| description | Monitor and manage project Spicepod instances and deployments. |
|---|---|
| icon | rocket-launch |
Navigate to the Deployments tab and click on Create Deployment.
Each deployment listed on the Deployments tab reports a status derived from the live state of its instances:
| Status | Meaning |
|---|---|
| Pending | The deployment is queued or created; no instances have started yet. |
| Deploying | Instances are starting. |
| Loading | Instances are running and loading their initial datasets. |
| Ready | All replicas are ready and serving traffic. |
| Ready with errors | All replicas are ready and passing health checks, but the runtime is reporting errors. |
| Unhealthy | All required replicas are ready, but at least one is failing its health check. |
| Terminating | The deployment is being replaced by a newer deployment that is ready to take traffic. |
| Succeeded | The deployment completed and its instances are no longer reporting live state. |
| Paused | The project is paused, so no deployment is serving. |
| Created | No live state is available for the deployment, typically because it has already been replaced. |
| Failed | The deployment failed. |
Ready with errors distinguishes a deployment that started successfully from one that works. The instances pass their health checks, but the runtime inside them is reporting problems — a dataset that cannot connect, or a model that fails to load. A failing health check is the stronger signal, so a deployment that is both unhealthy and reporting errors shows Unhealthy.
When a deployed Spicepod reports errors or warnings, the portal collects them into a single Issues feed instead of leaving them in the log tail.
Issues are surfaced in four places:
- A banner on every page of the project, listing the most recent error and the number of other errors. The banner covers errors only — warnings appear in the Issues panel.
- An error count on the Deployments tab in the project navigation.
- An Issues panel on the Deployments page, and on each instance page scoped to that instance. Each panel lists up to five issues and links to the full list.
- A dedicated Issues page for the project, listing every issue without a limit.
- An indicator on the affected row in Datasets and Models, when an issue can be attributed to a component.
The feed combines runtime ERROR and WARN log lines with the reported status of each dataset. Repeats of the same failure collapse into one row with an occurrence count, so a connector retrying every second appears once rather than hundreds of times.
Issues are ordered errors first, then by how often they occurred, then by how recently. A panel capped at five rows therefore shows the errors before any warning, and the panel header keeps the full error and warning counts for the project or instance. View all N issues in the panel footer opens the dedicated Issues page, which lists the entire set.
Each row shows where the issue came from, how many times it occurred, when it was last seen, the dataset or model it was attributed to, and the instance that reported it. Show details expands the full text, including stack traces. The link on the row opens the originating instance's logs filtered to the issue, or the component's page for an issue reported by a dataset.
{% hint style="info" %} Dismissing an error hides that specific error. A different failure raises the banner again. {% endhint %}
A project with no issues shows no banner, no count, and no panel. The dedicated Issues page remains reachable and reports that no issues were detected.
Each issue row has a Get AI help action that analyzes that issue on demand. The analysis reads the project's recent runtime logs and returns a likely root cause, a short numbered list of fix steps, links to the relevant documentation, and a confidence level.
Results are generated per issue and are not produced until the action is used.
{% hint style="warning" %} An AI analysis is a suggestion, not a diagnosis. Verify each step against the project's own configuration before applying it. {% endhint %}
Navigate to the Deployments tab and click on the Logs for the selected instance.