• 21 min read

9 Best Jaeger Alternatives for Tracing in 2025

Jaeger is the open-source hero, the free tool we all started with. But running it in production in 2025 is a different story. The operational overhead is a killer. You’re spending more time babysitting Cassandra or Elasticsearch backends than you are actually debugging your services. The UI feels clunky, and finding the exact trace you need in a high-volume environment feels like searching for a needle in a growing haystack.

You need more than just a trace viewer. You need analytics, you need seamless correlation with logs and metrics, and you don’t want to spend a full sprint setting up and scaling your storage backend. The good news is, you’ve got a ton of options to choose from.

This guide will break down the best Jaeger alternatives for cloud-native teams, looking at the real-world trade-offs. We’ll compare them on what actually matters: deployment and management headaches, performance at scale, analytics that don’t suck, and how they play with the rest of your observability data.

1. Dash0

Dash0 is a modern, OpenTelemetry-native observability platform built for teams that want to move fast without getting bogged down by their tools. It’s designed to give you the analytical power you’re missing from Jaeger, without the operational complexity or the vendor lock-in of the big incumbents. We take all three signals—logs, metrics, and traces—and unify them from the ground up, using OpenTelemetry’s resource-centric model as the foundation.

What’s good

  • Truly OpenTelemetry-Native: Dash0 isn’t just “compatible” with OpenTelemetry; it’s built on it. We use OTel’s terminology, its data model, and its semantic conventions as our native language. This means no data mapping, no loss of context, and a seamless experience for anyone already familiar with the standard. Your custom attributes and trace context flow through the entire platform.
  • Unified Querying with PromQL: Forget learning three different query languages. We use PromQL—the de-facto standard in cloud-native—for everything. You can write a single query to correlate a spike in trace latency with specific error logs and a dip in a key business metric. This is something you just can’t do with separate tools.
  • Zero-Effort Analytics with SIFT: Our SIFT framework helps you find the needle in the haystack, fast. It starts with Spam removal to drop noisy, useless traces before they cost you money. But the real game-changer is Triage, our one-click root cause analysis. It uses statistical analysis to compare error traces against successful ones and instantly highlights the attributes (like a specific user ID, software version, or region) that are the most likely cause of the problem.
  • No Vendor Lock-In: Your data comes in via OTLP. Your queries are in PromQL. Your dashboards are built on the open-source Perses project, and your alerts use the Prometheus standard. If you decide to leave, you can take your configurations with you. Switching is as simple as changing the OTLP endpoint in your Collector config.

The catch

Dash0 is a newer player, so the feature set, while deep in the core observability pillars, might not have every single bell and whistle that an established behemoth has bolted on over the last decade. We’re focused on doing the essentials of observability better and more intuitively. If you need niche features or extensive security monitoring suites, you’ll need to look elsewhere for now.

The verdict

Dash0 is for cloud-native teams who are frustrated with the operational toil of Jaeger but are unwilling to sign a blank check to the big vendors and get locked into their proprietary ecosystems. If you value open standards, predictable pricing, and powerful, automated analytics that let you debug faster, Dash0 is the clear choice.

Start your 14-day free trial of Dash0 today!

2. Grafana Tempo

Grafana Tempo is Grafana Labs’ answer to the distributed tracing problem. It’s designed to be a massively-scalable, low-overhead tracing backend that integrates perfectly with the rest of the Grafana (LGTM) stack: Loki for logs, Mimir for metrics, and of course, Grafana for visualization.

What’s good

  • Lower Operational Overhead: Compared to Jaeger’s database dependency, Tempo is simpler to manage. It relies on object storage (like S3 or GCS) for data, which is cheap, scalable, and something you’re probably already running. This design choice significantly reduces the operational burden of maintaining a complex stateful backend.
  • Trace ID-Based Search: Tempo’s core design is optimized for finding a trace by its ID. If you have a trace ID from a log or an error report, Tempo can pull it up almost instantly. This workflow is incredibly fast and efficient.
  • Seamless Grafana Integration: If you’re already a Grafana shop, Tempo is a natural fit. You can seamlessly link from logs in Loki or metrics in Mimir directly to traces in Tempo, all within a familiar Grafana dashboard. The context switching is minimal.

The catch

The biggest catch is how you find traces. Tempo’s primary lookup method is by trace ID. Searching for traces based on tags or attributes (e.g., “show me all traces for customer_id: 123 with an error”) is not its primary use case and requires a separate indexing mechanism, which can add complexity. This is a major departure from the powerful tag-based querying you might get from other platforms and can be a deal-breaker for exploratory debugging. The UI is just Grafana, which means alerting can be complex to configure.

The verdict

Grafana Tempo is the logical next step for teams already committed to the Grafana ecosystem who are feeling the operational pain of Jaeger. If your primary debugging workflow starts with a trace ID from a log, Tempo is a highly scalable and cost-effective solution. But if you rely on deep, exploratory searching across trace attributes to find unknown unknowns, you’ll find its search capabilities limiting.

3. Honeycomb

Honeycomb is a developer-focused observability tool that pioneered the concept of high-cardinality, event-based analysis. They don’t think in terms of the “three pillars”; they think about wide, structured events. For Honeycomb, a trace is just a collection of these rich events.

What’s good

  • Built for High Cardinality: This is Honeycomb’s superpower. They encourage you to add as much context (attributes) to your traces as possible—user IDs, tenant IDs, feature flag states, app versions, etc. Their query engine is built from the ground up to slice and dice this high-cardinality data with incredible speed.
  • Intuitive Querying and BubbleUp: You don’t need to learn a complex query language. Honeycomb’s UI is a visual query builder that lets you group, filter, and visualize your data interactively. Their standout feature, BubbleUp, automatically analyzes a selection of traces and tells you what makes them different from the baseline, instantly highlighting the problematic attributes.
  • Predictable, Event-Based Pricing: Honeycomb’s pricing is based on the number of events you send, not the number of users, hosts, or the cardinality of your data. This model is transparent and encourages you to send rich data without worrying about a surprise bill.

The catch

Honeycomb is a specialized tool. It’s phenomenal for debugging complex application logic but less suited for traditional infrastructure monitoring. It’s not an all-in-one replacement for a full-stack observability platform. It also requires a mindset shift; you have to embrace their event-based model and instrument your code to send the rich, structured data the platform thrives on.

The verdict

Honeycomb is the best choice for developer teams working on complex, distributed systems who need to answer “why” a problem is happening, not just “what” is broken. If your biggest pain is debugging “unknown unknowns” in your application code and you’re willing to invest in rich instrumentation, Honeycomb will feel like a superpower compared to Jaeger’s basic UI.

4. Datadog

Datadog is one of the “big three” observability giants. It’s a massive, all-in-one SaaS platform that offers everything from infrastructure monitoring and APM to log management, security, and RUM. Its tracing capabilities are part of its broader APM suite.

What’s good

  • Unified Platform: Datadog’s biggest strength is that everything is in one place. You can seamlessly pivot from a trace to related logs, infrastructure metrics, and even security signals in a single, polished UI. This unified context is powerful for troubleshooting.
  • Easy Setup and Broad Integrations: Getting started with Datadog is often as simple as installing their agent. They have a massive library of over 750 integrations, which means you can pull in data from almost any part of your stack with minimal configuration.
  • Advanced Features: As a market leader, Datadog offers a ton of advanced features that you won’t find in open-source tools, like AI-powered anomaly detection (Watchdog), a collaborative notebook interface, and sophisticated dashboarding.

The catch

The cost. It’s the cost. Datadog’s pricing is notoriously complex and expensive. They charge on multiple vectors: per host, per GB of ingested logs, per million indexed events, and—most painfully for OTel users—for “custom metrics.” Any data coming from OpenTelemetry is often billed at a premium. This creates a direct financial penalty for using open standards and can lead to massive, unpredictable bills. Secondly, it’s a proprietary ecosystem. You use their agent, their query language, and their dashboards. Getting your data and configs out is not easy, leading to significant vendor lock-in.

The verdict

Datadog is for large enterprises that want a single, powerful, vendor-managed platform for everything and have the budget to pay for it. If you value breadth of features and a unified UI over cost and open standards, it’s a contender. But for most cloud-native teams committed to OpenTelemetry, the pricing model and vendor lock-in make it a non-starter as a Jaeger alternative.

5. Dynatrace

Dynatrace is another enterprise-grade, all-in-one observability platform that competes directly with Datadog. Its core philosophy is built around AI-powered automation. Its distributed tracing technology, called PurePath, is a key component of this automated approach.

What’s good

  • AI-Powered Root Cause Analysis: This is Dynatrace’s main differentiator. Its “Davis” AI engine automatically analyzes performance issues, discovers dependencies, and points to the precise root cause without requiring manual digging. It’s designed to give you “answers, not just data.”
  • Automated Deployment: The “OneAgent” technology simplifies setup. A single agent installation can automatically discover and instrument all components of your application stack, providing full visibility with minimal configuration.
  • Full-Stack Context: PurePath captures deep, code-level visibility and automatically correlates it with user experience data (RUM) and infrastructure metrics, creating a complete picture of every transaction.

The catch

Like Datadog, Dynatrace is very expensive and is a proprietary, closed system. Its powerful automation can also feel like a “black box,” which might not appeal to hands-on teams who prefer to do their own data exploration. User reviews frequently mention a steep learning curve and a UI that can be confusing to navigate.

The verdict

Dynatrace is for large enterprises that are willing to pay a premium for a high-degree of automation. If your organization wants to reduce the manual troubleshooting burden on its teams and trusts an AI engine to provide root-cause analysis, Dynatrace is a strong option. However, if you value manual control, open standards, and cost-effectiveness, it’s not the right fit.

6. LightStep by ServiceNow

LightStep, now known as ServiceNow Cloud Observability, was founded by one of the co-creators of Google’s Dapper (the paper that inspired Jaeger and Zipkin). Its core focus is on deep, analytical tracing for complex systems.

What’s good

  • Analyzes 100% of Data: LightStep’s architecture is designed to analyze 100% of your unsampled transaction data. This allows it to find outliers and root causes that traditional sampling-based tools like Jaeger might miss.
  • Change Intelligence: A key feature is its ability to correlate performance regressions with recent deployments or changes in your environment. It helps you quickly answer the question, “What changed that caused this problem?”
  • Built for Scale: It was architected from the ground up to handle the massive scale of modern, microservices-based applications.

The catch

LightStep is a premium, enterprise-focused tool with a price tag to match. Pricing isn’t public, but it’s aimed at large organizations. Its integration into the broader ServiceNow ecosystem may add complexity for teams who just want a straightforward tracing tool. It’s a powerful solution, but it’s not a simple drop-in replacement for Jaeger.

The verdict

LightStep is for mature, large-scale organizations that are struggling with the limitations of sampled tracing and need a powerful analytical tool to debug performance issues in highly complex systems. If your primary goal is deep root cause analysis and you have the budget for an enterprise-grade solution, it’s a compelling Jaeger alternative.

7. SigNoz

SigNoz is an open-source, OpenTelemetry-native platform that positions itself as a direct alternative to Datadog and New Relic. It aims to provide a unified experience for logs, metrics, and traces in a single application, much like Dash0.

What’s good

  • All-in-One Open Source: SigNoz directly addresses Jaeger’s “traces-only” limitation. It provides a unified UI for logs, metrics, and traces, allowing you to correlate data across signals without needing separate tools.
  • OpenTelemetry-Native: Like Dash0, it’s built for OpenTelemetry from the ground up, using OTel for all data collection. This means no proprietary agents and no vendor lock-in.
  • High-Performance Backend: It uses ClickHouse as its storage backend, a columnar database known for high-speed query performance on large analytical datasets. This can lead to faster queries and lower infrastructure costs compared to Jaeger’s typical Elasticsearch backend.

The catch

As a younger open-source project, its feature set is less mature than the big commercial players. The UI/UX, while functional, may not feel as polished. For the self-hosted version, you still bear the operational overhead of managing the platform and its ClickHouse database, though it’s simpler than juggling Jaeger, Prometheus, and an ELK stack separately.

The verdict

SigNoz is an excellent open-source upgrade path for teams running Jaeger. It offers the unified, all-in-one experience you’re missing, is built on modern standards, and can be more performant and cost-effective. If you want to stick with open source but need more than just tracing, SigNoz is one of the best Jaeger alternatives out there.

8. Uptrace

Uptrace is another OpenTelemetry-native observability tool that focuses on providing a simple, developer-friendly experience. It uses ClickHouse as its backend, aiming for high performance and cost-efficiency.

What’s good

  • Simplicity and Ease of Use: Uptrace is designed to be easy to set up and use. The UI is clean, and the focus is on providing the essential features for troubleshooting without overwhelming the user.
  • Predictable Pricing: Like Honeycomb, Uptrace offers a simple, usage-based pricing model based on the gigabytes of data ingested. There are no per-user or per-host fees, which makes costs predictable.
  • OpenTelemetry-Native: It’s built around OpenTelemetry, ensuring a modern, vendor-neutral approach to data collection.

The catch

Uptrace is a smaller, more focused tool. It lacks the extensive feature breadth of the large all-in-one platforms. While it covers the core pillars, it doesn’t offer advanced features like extensive security monitoring or complex enterprise access controls. It’s a tool for core observability, not a sprawling enterprise suite.

The verdict

Uptrace is a great option for small to mid-sized teams and individual developers who want a simple, affordable, and modern tracing tool without the complexity of Jaeger or the cost of the enterprise giants. If your priority is a no-fuss, OTel-native platform with predictable pricing, Uptrace is definitely worth a look.

9. Zipkin

Zipkin is the other original, open-source distributed tracing system, created at Twitter around the same time Jaeger was being developed at Uber. The two projects share a common ancestry and are very similar in scope.

What’s good

  • Simplicity: Zipkin is often considered even simpler to set up and operate than Jaeger. For teams needing basic tracing capabilities with minimal fuss, Zipkin is a very lightweight and straightforward option.
  • Open Source and Community: It’s a mature, stable, and long-standing CNCF project with an active community. It’s a known quantity and a reliable choice for basic tracing.
  • OpenTelemetry Compatibility: Like Jaeger, Zipkin is fully compatible with OpenTelemetry. You can use standard OTel instrumentation to send trace data to a Zipkin backend, keeping your instrumentation vendor-neutral.

The catch

Zipkin shares all the same limitations as Jaeger. It’s a traces-only tool with a basic UI, limited search and analytics capabilities, and no native alerting. It also requires you to manage your own storage backend. In many ways, it’s a parallel choice to Jaeger rather than a significant upgrade.

The verdict

Zipkin is a solid, simple, and free alternative to Jaeger if your main goal is just to have a different open-source tracing backend. However, it doesn’t solve any of the core pain points around operational complexity, analytics, or signal correlation. Moving from Jaeger to Zipkin is more of a sideways step than a step up.

Final thoughts

The world of distributed tracing has moved far beyond Jaeger. While it was a foundational tool, the operational complexity and limited analytics just don’t cut it for modern cloud-native applications.

If you’re feeling the pain, your choice comes down to a few core philosophies. You can go all-in with an enterprise giant like Datadog or Dynatrace, but you’ll pay a steep price in both cost and vendor lock-in. You can adopt a powerful, developer-focused tool like Honeycomb, but you’ll need to embrace its event-based worldview. You can stick with the open-source ethos and upgrade to a unified platform like SigNoz or a scalable backend like Grafana Tempo, but you’ll retain some operational burden.

Or, you can choose a modern, OTel-native platform like Dash0 that balances power with simplicity. We give you the automated analytics and unified querying you’re missing, without the proprietary lock-in or the operational headaches. You get to keep your open-standards-based instrumentation while gaining the insights you need to actually solve problems faster.

Start your 14-day free trial of Dash0 today!

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