You’re here because Zipkin is showing its age. It was a foundational tool for distributed tracing, no doubt. But in 2025, in a world of polyglot microservices, Kubernetes, and the expectation of high-performance, low-overhead observability, the cracks are starting to show. You’re probably wrestling with its monolithic Java architecture, the hassle of manual instrumentation, or the constant fear of losing traces.
You need something better. Something that doesn’t just “support” OpenTelemetry but is built for it. Something that gives you powerful analytics without requiring a Ph.D. in a proprietary query language. And something that won’t punish you with a surprise bill for having the audacity to scale.
This is a no-nonsense guide to the best Zipkin alternatives on the market. We’ll look at the big players and the open-source heroes, break down what they’re good at, expose the “catch”, and give you a straight-up verdict to help you decide.
1. Dash0
Dash0 is an OpenTelemetry-native observability platform built for teams who want to move fast without getting locked into a proprietary ecosystem. It’s designed from the ground up to unify logs, metrics, and traces, leveraging open standards to give you full control over your data and costs. It’s for cloud-native teams who see observability as a core engineering practice, not just a dashboard you look at when things are on fire.
What’s good
- Truly OpenTelemetry-Native. Dash0’s entire architecture is built on the OTel data model. This means no data mapping, no lost context, and full support for all signals and their relationships, like trace contexts in logs. It uses OTel’s resource concept to tie everything together, so you get a unified view of every service, pod, or host.
- The SIFT Framework. Dash0 introduces a clever framework called SIFT (Spam removal, Improve telemetry, Filtering and grouping, Triage). The best part is Triage, an automated root cause analysis feature that uses statistical analysis to compare datasets and highlight the probable cause of errors and outliers, saving you from manual guesswork.
- Zero Lock-In Philosophy. Dash0 is serious about preventing vendor lock-in. It uses PromQL for all signals (logs, metrics, and traces), so your team’s existing knowledge is reusable. Dashboards are built on the open-source tool Perses, and alerts use the Prometheus standard, so you can take them with you if you leave. Instrumentation is pure OpenTelemetry, so switching vendors is just a config change.
- Transparent, Predictable Pricing. The pricing model is simple: you pay for the number of logs, spans, and metric data points you send. That’s it. No per-user fees, no penalties for data volume (GB), and no complex tiers. This encourages you to send rich metadata without worrying about your bill exploding. Built-in dashboards give you real-time visibility into your costs.
The catch
Dash0 is a newer player compared to the goliaths like Datadog or the long-established open-source projects. If you’re looking for a single vendor to replace every security, and business intelligence tool you have, Dash0’s focused approach on core observability might not fit that “everything-and-the-kitchen-sink” requirement.
The verdict
Dash0 is the top choice for modern, cloud-native teams who are invested in OpenTelemetry and want a powerful, unified observability platform without the vendor lock-in and pricing headaches of the old guard. It hits the sweet spot of powerful, AI-driven analytics and a commitment to open standards. If you’re building on Kubernetes and Prometheus and feel the pain of siloed data and unpredictable costs, Dash0 is built for you.
Start your 14-day free trial of Dash0 today!
2. Grafana Tempo
Grafana Tempo is Grafana Labs’ answer to distributed tracing. It’s an open-source, high-scale distributed tracing backend designed to be tightly integrated with Grafana, Loki (for logs), and Prometheus/Mimir (for metrics). Its main goal is to be a simple, cost-effective way to store and query massive volumes of traces.
What’s good
- Deep Integration with Grafana. If you’re already living in the Grafana ecosystem, Tempo is a natural fit. Its killer feature is the trace-to-logs integration, allowing you to jump directly from a span in Tempo to the relevant logs in Loki with a single click, assuming your labeling is consistent.
- Massively Scalable and Cost-Effective. Tempo’s architecture is built for scale. It only indexes the metadata needed to find traces (the trace ID) and uses object storage (like S3 or GCS) for the bulk trace data, which is incredibly cheap. This makes it one of the most cost-effective Zipkin tracing alternatives for storing huge volumes of traces.
- Open-Source and OTel-Native. Tempo is open-source and designed to ingest traces from open standards like OpenTelemetry, Jaeger, and Zipkin. This aligns well with a “no lock-in” philosophy.
The catch
- Search is Limited by Design. The main trade-off for Tempo’s cost-effectiveness is its search capability. By default, you can only search for traces by trace ID. To search by other attributes (like a service name, HTTP status, or customer ID), you need to either pipe your traces through a separate service that indexes that metadata or rely on finding the trace ID from a linked log or metric first. This is a major workflow disruption compared to tools that allow arbitrary attribute searches.
- Operational Overhead. While Grafana Cloud offers a managed version, self-hosting the full LGTM (Loki, Grafana, Tempo, Mimir) stack is a massive undertaking. You’re responsible for deploying, scaling, and maintaining four separate distributed systems, which requires significant SRE expertise.
- Fragmented Experience. While the integration is good, it’s still a composable stack, not a truly unified platform. Achieving seamless correlation relies entirely on you maintaining perfect label discipline across all signals. The user experience can feel less cohesive than a platform where all signals reside in a single database.
The verdict
Grafana Tempo is an excellent choice for organizations that are deeply committed to the Grafana open-source ecosystem and whose primary requirement is storing massive volumes of traces for a low cost. It’s ideal if your workflow primarily involves finding a trace ID from a log or metric and then looking it up. However, if your team needs to perform exploratory analysis by searching and filtering across arbitrary trace attributes, Tempo’s design will be a significant limitation.
3. Honeycomb
Honeycomb is a SaaS platform that pioneered the concept of “observability” and has a laser focus on enabling fast analysis of high-cardinality, high-dimensionality data. It was built from the ground up to analyze “wide events”—traces and structured logs with arbitrary context—to help engineers debug complex, unpredictable problems in production.
What’s good
- Built for High-Cardinality. This is Honeycomb’s superpower. Its query engine is designed to let you slice and dice your data by any attribute—customer_id, build_id, feature_flag—without performance degradation or cost penalties. This is perfect for debugging those “unknown unknown” issues.
- Intuitive Querying and BubbleUp. Honeycomb doesn’t require you to learn a complex query language. Its visual query builder is intuitive, and its standout feature, BubbleUp, automatically compares an outlier group to the baseline and highlights the exact attributes that are different. It’s an incredibly fast way to find the needle in the haystack.
- Predictable, Event-Based Pricing. The pricing model is simple: you pay based on the number of events (spans, structured logs) you send per month. There are no charges for users, cardinality, or custom metrics, which users overwhelmingly praise for its predictability and transparency.
The catch
- Hyper-Focused on Events and Traces. Honeycomb’s strength is also its limitation. It is not an all-in-one monitoring tool. Its capabilities for traditional infrastructure monitoring (basic host metrics) or handling unstructured text logs are less mature than dedicated tools. It also lacks features like synthetic monitoring entirely.
- Requires a Mindset Shift. Teams accustomed to a traditional, metric-centric monitoring approach need to adapt to an event-based, high-cardinality investigation workflow. There’s a learning curve associated with this different way of thinking about data.
The verdict
Honeycomb is the ideal tool for developer-centric teams with complex microservices architectures who have embraced an observability-driven culture. If your primary goal is to empower engineers to rapidly debug novel production issues by exploring rich, high-cardinality data, Honeycomb is one of the best Zipkin tracing alternatives you can choose. Its BubbleUp feature is a game-changer for root cause analysis.
4. Datadog
Datadog is the 800-pound gorilla in the observability market. It’s a massive, unified SaaS platform that aims to be the “single pane of glass” for everything: infrastructure monitoring, APM, log management, RUM, synthetics, security, and more. It has grown organically to cover a vast number of use cases and boasts over 900 integrations.
What’s good
- Massive Feature Breadth. Datadog’s most compelling strength is the sheer number of things it can do. For large enterprises that want to consolidate vendors, having infrastructure, APM, logging, and security tools in one platform can be operationally simpler than managing multiple point solutions.
- Polished User Experience. For its core features, the user experience is very polished. The dashboarding is extremely powerful, with drag-and-drop widgets and a huge library of pre-built dashboards. The Watchdog AI engine also automatically surfaces anomalies without requiring manual configuration.
- Easy Initial Setup. Users report that getting started with the proprietary Datadog Agent and enabling features like APM tracing is “laughably easy.” This low-friction data collection is a major plus for teams that want to get up and running quickly.
The catch
- Punitive and Complex Pricing. This is Datadog’s Achilles’ heel. The pricing model is notoriously complex and leads to frequent “surprise bills.” You’re charged on multiple vectors: per-host, per-GB ingested, for indexing logs, and, critically, for “custom metrics.”
- The “OpenTelemetry Tax.” Datadog is not OTel-native. It treats all OpenTelemetry metrics as “custom metrics,” which are billed at a premium rate. This creates a direct financial penalty for adopting open standards, forcing you to choose between portability and cost. This is the very definition of vendor lock-in.
- Overwhelmingly Complex. While the initial setup is easy, mastering the platform is not. The UI is often described by users as “overwhelming” and “intimidating” due to the sheer number of features, leading to a steep learning curve.
The verdict
Datadog is a powerful platform, but it’s a poor choice for any team committed to an OpenTelemetry-native strategy or operating on a constrained budget. The “OTel tax” is fundamentally hostile to open standards, and the complex pricing model creates unacceptable financial risk. It’s best suited for large enterprises that prioritize feature breadth above all else and have a dedicated finance team to manage the complex billing.
5. Dynatrace
Dynatrace is another major player in the all-in-one observability market, competing directly with Datadog and New Relic. Its core differentiator is a heavy focus on AI and automation. The entire platform is built around its causal AI engine, “Davis,” which aims to provide not just data, but “answers” by performing automated root cause analysis.
What’s good
- AI-Powered Root Cause Analysis. Dynatrace’s main selling point is its Davis AI engine. It leverages a real-time topology map (Smartscape) to understand cause-and-effect relationships, allowing it to deliver precise, automated root-cause analysis instead of just correlating events.
- Zero-Touch, Automated Instrumentation. The OneAgent technology is a huge strength. A single agent deployment can automatically discover all processes, services, and infrastructure components, dramatically reducing the manual configuration overhead associated with monitoring complex systems.
- First-Class OpenTelemetry Support. Unlike some incumbents, Dynatrace has invested in providing first-class support for OpenTelemetry data ingestion, allowing teams to use open standards without being penalized.
The catch
- Very Expensive. Like Datadog, Dynatrace is a premium-priced, enterprise-grade solution. User reviews frequently highlight the high cost as a significant drawback, making it a non-starter for startups or budget-conscious teams. The median contract value is over $221,000.
- Steep Learning Curve and Complexity. The platform’s immense power comes with significant complexity. Users often report that the vast number of options is confusing and that mastering the platform and its proprietary query language (DQL) takes a long time.
- The “Black Box” Problem. For teams that have a strong culture of hands-on, query-driven investigation, the highly automated, AI-driven approach can feel like a “black box.” If your engineers prefer powerful tools over automated answers, Dynatrace’s philosophy may not be a good cultural fit.
The verdict
Dynatrace is best suited for large enterprises, particularly in regulated industries, that prioritize automation and AI-driven answers over manual data exploration. If you want a platform to act as an intelligent partner that automatically finds and explains the root cause of issues, and you have the budget for it, Dynatrace is a top contender. It’s a poor fit for teams that want hands-on control or are budget-constrained.
6. Lightstep by ServiceNow
Lightstep, now part of ServiceNow, was one of the early innovators in the distributed tracing space, founded by engineers who worked on Google’s Dapper. It has since evolved into a broader observability platform, but its core strength remains in providing deep, trace-based analysis for complex microservice environments.
What’s good
- Deep Root Cause Analysis. Lightstep was built for deep, trace-based analysis. It captures full-context distributed traces and provides tools to analyze performance in complex systems, helping teams quickly understand the impact of changes.
- OpenTelemetry Heritage. Lightstep has been a major contributor to and proponent of OpenTelemetry. Its architecture and data model are well-aligned with open standards, which is a major plus for teams looking to avoid lock-in.
- Integration with ServiceNow. For enterprises already heavily invested in the ServiceNow ecosystem for IT Service Management (ITSM), the integration of Lightstep’s observability data into those workflows can be a powerful advantage, connecting production performance directly to business operations.
The catch
- Acquisition Uncertainty. Being acquired by a large enterprise like ServiceNow always introduces uncertainty. The product’s roadmap and focus can shift to better align with the parent company’s broader strategy, which may or may not benefit the original user base.
- Enterprise Focus and Pricing. As part of ServiceNow, the target audience is squarely in the enterprise space. The pricing is custom and contract-based, which typically means it’s not a fit for smaller teams or startups.
- Less of a Standalone Player. The primary value proposition is now tied to its place within the larger ServiceNow platform. For teams who are not ServiceNow customers, it may be a less compelling choice compared to other standalone observability platforms.
The verdict
Lightstep is an ideal choice for large enterprises that are existing ServiceNow customers and are looking to add deep, trace-based observability that integrates tightly with their ITSM workflows. Its strong OpenTelemetry foundation is a plus, but its future is now intrinsically linked to ServiceNow’s strategy. For teams outside that ecosystem, other developer-centric tracing tools may offer a more focused and accessible solution.
7. SigNoz
SigNoz is an open-source, OpenTelemetry-native observability platform that is explicitly positioned as a direct alternative to Datadog and New Relic. It provides a unified experience for logs, metrics, and traces in a single application, using ClickHouse as its backend for high-performance analytics.
What’s good
- Open-Source & OTel-Native. This is SigNoz’s core value proposition. It offers a Datadog-like unified experience but on a completely open-source and OpenTelemetry-native foundation. This combination avoids vendor lock-in while providing the convenience of an integrated tool.
- Cost-Effective. By using the high-performance ClickHouse database as its backend, SigNoz can offer fast analytics on large data volumes at a lower infrastructure cost. Its cloud pricing is simple, usage-based, and highly competitive, with no per-user or per-host fees.
- Flexible Deployment. You can choose between a fully managed SigNoz Cloud offering or a self-hosted open-source version, giving you flexibility based on your team’s needs and expertise.
The catch
- Less Mature Feature Set. As a younger project, SigNoz lacks some of the more polished, enterprise-grade features (like advanced RBAC or extensive compliance certifications) found in the market titans. Its UI/UX is functional but may not be as refined as a mature commercial product.
- Lacks Breadth. It is highly focused on the core three pillars of observability (logs, metrics, traces). It does not have the broader feature set of a Datadog, lacking mature RUM, Synthetic Monitoring, or SIEM capabilities.
The verdict
SigNoz is a fantastic choice for startups and engineering teams who want an all-in-one observability experience but are committed to open-source and OpenTelemetry. It’s a perfect fit for teams that like the idea of Datadog but are put off by its cost and proprietary nature. If you want a cost-effective, open-source unified platform, SigNoz is one of the strongest Zipkin alternatives available.
8. Uptrace
Uptrace is another open-source, OpenTelemetry-native observability tool. It’s built with a focus on simplicity and developer experience, using ClickHouse as its backend database. It aims to provide a powerful yet easy-to-use platform for logs, metrics, and traces, with a particular strength in its tracing UI.
What’s good
- Developer-Focused and Simple. Uptrace is designed with developers in mind. Its UI is clean, and the focus is on providing a straightforward experience for troubleshooting with OpenTelemetry data. The installation and setup are designed to be simple.
- Open-Source and OTel-Native. Like SigNoz, Uptrace is built on an open-source, OTel-native foundation. This provides all the benefits of avoiding lock-in and using open standards for instrumentation.
- Efficient ClickHouse Backend. By using ClickHouse, Uptrace can offer high-performance queries on large datasets, making it an efficient option for storing and analyzing telemetry data.
The catch
- Very Young Project. Uptrace is one of the newer players in the space. This means it has a smaller community, less brand recognition, and a less mature feature set compared to more established tools.
- Limited Scope. Similar to other newer open-source tools, its focus is primarily on the core signals. It doesn’t offer the broad range of features like RUM, Synthetics, or security monitoring that you’d find in the large enterprise platforms.
The verdict
Uptrace is a solid contender for individual developers or small teams who are looking for a simple, open-source, and OTel-native tool to get started with distributed tracing. Its developer-focused approach and simple setup are appealing. However, larger teams or enterprises may find it lacks the maturity, feature breadth, and enterprise support they require. It’s a promising project to watch in the open-source observability space.
9. New Relic
New Relic is one of the original pioneers in the APM space and has evolved into a comprehensive, all-in-one observability platform. It offers a unified solution covering APM, infrastructure, logging, RUM, and more, all built on a unified telemetry database (NRDB). Its marketing heavily emphasizes a simplified pricing model and “Intelligent Observability.”
What’s good
- Generous Free Tier. New Relic’s free tier is a major strength. It offers 100 GB of data ingest per month and one full-platform user for free, providing a powerful, frictionless entry point for developers and small teams.
- Simplified Pricing Concept. In a market full of complex pricing, New Relic has strategically moved to a simpler model based on just two main vectors: data ingest and users. This is designed to be more predictable than the multi-vector pricing of competitors.
- Strong APM Capabilities. With its deep roots in APM, New Relic maintains a core strength in providing deep, code-level performance insights and correlating frontend user experience with backend services.
The catch
- Cost Remains a Major Complaint. Despite the “simplified” model, cost is still the most frequent complaint from users at scale. The per-user pricing for “Full Platform” access can become prohibitively expensive for large teams, and the company has been actively increasing its data ingest prices.
- Risk of “Unethical Billing.” The platform’s reputation took a hit from a widely-publicized Reddit thread alleging “unethical billing,” where a user’s bill skyrocketed due to logs generated by the New Relic agent itself. This indicates that bill shock is still a significant risk.
- Steep Learning Curve. The platform’s power comes with inherent complexity. Users report a steep learning curve and a cluttered UI, indicating that mastering its full capabilities requires a significant time investment.
The verdict
New Relic is a strong choice for teams that want a powerful, feature-rich APM and observability platform and are attracted by its generous free tier. It’s a viable all-in-one alternative for those trying to escape the even more complex pricing of Datadog. However, organizations must be wary of the high costs at scale, particularly the per-user fees, and the potential for unexpected billing issues.
10. Splunk
Splunk is a titan in the world of machine data, a powerhouse primarily known for its legendary log management and SIEM capabilities. Its observability offering, the Splunk Observability Cloud, is a modern, OpenTelemetry-native suite that includes APM and infrastructure monitoring, but it’s often sold to organizations already invested in the core Splunk platform for logging and security.
What’s good
- Unmatched Log Search and Analytics. Splunk’s core strength is its search engine. The Splunk Search Processing Language (SPL) is an incredibly powerful tool for deep investigation and analysis of massive, unstructured datasets, and it is widely regarded as the gold standard for log analysis.
- OpenTelemetry-Native Observability Cloud. The modern Splunk Observability Cloud was built to be OTel-native, using its own distribution of the OTel Collector. A standout feature is its NoSample™ tracing, which captures 100% of trace data, eliminating sampling blind spots.
- Strong for Existing Splunk Customers. For enterprises already heavily invested in Splunk for logging and SIEM, adding the Observability Cloud is a natural extension. The Log Observer Connect feature provides a seamless bridge between traces in the Observability Cloud and the powerful log analytics in the core Splunk platform.
The catch
- Extremely High Cost. Splunk is notoriously expensive. The licensing model for the core platform, based on peak daily data ingest, is prohibitive for most companies. The total cost of ownership must also factor in the cost of the underlying Splunk platform license required for full log capabilities.
- Separated Log Data Store. The primary architectural weakness is that logs and traces live in different backends. While Log Observer Connect provides a bridge, this separation introduces complexity and potential latency compared to platforms where all telemetry resides in a single, unified database.
- Steep Learning Curve. Mastering the powerful but complex SPL requires significant time and expertise. This isn’t a tool you can just pick up and use effectively without dedicated training.
The verdict
Splunk Observability Cloud is a powerful and logical choice for large enterprises that are already committed Splunk customers for log management and security. For these organizations, it offers a modern, OTel-native tracing solution that integrates with their existing investment. However, for any company not already in the Splunk ecosystem, the high cost and the need for a separate Splunk license make it a non-starter compared to other Zipkin alternatives.
Final thoughts
Choosing a distributed tracing tool in 2025 is about more than just finding a replacement for Zipkin. It’s about choosing a platform that aligns with the future of cloud-native development. The message from the market is clear: proprietary agents and complex, punitive pricing models are on the way out. The future is built on open standards like OpenTelemetry, which give you the freedom and flexibility to own your data and control your costs.
While the titans like Datadog and Dynatrace offer immense power, they often come with the heavy chains of vendor lock-in and budget-breaking price tags. The open-source world, with tools like Grafana Tempo, offers freedom but demands a steep operational price.
The sweet spot lies with modern, OTel-native platforms that combine the best of both worlds: powerful, unified analytics and a commitment to open standards. Dash0, with its SIFT framework, zero lock-in architecture, and transparent pricing, is designed to be that modern choice. It gives you the advanced capabilities you need to troubleshoot complex systems without forcing you to compromise on your architectural principles or your budget.
Ready to see what a truly modern, OpenTelemetry-native platform can do? Give Dash0 a try.
