure signals to application traces and dependencies
- +RBAC plus audit logging provides governance for monitoring administrators
- –Unbounded trace or log attributes can increase cardinality and reduce usability
- –Deep tuning of ingest and retention needs engineering discipline to stay effective
- –Agent-based coverage can require rollout work across heterogeneous environments
- –Tailored analytics often depend on consistent instrumentation across services
SRE and on-call engineers
Triage multi-tier incidents with trace context
Lower time-to-mitigate
Platform engineering teams
Automate monitoring configuration at scale
Consistent observability rollouts
Show 2 more scenarios
Backend application teams
Track latency and errors across services
Clear dependency attribution
Distributed tracing and metrics support identifying which dependencies drive latency percentiles and error spikes.
Operations leaders
Control access to monitoring changes
Stronger governance
RBAC and audit logs track administrative actions so changes can be reviewed during incidents or audits.
Best for: Fits when teams need correlated APM and infrastructure visibility with automation and governance controls.
Dynatrace
enterpriseAI-driven observability platform with auto-discovery and dependency mapping for cloud-native workloads.
Automated root-cause analysis links distributed traces to dependency topology and deployment change context for incident prioritization.
Dynatrace uses an internal entity model to connect services, hosts, processes, and requests so dashboards and incident views share the same dependency graph. Data collection can be extended through OpenTelemetry ingestion for traces and metrics, and the platform automation surface supports configuration management via APIs for repeatable rollouts. The environment mapping and root-cause suggestions reduce manual pivoting across APM views, infrastructure views, and change context.
A key tradeoff is that accurate correlation depends on consistent instrumentation coverage and stable service naming across environments. Dynatrace fits teams with enough platform governance to standardize deployment tags, service identifiers, and entity attributes before scaling to many services and tenants.
- +Correlates traces and infrastructure entities in shared topology views
- +Automation APIs support configuration and environment provisioning workflows
- +Root-cause prioritization ties anomalies to deployments and dependencies
- +eBPF-based visibility option improves low overhead profiling and system context
- –Accurate correlation requires consistent service naming and deployment metadata
- –Deep configuration can slow rollout for teams without platform ownership
SRE incident response teams
Triage correlated app and host anomalies
Faster issue containment and reduced MTTR
Platform engineering teams
Standardize observability configuration across services
More consistent dashboards and alerting
Show 2 more scenarios
DevOps teams migrating observability
Ingest OpenTelemetry traces and metrics
Consolidated views without rewriting instrumentation
OTLP ingestion supports integrating existing instrumentation pipelines while keeping Dynatrace’s correlation model.
Performance engineering groups
Diagnose bottlenecks with host context
More actionable performance investigations
System-level context from enhanced monitoring helps isolate whether issues originate in services or infrastructure.
Best for: Fits when SRE teams need correlated incident context across apps and hosts with automation-friendly configuration.
Datadog
enterpriseCloud-scale monitoring and security platform for infrastructure, applications, logs, and traces.
Trace-to-log correlation with unified investigation timelines across services speeds root-cause analysis during incidents.
Datadog supports distributed tracing with span context propagation across services, plus trace-to-log and trace-to-metric correlation for incident timelines. Its metrics ingestion supports Prometheus exposition format via scraping-style integrations and also accepts push-style metric intake for non-Prometheus sources. It offers SLO tracking and burn-rate alerting so teams can move from alert firehose to error budget analysis.
A key tradeoff is that high-cardinality labels can raise ingestion and storage costs, so label discipline is required for long-running environments. Datadog fits teams that need cross-signal troubleshooting across backend services and frontend traffic with fast pivoting from symptoms to traces.
- +Cross-signal correlation ties traces, logs, and metrics into one investigation timeline
- +OTLP ingestion supports standard telemetry pipelines across services and tooling
- +SLO burn-rate alerting connects service health to error budget tracking
- +RBAC and audit log coverage supports operational governance for shared environments
- –Label cardinality mistakes can quickly inflate ingestion volume and costs
- –Some advanced workflows require careful integration configuration to match telemetry models
- –Trace sampling choices can hide edge-case failures if tuned incorrectly
- –Deep tail-based debugging still depends on how traces are sampled and retained
Platform engineering teams
Standardize telemetry across microservices
Consistent service health visibility
SRE and on-call teams
Triage incidents using correlated evidence
Faster incident resolution
Show 2 more scenarios
Backend service owners
Track latency and error budgets
Better reliability decisions
SLO monitoring and burn-rate alerts connect releases and regressions to error budget burn patterns.
DevOps teams with mixed tooling
Ingest existing Prometheus metrics
Shorter onboarding time
Prometheus exposition format integrations reduce migration friction for metrics-heavy environments.
Best for: Fits when teams need correlated APM, logs, and infrastructure telemetry with strong governance controls.
Grafana
API-firstOpen-source visualization and alerting platform supporting Prometheus, Loki, Tempo, and multiple backends.
Grafana Alerting unified rule management with per-contact routing and API controllability for dashboard-driven operations.
Grafana provides dashboarding and query-time visualization across metrics, logs, and traces while supporting alerting and shared governance. Its core strength is integration depth through data source plugins and ingest adapters such as Prometheus exposition format and OTLP ingestion.
- +Dashboard-as-code via provisioning and API-driven dashboard management
- +Broad data source support through plugin architecture and query adapters
- +Cross-signal navigation that links metrics, logs, and traces from dashboards
- +Alerting integration with notification channels and rule lifecycle management
- –Template variables can amplify query load and cause performance regressions
- –Advanced multi-team governance needs deliberate RBAC and folder strategy
- –Trace exploration depth depends on the chosen trace backend and query model
- –High-cardinality fields in logs can increase storage pressure and dashboard cost
Operations analysts and reporting teams
Reusable dashboards with variable-driven reporting
Reusable reports on shifting data
Environmental monitoring teams
Alerts on location-based threshold breaches
Faster awareness of threshold events
Show 2 more scenarios
DevOps and SRE teams
Live service health dashboards with alerts
Earlier incident visibility
Create system, application, and service telemetry dashboards with alerts on operational threshold conditions.
Developers and reliability engineers
Correlate telemetry during incident investigations
Connected view across telemetry types
Display related metrics, logs, and traces in coordinated dashboards to investigate changes and production incidents.
Best for: Fits when teams need dashboard-as-code, cross-signal correlation, and automation APIs across multiple telemetry backends.
Splunk
enterpriseLog-centric observability and SIEM platform for search, monitoring, and analytics at scale.
Search-driven correlation using Splunk field extraction and knowledge objects across logs and telemetry sources.
Splunk delivers observability through machine data indexing and search, with dashboards and alerting built on a unified event model. It supports log-centric workflows plus infrastructure and application telemetry through add-ons and collectors, with correlation across logs, metrics, and traces when data is normalized into searchable fields.
- +Field-based event model enables cross-signal correlation in one search language
- +Forwarder-driven ingestion supports high throughput and controlled buffering behavior
- +REST API surface covers automation for users, apps, knowledge objects, and data access
- +RBAC plus audit logging supports governance for teams and shared workspaces
- –Query-centric workflows can slow teams that expect metric-grade aggregation defaults
- –High-cardinality fields can inflate index size without strict field hygiene
- –Distributed tracing requires additional configuration and alignment of field mappings
- –Operational complexity rises with multiple apps, environments, and content promotion
IT operations and SRE teams
Reconstruct failures across logs and traces
Faster root-cause analysis
IT service management teams
Triage alerts with correlated service context
More focused triage
Show 2 more scenarios
Service management and operations leaders
Recurring reports from log-derived signals
Consistent health reporting
Build recurring reports from log-derived signals to track service performance and operational trends.
Infrastructure and reliability teams
Dashboards and alerts for performance trends
Ongoing performance visibility
Track latency, errors, availability, and operational trends using dashboards and scheduled alerts.
Best for: Fits when SRE and DevOps teams need unified event search, automation APIs, and governance for shared observability content.
Jaeger
API-firstOpen-source distributed tracing platform for monitoring and troubleshooting microservices transactions.
Span search in Jaeger’s UI ties trace context to service graphs and dependency views for fast incident root-cause navigation.
Jaeger is a distributed tracing system focused on ingesting, storing, and querying trace spans across microservices. It centers on span-based search with service, operation, and tag filters, plus configurable sampling behavior at instrumentation or collector layers.
- +Strong distributed tracing UI with service, operation, and tag-based span search
- +OTLP ingestion supports common OpenTelemetry tracing pipelines
- +Flexible storage backends for trace retention and query patterns
- +Integrates with tracing instrumentation via agentless collector components
- –Tail-based debugging across high-cardinality tags can increase storage and query cost
- –Operational setup needs careful tuning for sampling, indexing, and retention
- –Metrics and logs workflows depend on external systems rather than native dashboards
- –Cross-signal correlation requires consistent trace context propagation across producers
Backend engineering teams
Debug microservice request path failures
Root cause found faster
SRE and reliability engineers
Identify latency hotspots by service
Latency reduced through targeting
Show 2 more scenarios
Platform engineers
Centralize tracing with OpenTelemetry
Consistent traces across services
Platforms ingest OTLP spans, apply sampling, and standardize tracing pipelines across workloads.
Performance analysts
Analyze traffic patterns and errors
Actionable incident insights generated
Analysts query tag-based span data to correlate error rates with operations and deployments.
Best for: Fits when teams need deep distributed tracing with OpenTelemetry-compatible ingestion and trace-driven debugging workflows.
Prometheus
API-firstOpen-source metrics collection and alerting toolkit with a multi-dimensional data model and query language.
PromQL supports rich histogram queries and rate-aware alerting with predictable label-based aggregation.
Prometheus performs metrics collection and time-series storage by scraping targets and evaluating alert rules to drive notifications. Its core capability centers on the Prometheus exposition format and the PromQL query language for selection, aggregation, and histogram-aware analysis.
- +Scrape-based model with PromQL enables fast, repeatable metric queries
- +Alertmanager routing supports label-driven deduplication and grouping
- +Histograms and exemplars support latency distributions and trace correlation patterns
- +Extensible exporters cover node, application, and custom metrics endpoints
- –High-cardinality label sets can quickly degrade storage and query throughput
- –Distributed tracing and logs require additional components beyond Prometheus
Enterprise platform teams
Federate metrics across multiple clusters
Broader environment coverage
Industrial engineering teams
Centralize device and machine metrics
Queryable measurements in one place
Show 2 more scenarios
Infrastructure operations teams
Threshold alerts for host and component health
Threshold-based notifications
Track resource usage, availability, and health signals across hosts and platform components with alert rules.
Engineering managers and planners
Capacity trends from historical metrics
Evidence for scaling decisions
Aggregate historical time-series metrics to identify resource trends and support capacity planning decisions.
Best for: Fits when SRE teams need scrape-driven metrics control and PromQL for alerting on SLO-adjacent signals.
Coralogix
enterpriseLog analytics and observability platform using streaming analytics to reduce storage and query costs.
Unified investigation views that correlate trace spans with related log evidence for incident timelines.
Coralogix focuses on log and observability workflows that prioritize faster root-cause correlation across services and user journeys. It ingests telemetry through common open standards workflows and also supports vendor-native pipelines that map traces and logs into a unified investigation view.
- +Strong trace-to-log correlation for faster incident investigations
- +Open standards ingestion supports OTLP-based workflows and heterogeneous telemetry sources
- +Configurable alert context reduces back-and-forth during triage
- +High-signal log search built around structured fields and fast filtering
- –Provisioning and field normalization require governance discipline
- –Advanced automation depends on feature configuration that can take iteration
- –Dashboards require planning to stay maintainable across many services
- –Cardinality control is sensitive to ingestion labeling choices
SRE and operations teams
Unified views for logs, metrics, and traces
Consolidated incident views
Platform engineering teams
Managed ingestion and enrichment for telemetry
Centralized telemetry analysis
Show 2 more scenarios
Web operations teams
Investigate server and proxy log anomalies
Faster abnormal traffic diagnosis
Analyze server and proxy logs with request attributes like paths, referrers, client details, and error patterns.
Security operations teams
Correlate auth and access events
More consistent event timelines
Normalize and correlate authentication and access events to investigate anomalies with consistent searches and timelines.
Best for: Fits when teams want correlated log and trace investigation with automation focus for SRE triage.
Sumo Logic
enterpriseCloud-native log analytics and observability platform with machine-learning-based anomaly detection.
Log search that drives analysis, alert context, and correlation across traces and metrics from shared fields.
Sumo Logic differentiates with long-term log-centric observability that integrates logs, metrics, and traces into one search and analysis workflow. Its managed ingestion and data pipeline features support continuous collection from agents, API, and cloud integrations, so teams can scale telemetry intake without stitching custom collectors.
- +Unified log search workflow with correlated metrics and traces
- +Broad ingestion options that cover agents, APIs, and cloud sources
- +Audit logging and RBAC support controlled multi-team access
- +Automation via saved searches, scheduled reports, and API-driven management
- –Dashboards and alert logic can become complex with high-dimensional logs
- –Advanced sampling and trace throughput tuning often needs careful planning
- –Some cross-signal workflows require consistent field naming discipline
- –High-cardinality log patterns can strain query latency and storage retention
Site reliability teams
Monitor reliability with SLI-driven investigations
Shared reliability visibility
Network security operations
Alert on query-defined network patterns
Traceable network anomaly visibility
Show 2 more scenarios
Security operations centers
Query-based detections with incident timelines
More consistent investigations
Use normalized security events and query-based detections to investigate suspicious activity and build timelines.
Network operations teams
Normalize syslog for cross-source health checks
Cross-source monitoring without duplication
Normalize syslog from devices and services for health monitoring, access analysis, and centralized investigation.
Best for: Fits when teams need governed, log-first observability with cross-signal correlation and automation.
Elastic
enterpriseSearch-powered observability stack combining Elasticsearch, Kibana, Beats, and Elastic Agent.
Fleet-managed Elastic Agent with integrations provides consistent provisioning of observability collection and ingest settings at scale.
Elastic is a telemetry stack built around Elasticsearch indexing and Kibana exploration, so observability data lands in a queryable search datastore. It supports metrics, logs, and distributed tracing workflows through Elastic APM Server and Elastic Agents, with OTLP ingestion as an interoperability path.
- +APM Server integrates with Elasticsearch for direct trace and log correlation
- +Fleet-managed Elastic Agent standardizes collection across hosts and Kubernetes
- +Index templates and ingest pipelines provide explicit event shaping and retention control
- +OTLP ingestion supports vendor-neutral telemetry export into Elastic
- –Search-centric storage can increase cost pressure at high cardinality volumes
- –Tailored dashboards and alerts require careful mapping of fields and ECS conventions
- –Scaling ingest for bursts needs capacity planning around shard and pipeline throughput
- –Cross-team governance depends on disciplined role separation and index permission design
Operations and platform teams
Benchmarks with baselines, alerts, and drilldowns
Comparable metrics with tracebacks
Site reliability and operations teams
Searchable logs, metrics, and traces datasets
Common view of health signals
Show 2 more scenarios
Commerce and product engineering teams
Query and rank product or catalog records
Search reflects catalog attributes
Combine filters, aggregations, lexical retrieval, vector retrieval, and custom ranking for search results.
Security operations teams
Investigate audit telemetry with detection rules
Searchable evidence for detection
Search and investigate audit-oriented telemetry using security-focused workflows and detection rules.
Best for: Fits when platform teams want a single Elasticsearch-backed datastore for logs, metrics, and traces with governed indexing and ingestion.
Conclusion
After evaluating 10 observability software, New Relic stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
Frequently Asked Questions About Observability Software
How do Dynatrace and New Relic handle trace-to-log or trace-to-service correlation during incidents?
Which tools provide OTLP ingestion for OpenTelemetry data, and how does that affect pipeline compatibility?
How does Splunk correlate logs, metrics, and traces when telemetry arrives in different schemas?
What breaks if label strategy and cardinality controls are not defined when using Prometheus or Grafana for metrics?
When should SRE teams choose Grafana over a single-vendor observability suite for cross-signal dashboards and automation?
How do Elastic and Sumo Logic differ in how they govern ingestion, parsing, and retention across high-volume telemetry?
What security controls matter most for observability administration across Datadog, Splunk, and Dynatrace?
How do Jaeger and Prometheus handle alerting and sampling behavior for tracing and metrics?
How does data migration typically work when moving from an existing log or trace pipeline into Coralogix or Datadog?
What tradeoff appears when selecting eBPF-based visibility in Dynatrace versus agent-based visibility in other tools?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
How to Choose the Right Observability Software
The real differences show up in data handling and control depth. We focus on how the product manages the Observability API surface and how it turns your signals into usable alerting and dashboards without losing traceability.
Telemetry ingestion paths and routing
Why it matters
Telemetry ingestion paths and routing determine whether your logs, metrics, and traces arrive consistently and land where you expect. When routing is unclear, troubleshooting becomes guesswork.
How to check
Ask for a demo that ingests three signal types and shows where each ends up in the UI. Then test routing by sending the same event with two different service identifiers and confirm it separates cleanly.
Unified data model and service mapping
Why it matters
A unified data model and service mapping keeps related data connected across time and views. Without it, dashboards and investigations often disagree about “what happened.”
How to check
In the demo, locate one service and confirm you can pivot from metrics to logs to traces using the same service identity. Check whether fields like environment and version are normalized or require manual rewriting.
Query language for fast root-cause pivots
Why it matters
Query language for fast root-cause pivots affects how quickly teams narrow from symptoms to causes. If queries require heavy manual steps, investigations slow down.
How to check
Request a walkthrough of a root-cause scenario (for example, latency spike) and watch how many steps it takes to pivot to traces and then to correlated logs. Try changing one filter (service or time range) and ensure results update predictably.
Dashboard provisioning with configuration control
Why it matters
Dashboard provisioning with configuration control matters when teams need repeatable views and versioned changes. Without it, dashboards drift between environments.
How to check
Ask how dashboards are created and whether you can import/export definitions or apply them via automation. Then review how environment variables (like staging vs production) are handled during promotion.
Alert rules with actionable context
Why it matters
Alert rules with actionable context reduce back-and-forth during incidents. Good rules attach the context teams need to decide next actions quickly.
How to check
Review a sample alert and open the event it fired on; confirm the message includes the key fields needed to investigate. Ask whether the tool supports grouping and deduplication so repeated noise doesn’t hide the signal.
RBAC and audit log for changes
Why it matters
RBAC and audit log for changes support governance when multiple teams share the same telemetry space. If changes aren’t traceable, it becomes hard to explain regressions in dashboards or alert behavior.
How to check
In an admin section, check whether roles can limit what users can edit and whether changes generate an audit trail. Ask a demo admin to make a change to a dashboard or alert and show the resulting audit entries.
Red flags
- The UI shows data, but pivots between metrics, logs, and traces don’t use the same service identity.
- Routing and ingestion behavior differs by signal type without clear explanations.
- Dashboard and alert changes lack repeatable import/export or leave no audit trail.
Fit by situation
- If we must standardize how teams send and route telemetry, prioritize telemetry ingestion paths and routing.
- If we need consistent service identity across every investigation, prioritize unified data model and service mapping.
- If multiple teams edit views and alerts, prioritize RBAC and audit log for changes.
Funding and competing interests
This study was co-funded by several of the vendors assessed in it. No single vendor funded it. Funders could propose the market studied.
They had no role in selecting vendors, setting or weighting criteria, analysing data or drawing conclusions, and they received the same factual-accuracy check as every other vendor assessed.
Market definition, inclusion rules and criteria weights were fixed by an editor before research began and did not change afterwards. Editorial process →
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