Distributed Tracing with message payload.

The only SaaS Distributed Tracing Platform focused on Business Tracing.

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Distributed Tracing as a Service

Trasier is a Distributed Tracing SaaS Platform, meaning, you don't have to worry about configuration, update nor about the maintenance of the infrastructure. Just send the data to analyze it later.

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Distributed Tracing with message payload

Trasier allows to capture SOAP and REST message payloads along with the technical data like latency or HTTP error codes.
Capturing message payloads opens a tremendous amount of new possibilities:

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Simplify bug analysis
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Simplify bug triage
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Speed up development time
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Can be turned off for sensitive data
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Error detection based on business data
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Business Process Tracing

Distributed Business Tracing

Trasier takes Distributed Tracing to a new level by correlating Business Process, and not only individual requests. No matter how long they take! Together with the captured message payloads, it is for example possible to reproduce bugs for an entire user session throughout microservices. This also allows on the fly anomaly detection in business processes!

Learn more about Business Processes
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OpenTracing compliant

Spring support

Trasier Client is built on top of OpenTracing and has great support for Spring. Adding Trasier to your Spring Boot project means, adding one Maven dependency, and the whole magic happens behind the scenes.

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OpenTracing compliant
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Single dependency for Spring Boot
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Easy Spring integration
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Open Source Java-Client
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REST and SOAP support
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Key Benefits

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Bugfixing and bug triage
Being able to share a link pointing to a service that responds with incorrect data makes it easier to address the bug to the appropriate team. No more sending email attachments.
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Mocking services
Instead of configuring complicated mocks, or storing JSON or XML files on a hard drive, one can use Trasier as a mocking platform and say, "return response for operation productOffers for conversationId=XYZ".
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AI and Machine learning
The captured data is a perfect foundation for MI. This way it's possible to train a model to detect anomalies in system behavior.

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