> For the complete documentation index, see [llms.txt](https://docs.steadybit.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.steadybit.com/use-steadybit/reporting.md). # Reporting Steadybit's integrated reporting feature gives you a comprehensive overview of your service reliability risk, your experiment activity, and your adoption of Steadybit across users, teams and environments. Use it to track progress over time, share evidence with stakeholders, and spot regressions across your infrastructure. ![Example of a report showing average service risk](https://853194531-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FZLJS2c8EXBcu8PiwteIJ%2Fuploads%2Fgit-blob-996b9b9343c4d5e72fd33c73ac95ffec29964c49%2Fservice-risk-average.png?alt=media) You can filter the reports along different dimensions (e.g. service, team, environment) or use the legend's labels below any report to filter the data shown in the graphs. All charts can be downloaded as CSV, PNG or PDF. ## Service Risk The **Service Risk** reports answer questions like how risky are services on average, how are they distributed across risk levels, and which reliability categories are driving the risk. [Learn more about Steadybit's service risk](/use-steadybit/services.md#risk). Each report writes a fresh data point per service whenever the service's risk changes, or — at the latest — once per day. This means your historical timeline always reflects the property values that were in effect at the time. You can filter Service Risk reports by: * Timeframe * Teams * Environments * Services * Service Properties (enum-typed [custom properties](/use-steadybit/experiments/properties.md)). This lets you focus on services tagged, for example, with `Tier 0 - Mission Critical`, and drill down on [reliability categories](/install-and-configure/manage-service-profiles.md#categories) like 'Scalability'. ### Average Risk Over Time Track the rolling average risk across your services. Useful as a single-pane health number to share with stakeholders or to spot regressions when new services are onboarded or a service profile changes. ![Average service risk over time](https://853194531-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FZLJS2c8EXBcu8PiwteIJ%2Fuploads%2Fgit-blob-996b9b9343c4d5e72fd33c73ac95ffec29964c49%2Fservice-risk-average.png?alt=media) ### Risk Distribution See how many of your services fall into the low, medium and high risk levels. An easy way to show stakeholders the value of your reliability work, as services trend towards the lower risk levels. ![Example distribution of services by risk level](https://853194531-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FZLJS2c8EXBcu8PiwteIJ%2Fuploads%2Fgit-blob-f7307d29d5e3ff704c4c9dfb656cb5a00918dbc1%2Fservice-risk-distribution.png?alt=media) ### Risk by Category Break down the average risk per reliability category (e.g. Redundancy, Scalability, Dependencies) merged globally across all service profiles. Use this to identify which dimensions of reliability need the most investment across your services — independent of which service profile a service uses. ![Example risk by category](https://853194531-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FZLJS2c8EXBcu8PiwteIJ%2Fuploads%2Fgit-blob-e72494171d8237efa800005fafb5b73cc5032999%2Fservice-risk-category.png?alt=media) ## Experiment Runs The Experiment Runs report gives you an overview of all experiment runs that have been executed — including their outcomes, what triggered them, and how they move between completed and failed over time. Use it to make experiment activity visible across teams and to spot when chaos coverage starts to drift. You can filter the reports by the following criteria: * Timeframe * Teams * Environments * Services ### Number of Runs Find out how many experiments your teams have run in total. ![Example number of experiment runs](https://853194531-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FZLJS2c8EXBcu8PiwteIJ%2Fuploads%2Fgit-blob-9de5dd5f65073f6b3361dd6d8b5ed7e22227c3b9%2Fexperiment-runs-number.png?alt=media) ### Attack Types Identify which attacks your teams have used most frequently. ![Example experiment run attack types](https://853194531-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FZLJS2c8EXBcu8PiwteIJ%2Fuploads%2Fgit-blob-4ee9a8d5711f6c47134162bc64e2ccd9d6a42874%2Fexperiment-runs-attack-types.png?alt=media) ### Trigger Check out what typically triggers an experiment run, e.g., API, CLI, UI, or schedule. ![Example experiment run trigger](https://853194531-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FZLJS2c8EXBcu8PiwteIJ%2Fuploads%2Fgit-blob-1446dcf4a569217a35fcc36d899f0ccccafb664c%2Fexperiment-runs-trigger.png?alt=media) ### Result Drill down into the experiment runs by the result and compare the numbers of completed, canceled, failed, and errored experiment runs. ![Example experiment run result](https://853194531-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FZLJS2c8EXBcu8PiwteIJ%2Fuploads%2Fgit-blob-a3d45525716ea47923c6169772360e90e9be54a6%2Fexperiment-runs-results.png?alt=media) ### Result (Completed vs. Failed) Compare the proportion of completed experiment runs to failed ones to see how often experiments surface an issue. ![Example experiment run completed vs. failed](https://853194531-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FZLJS2c8EXBcu8PiwteIJ%2Fuploads%2Fgit-blob-58b068384eb807546270284aa241680460ba2acd%2Fexperiment-runs-completed-failed.png?alt=media) ### Issues Discovered Identify how many experiment runs turned from completed to failed. We count experiment failures that were immediately preceded by a completed experiment run. ![Example experiment run discovering issues](https://853194531-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FZLJS2c8EXBcu8PiwteIJ%2Fuploads%2Fgit-blob-d929d295eaa3abff3806d4968881a67ba3a41758%2Fexperiment-runs-issues-discovered.png?alt=media) ### Issues Fixed Identify how many experiment runs turned from failed to completed. We count completed experiment runs that were immediately preceded by a failed one. ![Example experiment run showing fixed issues](https://853194531-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FZLJS2c8EXBcu8PiwteIJ%2Fuploads%2Fgit-blob-006333244bd546f1c3fee3daaf08e03738ff1bc7%2Fexperiment-runs-issues-fixed.png?alt=media) ## Experiments The Experiments report gives you an overview of experiments that have been designed in your environment — how many designs exist, what channels teams use to create them, and which methods (from scratch, template, or advice) they prefer. Useful for tracking the spread of experiment authoring across the organization. You can filter the report by the following criteria: * Timeframe * Teams * Environments ### Number of Experiments Find out how many experiments your teams have designed in total. ![Example number of experiments](https://853194531-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FZLJS2c8EXBcu8PiwteIJ%2Fuploads%2Fgit-blob-4170b786d4a69d6995ae693e4e21b0440f323966%2Fexperiments-number.png?alt=media) ### Creation Channel Identify which channel your teams use most to create an experiment: UI, API, or CLI. ![Example of experiment creation channels](https://853194531-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FZLJS2c8EXBcu8PiwteIJ%2Fuploads%2Fgit-blob-4607c11526a7c94f4a6dcecaff788c068b213cfa%2Fexperiments-creation-channel.png?alt=media) ### Creation Method Identify which method your teams use most to create an experiment: from scratch, from a template, or from advice. ![Example of experiment creation method](https://853194531-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FZLJS2c8EXBcu8PiwteIJ%2Fuploads%2Fgit-blob-ab384edbff87f82012a42ef4f8434a82bb0cc9be%2Fexperiments-creation-method.png?alt=media) ## Others These reports give you an overview of the adoption of Steadybit across your organization. You can filter the reports by timeframe. ### Users Track the progress of your Steadybit rollout by following the number of invited users. ![Example of users](https://853194531-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FZLJS2c8EXBcu8PiwteIJ%2Fuploads%2Fgit-blob-d62107c53560885e16f06a88b7086662ab0c1753%2Fothers-users.png?alt=media) ### Teams Easily report on the number of teams that have access to safe Chaos Engineering in your organization. ![Example of teams](https://853194531-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FZLJS2c8EXBcu8PiwteIJ%2Fuploads%2Fgit-blob-40080d3a740070923ce876aca9692ef52ec0fd7c%2Fothers-teams.png?alt=media) ### Environments Find out how many environments you have created to roll out safe Chaos Engineering across your organization. ![Example of environments](https://853194531-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FZLJS2c8EXBcu8PiwteIJ%2Fuploads%2Fgit-blob-882a204282394025f93d880a3802e46a05a8ca0a%2Fothers-environments.png?alt=media)