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

You can filter the reports based on different dimensions (e.g. service, team, environment, service) 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.

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).

This allows to focus on services being tagged, e.g., with Tier 0 - Mission Critical and drill down on reliability 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

Risk Distribution

See how many of your services fall into the low, medium and high risk levels. Easy to share with stakeholders the value of your reliability work by showing trending of services into low risk levels.

Example distribution of services by risk level

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

Experiment Runs

The Experiment Runs report gives you an overview of all experiment runs that have been executed — including their outcomes, what triggers 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

Attack Types

Identify which attacks your teams have used most frequently.

Example experiment run attack types

Trigger

Check out what typically triggers an experiment run, e.g., API, CLI, UI, or schedule.

Example experiment run trigger

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

Result (Completed vs. Failed)

Compare the portion of completed experiment runs to failed experiment runs to identify the frequency of identifying issues.

Example experiment run completed vs. failed

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

Issues Fixed

Identify how many experiment runs turned from failed to completed. We count experiment runs completed that were immediately preceded by a failed experiment run.

Example experiment run showing fixed issues

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

Creation Channel

Identify which channel is used the most across your teams to create an experiment: UI, API, or CLI

Example of experiment creation channels

Creation Method

Identify which method is used the most across your teams to create an experiment: From scratch, template, or advice

Example of experiment creation method

Others

These reports give you an overview of the adoption of Steadybit across your organization.

You can filter the reports by timeframe.

Users

Identify the progress you have made to roll out Steadybit in your organization by seeing the number of invited users.

Example of users

Teams

Easily report on the numbers of teams having access to a safe Chaos Engineering in your organization.

Example of teams

Environments

Find out how many environments you have created to roll out a safe Chaos Engineering across your organization.

Example of environments