Latest Releases
Each release of Causely includes new features, bug fixes, and performance improvements. This page provides highlights of the most recent releases.
Have ideas, questions, or feedback? Please reach out to us at community@causely.ai.
Overview
Each release of Causely includes new features, bug fixes, and performance improvements. This page provides highlights of the most recent releases.
To get details on older releases, please go to the changelog page, listing all releases.
December 9, 2025
Version v1.0.105Impacted Services Graph
Causely now provides a dedicated Impacted Services Graph that makes it easier to understand how a reliability issue propagates across your environment.
This new view enables you to:
- Visualize the full impact of a root cause, including all degraded downstream services
- See live service-level metrics in context, directly within the graph
- Toggle extended hops to understand which additional services may be at risk if the issue remains unresolved
- Navigate seamlessly to the Live Topology view for any degraded service to continue investigation in real time
This makes it significantly easier for teams to assess the scope, urgency, and potential blast radius of reliability issues.
Datadog Dual Shipping & Hybrid Environment Support
Causely now supports Datadog APM dual shipping, allowing teams to reuse existing Datadog instrumentation while sending trace data from the Datadog collector directly to the Causely mediator without incurring additional egress costs.
This release also extends Datadog support to services running outside Kubernetes (such as standalone EC2 instances). As a result, Causely can now construct a complete, accurate causal model across heterogeneous environments, a prerequisite for:
- Confidently validating reliability in pre-production (load tests, release candidates)
- Continuously assuring reliability in production
- Detecting drift or regressions caused by infrastructure or configuration changes
Datadog monitors can also be ingested as symptom identifiers, enriching Causely's understanding of the environment.
Expanded Dynatrace Support for Mixed Container Platforms
Causely's Dynatrace integration has been expanded beyond Kubernetes to include ECS-based services. This ensures that teams using Dynatrace across multiple container platforms get a unified dependency and service map that reflects how the system actually behaves under change.
Enhancements include:
- Cross-platform stitching between ECS and Kubernetes workloads
- Service-level metric ingestion for more reliable behavior modeling
- Improved tracing fidelity for clearer dependency pathways
Together, these improvements help Causely maintain an end-to-end causal understanding of services regardless of where they run, strengthening reliability assurance across hybrid environments.
Did you know?
Causely allows you to automatically remediate resource contention issues directly from the UI, helping you restore performance faster, reduce time to resolve, and keep services within SLOs. When Causely identifies a deterministic Resource Contention root cause, you can trigger automated remediation or apply a guided fix with one click. Learn more.
Minor Improvements
- Added the ability to filter on external services
- Added support for multitenant Loki
- Improved Ask Causely search by enabling lookup by entity name
- Reduced duplication of services across Docker, Nomad, and Kubernetes environments
- Updated default SLO burn rate threshold from 2 to 4, making urgent root causes more reflective of sustained reliability degradation rather than short-lived spikes
- Improved snapshot reliability by fixing an issue that sometimes caused snapshot generation to time out in the UI
- Reduced Beyla log verbosity so logs remain clean and actionable without excessive noise
- Added notifications for missing agent coverage on nodes, making it easier to detect when parts of the infrastructure are not reporting the telemetry required for complete reliability analysis
November 24, 2025
Version v1.0.103Reliability Delta
Reliability Delta lets you compare two snapshots of your environment to understand whether reliability has improved, regressed, or meaningfully changed. Snapshots capture the state of your services, dependencies, and infrastructure over a defined window (up to two hours).
By comparing snapshots, Causely highlights differences in reliability, service behavior, root causes, and resource utilization, helping you evaluate release candidates, validate load tests, and confirm production stability with confidence.
OTel Collector Support for Prometheus Metrics
Causely now supports ingesting Prometheus metrics via the OpenTelemetry Collector. This allows teams already standardizing on OTel pipelines to route metrics, alongside existing exporters for Kafka, MongoDB, MySQL, Postgres, RabbitMQ, Redis, and common language runtimes, directly into Causely through your OTel collector.
Did you know?
Causely provides native cloud integrations with Azure, GCP, and AWS to bring your cloud resources and managed services into your causal model.
Minor Improvements
- Improved remediation messages for external services, making recommended actions clearer.
- Added detail on attribute updates for configured integrations, making it easier to verify that data is flowing correctly into the mediator.
- Enhanced presentation of key evidence in the Root Cause Summary view for faster analysis.
- Reduced noise in Beyla tracing by ignoring Datadog Trace API endpoints.
- Increased relevance of Datadog event ingestion by pulling only the last hour of events.
November 12, 2025
Version v1.0.101MCP Server: Causely's Causal Reasoning Engine, Now in Your IDE
The new MCP Server gives you direct access to Causely's Causal Reasoning Engine (CRE) from any MCP-compatible IDE.
You can now pull the full causal context of an inferred cause to automatically generate a code fix and a pull request, without leaving your workflow. This capability is in Early Access; let us know if you'd like to try it in your environment.
Read the blog post announcing MCP Server for IDEs: Introducing Causely’s MCP Server.
Telemetry Integrations in the UI
You can now add and manage telemetry data sources directly in the Causely UI. The Integrations experience shows:
- The health of all configured sources
- The last discovery time and data retrieved
- Recommendations for which data sources to prioritize next
By expanding your telemetry coverage, Causely builds a more complete causal model of your system, improving precision in root cause inference and proactive detection.
Adding additional instrumentation enhances Causely's ability to model service-to-service communication and automatically detect reliability risks.
Background Operation Root Causes
Causely's causal model now includes background operations such as asynchronous message consumers (Kafka, RabbitMQ, and more).
This enables automatic inference of slow consumer root causes for message-driven workloads.
Expanded Elasticsearch Support
Causely now supports additional Elasticsearch indices, ensuring that the most relevant logs are linked to each inferred root cause — giving you the “why” behind degradations with more context and precision.
Did you know?
Not only can Causely leverage Grafana Alloy, Loki and Beyla as telemetry sources, but you can also use the Grafana Plugin to bring Causely's causal insights directly into your Grafana dashboards. Learn more in the docs.
Bug Fixes and Minor Improvements
- Automatically discover Docker services exposed on host IPs for more complete topology mapping
- Make OpenTelemetry sample rate configurable to maintain mediation health
- Show active scope and filter details in the Topology view
- Update service graph to display intermediate services along route destinations
More Releases
Go to the changelog page.