Zipkin helped a lot of teams take their first steps into distributed tracing. It’s simple, open source, and does the basics well. But as systems grow more complex, that simplicity can start to feel limiting. More services, more environments, more noise – and suddenly tracing is no longer just about seeing a request path.
Many teams today want tracing that fits naturally into how they build and ship software. Less manual setup, fewer moving parts to maintain, and better context across logs, metrics, and infrastructure. That’s where Zipkin alternatives come in. Some focus on deeper observability, others on ease of use or tighter cloud integration. The right choice usually depends on how fast your team moves and how much overhead you’re willing to carry just to see what’s happening inside your system.
1. AppFirst
AppFirst comes at the tracing conversation from an unusual angle. They are not trying to replace Zipkin feature for feature. Instead, they treat observability as something that should already be there when an application runs, not something teams bolt on later. Tracing, logs, and metrics live inside a wider setup where developers define what their app needs, and the platform handles the infrastructure behind it. In practice, that means tracing data shows up as part of the application lifecycle, not as a separate system someone has to wire together.
What stands out is how AppFirst shifts responsibility. Developers keep ownership of the app end to end, but they are not pulled into Terraform files, cloud policies, or infra pull requests just to get visibility. For teams used to Zipkin running as one more service to maintain, this can feel like a reset. Tracing is less about managing collectors and storage and more about seeing behavior in context – which service, which environment, and what it costs to run. It is not a pure tracing tool, but for some teams that is exactly the point.
Faits marquants :
- Application-first approach to observability and infrastructure
- Built-in tracing alongside logging and monitoring
- Centralized audit trails for infrastructure changes
- Cost visibility tied to apps and environments
- Fonctionne sur AWS, Azure et GCP
- Options de déploiement SaaS et auto-hébergées
Pour qui c'est le mieux :
- Product teams that do not want to manage tracing infrastructure
- Teams shipping quickly with limited DevOps bandwidth
- Organizations standardizing how apps are deployed and observed
- Developers who want tracing without learning cloud tooling
Informations de contact :
- Site web : www.appfirst.dev
2. Jaeger
Jaeger is often the first serious Zipkin alternative teams look at, especially once distributed systems start getting messy. They focus squarely on tracing itself: following requests across services, understanding latency, and spotting where things slow down or fail. Jaeger usually brings more control, more configuration options, and better visibility into complex service graphs.
There is also a strong community angle. Jaeger is open source, governed openly, and closely aligned with OpenTelemetry. That matters for teams that want to avoid lock-in or rely on widely adopted standards. The tradeoff is effort. Running Jaeger well means thinking about storage, sampling, and scaling. It fits teams that are comfortable owning that complexity and tuning it over time, rather than expecting tracing to just appear by default.
Faits marquants :
- Open source distributed tracing platform
- Designed for microservices and complex workflows
- Deep integration with OpenTelemetry
- Service dependency and latency analysis
- Active community and long-term project maturity
Pour qui c'est le mieux :
- Engineering teams already running microservices at scale
- Organizations committed to open source tooling
- Teams that want fine-grained control over tracing behavior
Informations de contact :
- Website: www.jaegertracing.io
- Twitter: x.com/JaegerTracing
3. Grafana Tempo
Grafana Tempo takes a different route than classic Zipkin-style systems. Instead of indexing every trace, they focus on storing large volumes of trace data cheaply and linking it with metrics and logs when needed. For teams that hit scaling limits with Zipkin, this approach can feel more practical, especially when tracing volume grows faster than anyone expected.
Tempo is usually used alongside other Grafana tools, which shapes how teams work with it. Traces are not always the first thing you query on their own. Instead, engineers jump from a metric spike or a log line straight into a trace. That workflow makes Tempo less about browsing traces and more about connecting signals. It works well if you already live in Grafana dashboards, but it can feel unfamiliar if you expect tracing to be a standalone experience.
Faits marquants :
- High-scale tracing backend built for object storage
- Supports Zipkin, Jaeger, and OpenTelemetry protocols
- Tight integration with Grafana, Loki, and Prometheus
- Designed to handle very large trace volumes
- Open source with self-managed and cloud options
Pour qui c'est le mieux :
- Systems generating large amounts of trace data
- Organizations focused on cost-efficient long-term storage
- Engineers who correlate traces with logs and metrics rather than browsing traces alone
Informations de contact :
- Site web : grafana.com
- Facebook : www.facebook.com/grafana
- Twitter : x.com/grafana
- LinkedIn : www.linkedin.com/company/grafana-labs
4. SigNoz
SigNoz is commonly regarded as an alternative to running Zipkin independently. It treats tracing as part of a larger observability approach, integrating it with logs and metrics instead of keeping it separate. For teams that initially used Zipkin and later incorporated other tools, SigNoz often becomes relevant when their toolset feels disjointed. Its design revolves around OpenTelemetry from the beginning, influencing data gathering and the of various signals during debugging.
Teams quickly observe the workflow benefits. Rather than switching between different tracing, logging, and metrics tools, SigNoz keeps these views integrated. A slow endpoint can lead directly to a trace, then to related logs without losing context. It is not as lightweight as Zipkin, which is a trade-off. You gain more context but also have a bigger system to operate. Some teams find this acceptable as their systems surpass basic tracing needs.
Faits marquants :
- OpenTelemetry-native design for traces, logs, and metrics
- Uses a columnar database for handling observability data
- Can be self-hosted or used as a managed service
- Focus on correlating signals during debugging
Pour qui c'est le mieux :
- Teams that already use OpenTelemetry across services
- Engineers tired of stitching together multiple observability tools
- Teams comfortable running a broader observability stack
Informations de contact :
- Site web : signoz.io
- Twitter : x.com/SigNozHQ
- LinkedIn : www.linkedin.com/company/signozio
5. OpenTelemetry
OpenTelemetry instead of being a single tool you deploy, they provide the common language for how traces, metrics, and logs are created and moved around. Many teams replace Zipkin by standardizing on OpenTelemetry for instrumentation, then choosing a backend later.
This approach changes how tracing decisions are made. Rather than locking into one system early, teams instrument once and keep their options open. A service might start by sending traces to a simple backend and later move to something more advanced without touching application code. That flexibility is appealing, but it does come with responsibility. Someone still has to decide where the data goes and how it is stored. OpenTelemetry does not remove that work, it just avoids hard dependencies.
Faits marquants :
- Vendor-neutral APIs and SDKs for tracing, logs, and metrics
- Supports many languages and frameworks out of the box
- Designed to work with multiple backends, not replace them
- Open source with community-driven development
Pour qui c'est le mieux :
- Teams planning to move away from Zipkin without backend lock-in
- Organizations standardizing instrumentation across services
- Engineering groups that want flexibility in observability tooling
Informations de contact :
- Website: opentelemetry.io
6. Uptrace
Uptrace is usually considered when teams want more than Zipkin but do not want to assemble a full observability stack themselves. They focus heavily on distributed tracing, but keep metrics and logs close enough that debugging stays practical. Traces are stored and queried in a way that works well even when individual requests get large, which matters once services start fanning out across many dependencies.
One thing that stands out is how Uptrace balances control and convenience. Teams can run it themselves or use a managed setup, but the experience stays fairly similar. Engineers often describe moving from Zipkin as less painful than expected, mostly because OpenTelemetry handles instrumentation and Uptrace focuses on what happens after the data arrives. It feels closer to a tracing-first system than an all-in-one platform, which some teams prefer.
Faits marquants :
- Distributed tracing built on OpenTelemetry
- Supports large traces with many spans
- Works as both a self-hosted and managed option
- Traces, metrics, and logs available in one place
Pour qui c'est le mieux :
- Systems with complex request paths and large traces
- Engineers who want OpenTelemetry without building everything themselves
Informations de contact :
- Site web : uptrace.dev
- E-mail: support@uptrace.dev
7. Apache SkyWalking
Apache SkyWalking is usually considered when Zipkin starts to feel too narrow for what teams actually need day to day. They treat tracing as part of a wider application performance picture, especially for microservices and Kubernetes-based systems. Instead of focusing only on request paths, SkyWalking leans into service topology, dependency views, and how services behave as a whole. In practice, teams often use it to answer questions like why one service slows everything else down, not just where a single trace failed.
What makes SkyWalking feel different is how much it tries to cover in one place. Traces, metrics, and logs can all flow through the same system, even if they come from different sources like Zipkin or OpenTelemetry. That breadth can be useful, but it also means SkyWalking works best when someone takes ownership of it.
Faits marquants :
- Distributed tracing with service topology views
- Designed for microservices and container-heavy environments
- Supports multiple telemetry formats including Zipkin and OpenTelemetry
- Agents available for a wide range of languages
- Built-in alerting and telemetry pipelines
- Native observability database option
Pour qui c'est le mieux :
- Teams running complex microservice architectures
- Environments where service relationships matter as much as individual traces
- Organizations that want tracing and APM in one system
- Engineering teams comfortable managing a larger observability platform
Informations de contact :
- Site web : skywalking.apache.org
- Twitter: x.com/asfskywalking
- Address: 1000 N West Street, Suite 1200 Wilmington, DE 19801 USA
8. Datadog
Datadog approaches Zipkin alternatives from a platform angle. Distributed tracing sits alongside logs, metrics, profiling, and a long list of other signals. Teams usually come to Datadog when Zipkin answers some questions but leaves too many gaps around context, especially once systems span multiple clouds or teams.
In real use, Datadog tracing often shows up during incident reviews. Someone starts with a slow user action, follows the trace, then jumps into logs or infrastructure metrics without switching tools. That convenience comes from everything being tightly integrated, but it also means Datadog is less modular than open source tracing tools. You adopt tracing as part of a broader ecosystem, not as a standalone service.
Faits marquants :
- Distributed tracing integrated with logs and metrics
- Auto-instrumentation support for many languages
- Visual trace exploration with service and dependency views
- Correlation between application and infrastructure data
Pour qui c'est le mieux :
- Teams that want tracing tightly linked to other observability data
- Organizations managing large or mixed cloud environments
- Engineering groups that prefer a single platform over multiple tools
Informations de contact :
- Site web : www.datadoghq.com
- Courriel : info@datadoghq.com
- Twitter : x.com/datadoghq
- LinkedIn : www.linkedin.com/company/datadog
- Instagram : www.instagram.com/datadoghq
- Adresse : 620 8th Ave 45th Floor New York, NY 10018 USA
- Phone: 866 329 4466
9. Honeycomb
Honeycomb focuses heavily on high-cardinality data and on letting engineers ask questions after the fact, not just view predefined dashboards. Tracing in Honeycomb tends to be exploratory. People click into a trace, slice it by custom fields, and follow patterns rather than single failures.
The experience is more investigative than operational. Teams sometimes describe Honeycomb as something they open when an issue feels weird or hard to reproduce. That makes it a good fit for debugging unknown behavior, but it can feel different from traditional monitoring tools. You do not just watch traces scroll by. You dig into them.
Faits marquants :
- Distributed tracing built around high-cardinality data
- Strong focus on exploratory debugging workflows
- Tight integration with OpenTelemetry instrumentation
- Trace views designed for team-wide investigation
Pour qui c'est le mieux :
- Teams debugging complex or unpredictable system behavior
- Engineering cultures that value deep investigation over dashboards
Informations de contact :
- Site web : www.honeycomb.io
- LinkedIn : www.linkedin.com/company/honeycomb.io
10. Sentry
Sentry tends to enter the Zipkin replacement conversation from a debugging angle. They focus on connecting traces to real application problems like slow endpoints, failed background jobs, or crashes users actually hit. Tracing is not treated as a standalone map of services, but as context around errors and performance issues. A developer following a slow checkout flow, for example, can jump from a frontend action into backend spans and see where time disappears.
What makes Sentry feel different is how opinionated the workflow is. Instead of browsing traces for their own sake, teams usually land on traces through issues, alerts, or regressions after a deploy. That can be refreshing for product-focused teams, but less appealing if you want tracing as a neutral infrastructure view. Sentry works best when tracing is part of everyday debugging, not something only SREs open.
Faits marquants :
- Distributed tracing tied closely to errors and performance issues
- End-to-end context from frontend actions to backend services
- Span-level metrics for latency and failure tracking
- Traces connected to deploys and code changes
Pour qui c'est le mieux :
- Product teams debugging real user-facing issues
- Developers who want tracing linked directly to errors
- Teams that care more about fixing problems than exploring service maps
Informations de contact :
- Website: sentry.io
- Twitter: x.com/sentry
- LinkedIn: www.linkedin.com/company/getsentry
- Instagram: www.instagram.com/getsentry
11. Dash0
Dash0 positions tracing as something that should be fast to get value from, not something you babysit for weeks. They build everything around OpenTelemetry and assume teams already want standard instrumentation instead of vendor-specific agents. Traces, logs, and metrics are presented together, but tracing often acts as the spine that connects everything else. Engineers typically start with a suspicious request and fan out from there.
The experience is intentionally streamlined. Filtering traces by attributes feels closer to searching code than configuring dashboards, and configuration-as-code shows up early in the workflow. Dash0 is less about long-term historical analysis and more about fast answers during development and incidents. That makes it appealing to teams who find traditional observability tools heavy or slow to navigate.
Faits marquants :
- OpenTelemetry-native across traces, logs, and metrics
- High-cardinality trace filtering and fast search
- Configuration-as-code support for dashboards and alerts
- Tight correlation between signals without manual wiring
Pour qui c'est le mieux :
- Teams already standardized on OpenTelemetry
- Engineers who value fast investigation over complex dashboards
- Platform teams that want observability treated like code
Informations de contact :
- Site web : www.dash0.com
- E-mail: hi@dash0.com
- Twitter : x.com/dash0hq
- LinkedIn : www.linkedin.com/company/dash0hq
- Address: 169 Madison Ave STE 38218 New York, NY 10016 United States
12. Elastic APM
Elastic APM often replaces Zipkin when tracing needs to live next to search, logs, and broader system data. They treat distributed tracing as one signal in a larger observability setup built on Elastic’s data model. Traces can be followed across services, then correlated with logs, metrics, or even custom fields that teams already store in Elastic.
What stands out is flexibility. Elastic APM works well for mixed environments where some services are modern and others are not. Tracing does not force a clean-slate approach. Teams can instrument gradually, bring in OpenTelemetry data, and analyze everything through a familiar interface. It is not minimal, but it scales naturally for organizations already using Elastic for other reasons.
Faits marquants :
- Distributed tracing integrated with logs and search
- OpenTelemetry-based instrumentation support
- Service dependency and latency analysis
- Works across modern and legacy applications
Pour qui c'est le mieux :
- Organizations with diverse or legacy-heavy systems
- Engineers who want tracing tied to search and logs
Informations de contact :
- Site web : www.elastic.co
- Courriel : info@elastic.co
- Facebook : www.facebook.com/elastic.co
- Twitter : x.com/elastic
- LinkedIn : www.linkedin.com/company/elastic-co
- Adresse : 5 Southampton Street Londres WC2E 7HA
13. Kamon
Kamon focuses on helping developers understand latency and failures without needing deep monitoring expertise. Tracing is combined with metrics and logs, but the UI pushes users toward practical questions like which endpoint slowed down or which database call caused a spike after a deployment.
There is also a strong focus on specific ecosystems. Kamon fits naturally into stacks built with Akka, Play, or JVM-based services, where automatic instrumentation reduces setup friction. Compared to broader platforms, Kamon feels narrower, but that can be a benefit. Teams often adopt it because it answers their daily questions without asking them to redesign their monitoring approach.
Faits marquants :
- Distributed tracing focused on backend services
- Strong support for JVM and Scala-based stacks
- Correlated metrics and traces for latency analysis
- Minimal infrastructure and setup overhead
Pour qui c'est le mieux :
- Backend-heavy development teams
- JVM and Akka based systems
- Developers who want simple, practical tracing without complex tooling
Informations de contact :
- Website: kamon.io
- Twitter: x.com/kamonteam
Conclusion
Wrapping it up, moving beyond Zipkin is less about chasing features and more about deciding how you want tracing to fit into everyday work. Some teams want traces tightly linked to errors and deploys so debugging stays close to the code. Others care more about seeing how services interact at scale, or about unifying traces with logs and metrics without juggling tools.
What stands out across these alternatives is that there is no single upgrade path that works for everyone. The right choice usually reflects how a team builds, ships, and fixes software, not how impressive a tracing UI looks.
