If you're reading this, chances are you're already neck-deep in distributed tracing. You've probably hit that wall where Grafana Tempo, while doing its job, isn't cutting it for your specific needs. Maybe the costs are spiraling, tracing through that mountain of data feels like finding a needle in a haystack, or you're just tired of stitching together a dozen different tools to get a full picture.
The good news is, there are now a ton of excellent Grafana Tempo alternatives that might be a better match for your team’s workflow, architecture, or your budget.
Subsequently, I'm going to list the best alternatives to Grafana Tempo. We'll talk about who they're for, what they do well, and what's the real catch. We're cutting through the noise to help you figure out what tracing tool actually fits your cloud-native stack, your wallet, and your sanity.
1. Dash0
Dash0 is built from the ground up as an OpenTelemetry-native observability platform. This means it’s fundamentally designed to work with1 your trace data without the usual impedance mismatches. It takes all your OpenTelemetry signals—logs, metrics, and traces—and ties them together using the OpenTelemetry resource concept. Think of it as a unified view where everything related to a service, host, or pod is just a click away.
Dash0 also brings its SIFT framework to the table. That's "Spam removal," "Improve telemetry," "Filtering and grouping," and "Triage." It's about getting rid of the noise, making your data smarter, letting you slice and dice it intuitively, and even automatically pinpointing probable root causes with one click.
What's good
OpenTelemetry-native: It's not just compatible with OpenTelemetry, it's the core architecture. Dash0 speaks OTel fluently, so you get full signal integration, resource-centricity, and consistent terminology right out of the box. No weird data mappings or lost context.
PromQL for Everything: Tired of learning a new query language for every data type? Dash0 uses PromQL for logs, metrics, and traces. If you know Prometheus, you're already halfway there. Plus, there's a visual query builder if PromQL isn't your daily jam.
AI-Driven Insights, Not Hype: Dash0’s AI isn't about a chatbot; it's about making your life easier in the background. Features like Log AI automatically assign severity to unstructured logs with high accuracy and zero false positives, and Triage provides one-click root cause analysis. It cleans up your data and finds the needle for you.
Zero Lock-In Commitment: Dash0 is all about open standards. Dashboards are based on Perses (so you can take them with you), alerts use Prometheus standards, and instrumentation relies on vendor-agnostic OTel SDKs. Switching vendors means changing a URL, not re-instrumenting your entire stack.
Transparent and Predictable Pricing: You pay by the number of logs, spans, and metric data points ingested, not by GB or users. This encourages rich metadata without ballooning costs and offers real-time cost visibility. No more surprise bills.
The catch
The biggest "catch" is that Dash0 is a newer player. While it's built on battle-tested open standards and is rapidly evolving, it might not have the sheer breadth of niche integrations or the decades of legacy feature bloat that some older, more established vendors have accumulated. If your team relies on very specific, deeply ingrained proprietary features from legacy tools, there might be a minor adjustment period. However, for true cloud-native stacks, this isn't a problem.
The verdict
Dash0 is the modern choice for cloud-native startups and mid-sized companies that are serious about OpenTelemetry and Prometheus. If you're tired of proprietary vendor lock-in, unpredictable bills, and tools that treat telemetry signals like separate silos, Dash0 offers a refreshing, opinionated, and cost-effective approach. It’s built by engineers, for engineers, with a laser focus on simplifying observability without compromise.
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2. Jaeger
Jaeger is an open-source, end-to-end distributed tracing system that came out of Uber and is now a CNCF graduated project. It’s purpose-built for monitoring and troubleshooting transactions in complex distributed systems, which means it’s right at home in a microservices environment.
What's good
- Open Source & CNCF Graduated: This means no licensing fees, complete control over your data, and a large, active community backing its development. It’s a trusted standard.
- Rich Trace Visualization: Jaeger provides excellent tools to visualize trace timelines (Gantt charts), service dependency graphs, and allows for trace comparison. This is critical for debugging performance issues in microservices.
- Scalable & Flexible: You can deploy it as a simple binary for dev or scale it out with Kafka and various storage backends like Cassandra or Elasticsearch to handle massive trace volumes.
- OpenTelemetry Alignment: Jaeger is moving towards deeper integration with OpenTelemetry, deprecating its native SDKs and agents in favor of OTel. This ensures future compatibility and interoperability.
The catch
Jaeger is only a tracing tool. It doesn't handle logs or metrics natively, so you'll need to stitch it together with other open-source tools (like Prometheus and Loki/Grafana) to get a full observability picture. This "composable" approach means significant operational overhead for deployment, management, and scaling, especially for the storage backend. Its UI, while functional, isn't as polished as commercial alternatives, and there's no enterprise-level support.
The verdict
Jaeger is an excellent choice for engineering teams with deep in-house expertise who are committed to an open-source stack and want ultimate control over their distributed tracing. If you're running a complex microservices architecture and have the SREs on staff to manage the operational complexity of a self-hosted solution, Jaeger gives you powerful tracing capabilities without vendor lock-in. Just be prepared to build out the rest of your observability stack around it.
3. Zipkin
Zipkin is another open-source distributed tracing system. It was originally developed at Twitter and is inspired by Google's Dapper. Like Jaeger, Zipkin helps collect and visualize timing data for operations in a distributed system, making it easier to troubleshoot latency issues and errors.
What's good
Open Source & Lightweight: It’s free, easy to get started with, and has a relatively small footprint, especially compared to some of the more heavyweight commercial platforms.
Simple UI: Zipkin's user interface is straightforward and gets the job done for visualizing traces, showing dependencies, and identifying bottlenecks.
Broad Language Support: It offers libraries for a wide array of programming languages, making it flexible for polyglot environments.
Community-Driven: Being open source, it benefits from community contributions and a supportive user base.
The catch
Zipkin, while effective for basic distributed tracing, is much less feature-rich than more modern or commercial alternatives. Its UI is functional but basic, lacking advanced analytics or correlation features. It doesn't natively handle metrics or logs, so it shares the same "stitching together" challenge as Jaeger for a complete observability picture. Scalability can also become an issue at very high data volumes, and it generally requires more manual effort for setup and maintenance compared to managed services.
The verdict
Zipkin is a solid choice for smaller teams or those just dipping their toes into distributed tracing. If you need a simple, open-source tool primarily for basic trace visualization and analysis without significant budget constraints on operational overhead, Zipkin can get you started quickly. However, for complex, large-scale microservices or a desire for deeper insights and unified observability, you'll likely outgrow its capabilities fairly quickly.
4. SigNoz
SigNoz positions itself as a direct, open-source alternative to all-in-one observability platforms like Datadog. It's built as an OpenTelemetry-native solution, designed to unify logs, metrics, and traces into a single application. SigNoz uses ClickHouse as its underlying datastore for fast analytics on large data volumes.
What's good
Open-Source, OTel-Native, All-in-One: This is SigNoz's core pitch: get a unified observability experience (logs, metrics, traces) on an open-source, OpenTelemetry-native foundation. This helps avoid vendor lock-in and proprietary agents.
ClickHouse-Powered Performance: Using ClickHouse as its backend means high-performance analytics on large volumes of observability data at a potentially lower infrastructure cost than Elasticsearch-based solutions.
Simple, Transparent Pricing (Cloud): For its managed cloud offering, SigNoz uses a straightforward usage-based model with no per-user or per-host fees, directly addressing a major pain point of incumbents.
Flexible Deployment: You can choose between a self-hosted open-source version for maximum control or a managed SaaS offering for convenience.
The catch
As a younger project, SigNoz's feature set and pre-built integrations might not be as mature or extensive as those of long-standing market leaders. The UI/UX, while functional, might not be as refined. While the self-hosted version avoids licensing costs, it still requires operational effort to manage the underlying ClickHouse database and the SigNoz application itself.
The verdict
SigNoz is a compelling choice for startups and engineering teams committed to open-source and OpenTelemetry who want an all-in-one observability solution without the cost and proprietary nature of Datadog or New Relic. If you're looking for a unified platform with predictable pricing and the flexibility of self-hosting or a managed cloud service, SigNoz offers a strong alternative.
5. Honeycomb
Honeycomb is a SaaS-only platform built specifically for observability, centered around "wide events" and traces. Its architecture is fundamentally different from traditional metric-based systems, focusing on helping engineers debug complex, unpredictable "unknown unknown" problems in production systems by rapidly analyzing high-cardinality, high-dimensionality data.
What's good
- High-Cardinality Data Analysis: This is Honeycomb's superpower. It excels at slicing and dicing your data by any attribute at high speed, letting you quickly find patterns in highly granular telemetry. This is invaluable for complex microservices.
- BubbleUp: This signature feature automatically compares outliers against a baseline, highlighting the specific attributes that are different. It’s an incredibly fast and intuitive way to pinpoint the scope and potential cause of an issue.
- OpenTelemetry-Native & Advocate: Honeycomb is a strong proponent of OpenTelemetry and is built natively to ingest OTel data. This means seamless integration and strong future-proofing.
- Predictable, Event-Based Pricing: Their pricing is simple, based solely on the number of events (trace spans, structured logs) ingested per month, with no charges for users, cardinality, or custom metrics. This makes costs highly predictable.
- Developer-Centric Debugging: It's designed for how developers actually debug in complex distributed systems, making it a favorite among teams embracing observability-driven development.
The catch
Honeycomb's strength is also its limitation: it's laser-focused on event and trace-based debugging. It's not a traditional all-encompassing monitoring tool. Its capabilities for classic infrastructure monitoring (like simple host metrics dashboards) and unstructured log management are less mature than dedicated tools. It also lacks features like synthetic monitoring. The different approach means a learning curve for teams used to metric-centric monitoring.
The verdict
Honeycomb is ideal for developer-centric engineering teams with complex, distributed microservices architectures that need to debug novel production issues fast. If your team values deep, high-cardinality tracing and rapid investigative capabilities over a broad, traditional monitoring suite, and you're willing to adapt to an event-driven mindset, Honeycomb is a top-tier choice.
6. Lightstep (ServiceNow Cloud Observability)
Lightstep, now part of ServiceNow Cloud Observability, is a distributed tracing and APM platform. It's known for its focus on full-context distributed traces and connecting them with change intelligence. Lightstep aims to provide deep root cause analysis for complex microservice performance issues.
What's good
- Deep Distributed Tracing & Root Cause Analysis: Lightstep excels at capturing full-context distributed traces, enabling detailed analysis of request flows across microservices to quickly identify performance bottlenecks and errors.
- OpenTelemetry Heritage: It has a strong history with OpenTelemetry, emphasizing vendor-neutral instrumentation, which is great for avoiding lock-in.
- Change Intelligence: As part of ServiceNow, Lightstep is increasingly focused on correlating observability data with changes in your environment, helping SREs understand the impact of deployments and configurations.
- Enterprise-Ready: Being part of ServiceNow, it's geared towards large enterprises, often appealing to existing ServiceNow customers.
The catch
While strong in tracing, Lightstep might be less comprehensive as a full-stack observability platform compared to all-in-one solutions, especially outside of tracing and APM. Its pricing model, often custom and contract-based, might be less transparent for smaller organizations. The integration into the broader ServiceNow ecosystem could also mean a learning curve or workflow adjustments for teams not already using ServiceNow products.
The verdict
Lightstep is a strong contender for enterprises, especially those already using ServiceNow, who are deeply invested in microservices and require sophisticated distributed tracing for debugging performance. If your primary need is robust root cause analysis tied to changes in complex distributed systems, and you're operating at an enterprise scale, Lightstep (ServiceNow Cloud Observability) offers a compelling solution.
7. Datadog (APM/Tracing)
Datadog is a dominant force in the observability market, offering a vast, unified platform that includes APM and distributed tracing as core components. It aims to provide real-time visibility across your entire technology stack, from infrastructure to applications.
What's good
- Broadest Feature Set: Datadog's sheer breadth of features is unmatched. It combines infrastructure monitoring, APM, log management, RUM, synthetics, and security tools into one platform. This can simplify operations for large enterprises by reducing tool sprawl.
- Unified UI & Integrations: It offers a generally cohesive user experience despite its vastness, with over 900 integrations, making it easy to pull data from nearly any source.
- Watchdog AI & Polished Dashboards: The Watchdog AI engine automatically surfaces anomalies, and its dashboarding experience is highly polished, enabling powerful data exploration and visualization.
- Easy Initial Setup: Users often report that getting started with key features, including APM tracing, is remarkably simple due to its proprietary agent and auto-instrumentation.
The catch
The most significant pain point is Datadog's cost. Its complex, multi-vector pricing model is notoriously difficult to forecast, often leading to "surprise bills." All OpenTelemetry metrics are treated as "custom metrics" and are priced at a premium, financially penalizing teams adopting open standards. Per-host pricing can be punitive for high-density container environments. The UI can also be overwhelming for new users due to the sheer number of features.
The verdict
Datadog is a powerhouse for large enterprises with heterogeneous environments and significant budgets who prioritize a single, fully-managed, feature-complete platform. For distributed tracing, it offers deep visibility. However, for budget-conscious teams or those philosophically committed to an OpenTelemetry-native strategy to avoid vendor lock-in and unpredictable costs, Datadog can quickly become a financial burden.
8. New Relic
New Relic, a long-standing player in APM, has evolved into a comprehensive full-stack observability platform. Its distributed tracing capabilities are part of a unified solution that includes infrastructure monitoring, RUM, and log management, all built on a unified telemetry database (NRDB).
What's good
- Unified Telemetry Platform: New Relic One consolidates all data types into a single platform and unified database (NRDB), aiming for seamless correlation across logs, metrics, and traces.
- Generous Free Tier: A significant differentiator is its free tier, offering 100 GB of data ingest per month and one full platform user at no cost, which is attractive for startups and small teams.
- OpenTelemetry Support: New Relic provides first-class support for OpenTelemetry data ingestion alongside its proprietary agents.
- NRQL (New Relic Query Language): This powerful, SQL-like query language allows for flexible and sophisticated data exploration across all telemetry data.
- Deep APM Roots: With its history in APM, New Relic offers strong code-level performance insights and full-stack correlation.
The catch
Despite efforts to simplify pricing, cost at scale remains a major concern, particularly the per-user fees for "Full Platform" access and data ingest volume. There have been public complaints about "unethical billing" due to unexpected log data generated by the New Relic agent itself, leading to bill shock. The UI can also be complex and have a steep learning curve.
The verdict
New Relic is a good fit for engineering teams and enterprises that want a powerful, all-in-one observability platform with deep APM capabilities and a
generous free tier. It's suitable if you're prepared for potential cost escalation at scale due to user and data volume, and are comfortable with its proprietary query language. It's a strong choice for those looking to consolidate tools and gain better control over observability if they manage their usage carefully.
9. Splunk APM
Splunk Observability Cloud is a comprehensive SaaS platform that unifies several of Splunk's monitoring products, including APM. It's built to be OpenTelemetry-native and emphasizes full-fidelity, NoSample™ tracing, aiming to capture 100% of trace data.
What's good
- OpenTelemetry-Native & Full-Fidelity Tracing: Splunk Observability Cloud is designed from the ground up for OpenTelemetry, capturing 100% of trace data, which eliminates blind spots from sampling.
- Log Observer Connect: This feature seamlessly links metrics and traces in Observability Cloud with the deep log analytics capabilities of the core Splunk Platform, allowing users to pivot from a trace directly to relevant logs.
- Enterprise-Grade Scalability: Leveraging Splunk's strong backend, it's built for large-scale enterprise environments.
- Strong Security Integration: For organizations already invested in the broader Splunk ecosystem for SIEM and log management, it offers tight integration for a unified operational and security view.
The catch
Splunk is notoriously expensive, and Observability Cloud is no exception. Its per-host pricing model can quickly become very costly, especially for large, dynamic environments. A major architectural "catch" is the separation of the log data store; logs still reside in the traditional Splunk Platform backend, which can introduce complexity and potential latency compared to truly unified platforms. The platform can also be complex to master, and some users report lacking documentation or support for APM in certain regions.
The verdict
Splunk APM is best suited for organizations already heavily invested in the broader Splunk ecosystem for log management and security. If you need full-fidelity, OpenTelemetry-native tracing that integrates tightly with your existing Splunk deployment and are willing to pay a premium price, it's a viable option. However, for green-field projects or companies without a prior Splunk investment, the high cost and segmented log storage make it less attractive.
Final thoughts
The world of distributed tracing, and observability in general, is constantly evolving. Grafana Tempo is solid, but if you're hitting its limits, you've got real options. The key isn't just swapping one tool for another; it's about finding a solution that genuinely aligns with your cloud-native principles, your budget, and your team's workflow.
For most modern teams drowning in data and frustrated by hidden costs, an OpenTelemetry-native platform like Dash0 is the clear winner. It's built for today's cloud-native challenges, puts you in control of your data and your wallet, and actually simplifies troubleshooting.
Don't settle for less than what your team needs. If you're ready to cut through the noise and get real answers from your traces, give Dash0 a shot.
