Top 12 Distributed Tracing Tools in 2026: Complete Comparison & Reviews

Distributed tracing has become essential for modern software teams. As applications evolve into complex distributed systems with microservices, APIs, databases, and third-party integrations, understanding how a single user request travels through your entire stack is no longer optional, it’s critical for maintaining performance, reliability, and user satisfaction.

Whether you’re debugging a slow checkout flow, investigating intermittent errors, or optimizing database queries, the right distributed tracing tool can mean the difference between hours of frustration and minutes to resolution.

In this guide, we compare the best tracing tools available in 2026, evaluating their features, pricing, ease of use, and ideal use cases to help you find the right fit for your team.

What is Distributed Tracing?

Distributed tracing (also called transaction tracing) is the practice of tracking a request as it flows through multiple services, databases, and external calls in a distributed system. Each step in the journey is recorded as a “span,” and these spans are assembled into a complete “trace” that shows the full timeline of the request.

This visibility helps engineering teams:

• Identify performance bottlenecks — See exactly where time is spent and which service or query is causing latency
• Debug errors in context — Understand the complete path leading to a failure, not just isolated error logs
• Map service dependencies — Visualize how services communicate and identify critical paths
• Optimize resource usage — Spot inefficient database queries, N+1 problems, and unnecessary external calls
• Reduce mean time to resolution (MTTR) — Jump directly to the root cause instead of guessing

What to Look for in a Distributed Tracing Tool

Before diving into specific tools, here are the key criteria that matter most when evaluating tracing solutions:

OpenTelemetry Support

OpenTelemetry (OTel) has become the industry standard for instrumentation. Tools that support OTel natively let you avoid vendor lock-in, reuse instrumentation across multiple backends, and take advantage of a massive ecosystem of auto-instrumentation libraries.

Auto-Instrumentation

The best tracing tools offer zero-code or low-code instrumentation for popular languages and frameworks. This dramatically reduces setup time and ensures comprehensive coverage without manual span creation.

Trace-Log-Metric Correlation

Distributed traces are most powerful when correlated with logs and metrics. Look for tools that let you pivot seamlessly from a slow trace to related logs or infrastructure metrics for complete context.

Sampling Strategies

High-volume systems generate millions of spans. Intelligent sampling—head-based, tail-based, or dynamic—helps you capture what matters (errors, slow requests) while controlling costs.

Service Maps & Visualization

Clear service dependency maps and waterfall trace views make it easier to understand complex architectures and quickly identify problematic services.

Pricing Transparency

Distributed tracing costs can spiral quickly at scale. Look for tools with predictable, transparent pricing that won’t surprise you as your traffic grows.

The 12 Best Distributed Tracing Tools in 2026

1. Sematext Tracing

Best for: Teams wanting OpenTelemetry-native tracing with full observability at 50-80% lower cost than competitors

Sematext Tracing delivers enterprise-grade distributed tracing built on OpenTelemetry from day one. It’s designed for teams that want deep visibility into their applications without the complexity or cost of traditional APM platforms, like Datadog, New Relic, Dynatrace, Splunk, etc.

Unlike solutions that bolt on OpenTelemetry support as an afterthought, Sematext was built around open standards. You can start tracing in under 10 minutes using auto-instrumentation for Java, Python, Node.js, Go, .NET, Ruby, and more—no code changes required.

Key Features:

• OpenTelemetry-native architecture — Send traces via OTLP (HTTP or gRPC) using any OpenTelemetry-compatible instrumentation
• Zero-code auto-instrumentation — Automatic tracing for all major languages and frameworks without modifying your codebase
• Powerful trace analysis — Search and filter traces by service, operation, latency, errors, or custom attributes with waterfall visualizations
• Error detection & root cause analysis — Automatically track errors across services with full stack traces in context
• Trace-log-metric correlation — Navigate from traces to related logs and infrastructure metrics with one click
• Intelligent sampling — Configure head-based sampling to always capture errors and high-latency requests while reducing costs
• RED metrics out of the box — Automatically generate Rate, Error, and Duration metrics from trace data
• Database query insights — Identify slow queries, N+1 problems, and connection pool issues directly from traces

Pros:

• 50-80% cheaper than Datadog or New Relic with no compromise on features (see comparison below)
• ClickHouse-powered backend delivers real-time trace analysis at any scale, no query slowdowns as data grows
• True OpenTelemetry-native design eliminates vendor lock-in
• Overview dashboard showing service performance, error rates, and trace volume for the selected time period
• Works with existing Jaeger, Zipkin, or any OTLP-compatible instrumentation
• Unified platform combining logs, metrics, and traces in one UI
• Out of the box alerts of most common use cases
• Simple, transparent per-span pricing with no hidden fees
• Fast onboarding with comprehensive SDKs for all major platforms including mobile (iOS, Android)

Cons:

• Service maps coming soon (currently visualize dependencies through distributed traces)
• Smaller brand recognition than enterprise incumbents

Pricing: Starts at $19 per month for 2 million spans. 14-day free trial, no credit card required. Typically 50-80% less expensive than Datadog or New Relic at scale.

Best For: DevOps teams, SREs, and engineering organizations that want full-featured distributed tracing without the enterprise price tag. Ideal for teams already using or planning to adopt OpenTelemetry.

Get started with Sematext Tracing →

2. Datadog APM

Best for: Enterprises with deep pockets already invested in the Datadog ecosystem

Datadog APM provides distributed tracing as part of its comprehensive observability platform. It’s known for its extensive integrations and polished user experience, though pricing can become substantial at scale.

Key Features:

• Distributed tracing with service maps and flame graphs
• Automatic instrumentation for most languages
• Continuous profiler for code-level insights
• Integration with logs, metrics, and 800+ technologies
• Watchdog AI for anomaly detection

Pros:

• Mature, feature-rich platform with excellent UX
• Strong integration ecosystem
• Unified observability across infrastructure, APM, logs, and RUM
• AI-powered anomaly detection

Cons:

• Pricing complexity with multiple SKUs that add up quickly
• Can become very expensive at scale (users frequently report “bill shock”)
• Proprietary agents alongside OTel support

Pricing: APM starts at $31 per host per month. Additional charges for indexed spans, continuous profiling, and other features. Costs can escalate significantly with volume.

Best For: Large enterprises with budget flexibility that want a comprehensive, all-in-one observability platform and are already using other Datadog products.

3. New Relic

Best for: Teams wanting unlimited users with usage-based pricing

New Relic offers distributed tracing as part of its full-stack observability platform. Its pricing model based on data ingestion and user tiers can be cost-effective for some teams – see further down for the specifics.

Key Features:

• End-to-end distributed tracing with service maps
• Logs in context correlating traces with log events
• Error analytics with real-time alerts
• Browser and mobile monitoring for frontend-to-backend visibility
• Strong JVM and .NET monitoring capabilities

Pros:

• Unlimited basic users included
• Comprehensive APM with deep language runtime insights
• Good transaction-level tracing detail
• Free tier available (100GB/month)

Cons:

• Pricing complexity with user types (Basic, Core, Full)
• Can be expensive for teams needing many “Full” users
• Learning curve for the full platform

Pricing: Free tier includes 100GB/month. Paid plans from $0.30/GB for data plus per-user fees for full-access users. Complex pricing structure requires careful planning.

Best For: Teams that want a mature APM solution with a free tier and don’t mind complexity in pricing models. Good for organizations with many developers who only need basic access.

 

4. Dynatrace

Best for: Large enterprises requiring automatic discovery and AI-driven insights

Dynatrace is an enterprise observability platform known for its automatic instrumentation and AI-powered root cause analysis. Its PurePath technology automatically captures every transaction end-to-end.

Key Features:

• PurePath technology for automatic end-to-end trace capture
• Davis AI for automatic root cause detection
• Full-stack observability including infrastructure and cloud
• Automatic service discovery and dependency mapping
• Code-level visibility with method-level insights

Pros:

• Truly automatic instrumentation with minimal configuration
• Excellent for dynamic Kubernetes and microservices environments
• AI-driven anomaly detection and root cause analysis
• Strong enterprise governance and security features

Cons:

• Premium pricing significantly higher than alternatives
• Steep learning curve with 15+ different “apps” in the UI requiring significant onboarding investment
• OneAgent’s full-stack instrumentation (code-level, infrastructure, network) consumes more memory (typically 200-500MB) than lightweight OTel-based alternatives

Pricing: Contact sales for pricing. Generally considered one of the most expensive options in the market, targeting large enterprise budgets.

Best For: Large enterprises with complex, dynamic environments (Kubernetes, microservices, multi-cloud) that need automatic discovery and AI-assisted troubleshooting and have the budget to match.

 

5. Jaeger

Best for: Teams wanting open-source, self-hosted tracing with OpenTelemetry

Jaeger is an open-source, CNCF-graduated distributed tracing platform originally developed at Uber. It’s a battle-tested solution that integrates seamlessly with OpenTelemetry.

Key Features:

• Native OpenTelemetry support (Jaeger v2 is OpenTelemetry-native)
• Multiple storage backends: Cassandra, Elasticsearch, OpenSearch, ClickHouse
• Service dependency graphs
• Adaptive sampling
• Self-hosted with full data control

Pros:

• Completely free and open-source
• No vendor lock-in with full data ownership
• Mature, production-proven at massive scale
• Active community and CNCF backing
• Flexible storage options

Cons:

• Requires operational expertise to run and scale
• UI is functional but less polished than commercial tools
• No built-in log/metric correlation (need additional tools)
• Limited alerting capabilities out of the box

Pricing: Free (open-source). Infrastructure costs for self-hosting vary based on scale and storage backend.

Best For: Teams with Kubernetes expertise who want complete control over their tracing infrastructure and data, and have the operational capacity to manage self-hosted systems.

 

6. Zipkin

Best for: Simple, lightweight distributed tracing with minimal overhead

Zipkin is one of the original open-source distributed tracing systems, inspired by Google’s Dapper paper. It remains actively maintained and offers a straightforward approach to tracing.

Key Features:

• Lightweight architecture with low overhead
• Support for most major programming languages
• In-memory, Cassandra, or Elasticsearch storage
• Simple, clean UI for trace visualization
• W3C Trace Context support

Pros:

• Very lightweight and easy to get started
• Broad language support with instrumentation libraries
• Straightforward architecture easy to understand
• Good for resource-constrained environments
• Active maintenance with security updates

Cons:

• Fewer advanced features than modern alternatives
• Basic UI compared to commercial tools
• No built-in metrics or log integration
• Limited sampling options

Pricing: Free (open-source). Infrastructure costs for self-hosting.

Best For: Teams new to distributed tracing who want a simple, lightweight solution, or organizations with resource constraints that need minimal overhead.

 

7. Grafana Tempo

Best for: Teams already using Grafana with Prometheus and Loki

Grafana Tempo is an open-source, high-scale distributed tracing backend designed to work seamlessly with Grafana’s observability stack. It’s optimized for cost-effective trace storage.

Key Features:

• Object storage backend (S3, GCS, Azure) for cost efficiency
• Native integration with Grafana, Loki, and Prometheus
• TraceQL query language for powerful trace analysis
• OpenTelemetry and Jaeger/Zipkin protocol support
• Exemplar linking between metrics and traces

Pros:

• Extremely cost-effective storage using object storage
• Excellent Grafana integration for unified visualization
• Strong correlation with Prometheus metrics via exemplars
• OpenTelemetry-native
• Scales to massive trace volumes

Cons:

• Requires Grafana ecosystem investment
• Less intuitive standalone than purpose-built tracing UIs
• Steeper learning curve for TraceQL

Pricing: Free (open-source). Grafana Cloud offers hosted Tempo with usage-based pricing starting from free tier.

Best For: Teams already invested in the Grafana ecosystem (Prometheus, Loki) who want tightly integrated tracing without high storage costs.

 

8. SigNoz

Best for: Open-source alternative to Datadog with traces, metrics, and logs

SigNoz is a modern open-source observability platform providing distributed tracing, metrics, and logs in a single tool. It’s often positioned as an open-source alternative to Datadog.

Key Features:

• Unified traces, metrics, and logs in one platform
• ClickHouse backend for fast queries at scale
• OpenTelemetry-native design
• Service maps and flame graphs
• Alerts and dashboards included

Pros:

• Full observability stack in one open-source tool
• No vendor lock-in with OpenTelemetry
• Active development and growing community
• Both self-hosted and cloud options available
• Cost-effective compared to commercial alternatives

Cons:

• Younger project than some alternatives
• Smaller community than Jaeger or Grafana
• Some enterprise features still maturing

Pricing: Free (open-source self-hosted). SigNoz Cloud starts at $199/month with usage-based pricing.

Best For: Teams wanting a modern, open-source observability platform that combines traces, metrics, and logs without stitching together multiple tools.

 

9. Elastic APM

Best for: Organizations already using the Elastic Stack (ELK)

Elastic APM provides distributed tracing as part of Elastic Observability. It leverages Elasticsearch for storage and integrates tightly with Kibana for visualization.

Key Features:

• End-to-end distributed tracing with W3C Trace Context
• OpenTelemetry ingest via OTLP
• Trace correlation with logs and metrics in Elasticsearch
• Service maps and transaction timelines
• RUM agents for browser tracing

Pros:

• Deep integration with Elastic Stack
• Powerful search and analytics via Elasticsearch
• Flexible deployment (self-hosted or Elastic Cloud)
• Strong log correlation if already using ELK

Cons:

• Complexity of managing Elasticsearch at scale
• Resource-intensive compared to purpose-built solutions
• Pricing can be complex for Elastic Cloud

Pricing: Free tier available. Elastic Cloud pricing is based on deployment size and features. Self-hosted is free but requires infrastructure and maintenance.

Best For: Organizations already invested in the Elastic Stack who want to add APM without introducing new technologies.

 

10. Splunk APM

Best for: Enterprises using Splunk for security and log analytics

Splunk APM (formerly SignalFx) provides full-fidelity distributed tracing with no sampling required, capturing every transaction for high-fidelity analysis.

Key Features:

• Full-fidelity trace ingestion (no sampling)
• AI-powered root cause analysis
• Service maps with real-time dependency visualization
• NoSample technology for complete data capture
• Integration with Splunk’s security and log platform

Pros:

• Full-fidelity tracing captures every request
• Strong correlation with Splunk logs and SIEM
• AI-assisted troubleshooting
• Good for compliance-heavy environments

Cons:

• Very expensive, especially for full-fidelity ingestion
• Complex pricing model
• Can be overkill for smaller deployments

Pricing: Contact sales. Generally positioned at the premium end of the market.

Best For: Large enterprises already using Splunk for security/logging who need complete trace capture for compliance or forensic analysis.

 

11. Honeycomb

Best for: Teams practicing observability-driven development with high-cardinality data

Honeycomb pioneered the “observability” approach, focusing on high-cardinality data exploration rather than traditional APM metrics. It’s designed for debugging unknown-unknowns.

Key Features:

• High-cardinality data support without limits on dimensions
• BubbleUp for automatic pattern detection
• Query-based exploration of trace data
• OpenTelemetry support
• Collaborative debugging workflows

Pros:

• Excellent for exploring unknown issues
• No cardinality limits on trace attributes
• Strong developer experience
• BubbleUp makes patterns visible quickly

Cons:

• Different mental model from traditional APM
• Less focus on pre-built dashboards
• Pricing can be significant at scale

Pricing: Free tier available. Team plan from $130/month. Usage-based pricing for events ingested.

Best For: Engineering teams practicing modern observability who need to debug complex, novel issues in distributed systems with high-cardinality data.

 

12. AWS X-Ray

Best for: Teams running primarily on AWS

AWS X-Ray provides distributed tracing for applications running on AWS services. It integrates natively with Lambda, ECS, EKS, and other AWS offerings.

Key Features:

• Native integration with AWS services (Lambda, API Gateway, ECS, EKS)
• Service maps showing AWS resource relationships
• Sampling rules for cost control
• Integration with CloudWatch for logs and metrics
• X-Ray SDK and OpenTelemetry support

Pros:

• Seamless integration with AWS ecosystem
• Low barrier to entry for AWS-native applications
• Pay-per-use pricing
• No infrastructure to manage

Cons:

• Limited for hybrid/multi-cloud environments
• Less powerful analysis than dedicated APM tools
• Basic compared to commercial alternatives
• Adds overhead when tracing non-AWS services

Pricing: Pay per trace recorded ($5.00 per million traces) and retrieved ($0.50 per million traces). Free tier includes 100,000 traces recorded per month. No other tool has retrieval-based cost.

Best For: Teams running primarily AWS-native workloads (Lambda, ECS, EKS) who want basic tracing without additional vendors.

Distributed Tracing Tools Comparison Table

Tool	OpenTelemetry Support	Self-Hosted Option	Log/Metric Correlation	Pricing Model	Best For
Sematext Tracing	Native	No (SaaS)	Yes	Per-span	Cost-conscious teams wanting full features
Datadog APM	Yes	No	Yes	Per-host + spans	Enterprises in Datadog ecosystem
New Relic	Yes	No	Yes	Per-GB + users	Teams wanting unlimited basic users
Dynatrace	Yes	Yes	Yes	Custom	Large enterprises needing AI
Jaeger	Native	Yes	No	Free	Self-hosted Kubernetes teams
Zipkin	Yes	Yes	No	Free	Simple, lightweight needs
Grafana Tempo	Native	Yes	Yes	Free/Usage	Grafana ecosystem users
SigNoz	Native	Yes	Yes	Free/Usage	Open-source full-stack observability
Elastic APM	Yes	Yes	Yes	Deployment-based	Elastic Stack users
Splunk APM	Yes	Yes	Yes	Custom	Splunk enterprises
Honeycomb	Yes	No	Partial	Per-event	High-cardinality exploration
AWS X-Ray	Yes	No	Yes (CloudWatch)	Per-trace	AWS-native workloads

 

Note on OpenTelemetry Support: “Native” means the tool was built from the ground up on OpenTelemetry. OTel is the primary instrumentation method with no proprietary agents to choose between. “Yes” means the tool supports OpenTelemetry but also offers proprietary agents that may provide additional features or be the vendor’s recommended path. This distinction matters for vendor lock-in: with Native tools, switching backends later is straightforward since you’re already on the open standard, while Yes tools often steer teams toward proprietary instrumentation that makes future migration harder.

How to Choose the Right Distributed Tracing Tool

Consider Your Stack and Scale

If you’re running on AWS, X-Ray offers easy integration but limited features. If you’re already invested in Grafana, Tempo makes sense. For Kubernetes-native deployments with internal expertise, Jaeger or SigNoz provide open-source flexibility if you cannot use SaaS or prefer hosting your own observability infrastructure.

Cost Transparency

Observability costs can spiral unexpectedly, especially with tracing where span volumes grow with traffic. Look for tools with clear, predictable pricing models, ideally based on data ingested rather than complex combinations of hosts, users, and feature tiers. Sematext’s per-span pricing let you forecast costs accurately, while tools like Datadog and Dynatrace often require sales conversations and can surprise teams with “bill shock” as usage scales. Before committing, model your expected trace volume and get concrete pricing at your anticipated scale, not just starter tier rates.

Evaluate Total Cost of Ownership

Commercial tools like Datadog and Dynatrace offer polished experiences but can become expensive quickly. Calculate your expected trace volume and compare pricing at your scale—many teams find 50-80% savings with alternatives like Sematext while retaining full functionality. When considering self-hosted option take into account both infrastructure and human costs.

Prioritize OpenTelemetry Support

Vendor lock-in is a real concern. Tools with native OpenTelemetry support let you change backends without re-instrumenting your applications. Sematext, Jaeger, Tempo, and SigNoz are all OpenTelemetry-native.

Assess Correlation Capabilities

Traces alone aren’t enough. The ability to quickly pivot from a slow trace to related logs and infrastructure metrics dramatically speeds up troubleshooting. Look for unified platforms or strong integrations.

Start with a Trial

Most commercial tools offer free trials. Test your actual workloads to evaluate performance, usability, and cost before committing.

Conclusion

Distributed tracing is no longer optional for teams operating distributed systems. The right tool gives you visibility into every request, helping you debug faster, optimize performance, and improve user experience.

For teams seeking a balance of features, OpenTelemetry-native design, and cost-effectiveness, Sematext Tracing stands out as the top choice in 2026. It delivers enterprise-grade distributed tracing at 50-80% lower cost than incumbents like Datadog and New Relic, with no compromise on functionality.

If you’re already invested in specific ecosystems (Datadog, Grafana, Elastic, AWS), those platform-native options may make sense. For teams wanting full control, open-source tools like Jaeger or SigNoz provide flexibility at the cost of operational overhead.

Whatever you choose, prioritize OpenTelemetry support to future-proof your instrumentation investment and avoid vendor lock-in.

Ready to get started with modern distributed tracing? Try Sematext Tracing free for 14 days—no credit card required.

FAQ

What’s the difference between transaction tracing and distributed tracing?

These terms are often used interchangeably. Transaction tracing typically refers to tracking a single business transaction (like a purchase) through all its steps, while distributed tracing is the broader practice of tracking requests across distributed services. Modern tools handle both use cases.

Do I need to modify my code to use distributed tracing?

Not necessarily. Most modern tracing tools offer auto-instrumentation that captures traces without code changes. You simply add an agent or library to your deployment configuration. Custom spans for business-specific operations do require code changes.

How much does distributed tracing cost?

Costs vary dramatically. Open-source tools (Jaeger, Zipkin, Tempo) are free but require infrastructure. Commercial tools range from $0.007/10K spans (Sematext) to $31+/host/month (Datadog) plus additional charges. At scale, the difference between tools can be 50-80% of your budget.

Which distributed tracing tool supports OpenTelemetry?

All major tools now support OpenTelemetry to varying degrees. Sematext, Jaeger, Tempo, and SigNoz are OpenTelemetry-native. Datadog, New Relic, Dynatrace, and others support OTel alongside their proprietary agents.

Can I use multiple tracing tools together?

Yes, with OpenTelemetry you can send the same trace data to multiple backends. This is useful during migrations or for comparing tools. The OpenTelemetry Collector supports multiple exporters simultaneously.