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Your workflow operations, at a glance

The new Tilebox Console Dashboard puts workflow health front and center. See queue pressure, recent job outcomes, workflow logs, workspace activity, and usage in one operational view, then drill directly into the jobs and logs that need attention.

Open the Console Dashboard

Monitor your workflows and investigate issues.
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Sentinel-1 radar imagery, ready day or night

The new open_data.aws_earth.sentinel1 dataset provides credentials-free access to global Sentinel-1 Level-1 Ground Range Detected (GRD) scenes and their public AWS assets. Unlike optical sensors, Sentinel-1 uses synthetic aperture radar (SAR) to observe the surface through clouds and without sunlight, so you can keep analyzing conditions when optical imagery is unavailable.Query scenes by time, location, or satellite platform, then open the available polarization measurements as Cloud Optimized GeoTIFFs (COGs). Each datapoint also includes acquisition and orbit details, SAR metadata, a preview image, and the product, calibration, noise, and SAFE manifest files needed for further processing.These consistent, all-weather observations are well suited to flood and disaster mapping, sea-ice and maritime monitoring, forest disturbance detection, and tracking changes in crops, soil moisture, and infrastructure. You can move from a spatial query to a focused pixel read in one workflow, without configuring separate AWS credentials or downloading a complete scene first.

Create a Sentinel-1 radar image

Query a GRD scene and render a monochrome image from its public VV measurement.
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Understand and debug workflow jobs faster

The redesigned job details page makes it easier to understand what a workflow is doing, where it spends time, and why it failed. Job progress, execution statistics, tasks, traces, and logs now form one connected view, so you can move from the state of the whole job to the work of an individual task without losing context.Tasks appear in their workflow hierarchy instead of a flat list. Expand the branches you care about, follow state and timing through nested work, and navigate large jobs without loading the entire task graph at once.Select any task to see its input, timing, retries, compute location, execution trace, and logs together. A failed or slow task is no longer an isolated telemetry record: you can see where it sits in the workflow, inspect what it received, and trace exactly what happened during its execution.

Workflow observability

Learn how Tilebox connects job tasks, execution traces, logs, and runner context.
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Sentinel-2 imagery, ready to query and read

The new open_data.aws_earth.sentinel2 dataset provides credentials-free access to Sentinel-2 metadata and Cloud Optimized GeoTIFFs (COGs). You can start working with satellite imagery using only your Tilebox API key—no external data provider account, separate credentials, or storage configuration required.Query Level-2A scenes by time, location, cloud cover, or satellite platform, then resolve a result directly into Tilebox assets. From the same datapoint, you can inspect the available spectral bands, download a complete image, or open a COG remotely and read only the pixels covering your area of interest.This makes it practical to move from catalog search to image processing in one workflow: find a low-cloud scene, select a band, crop it to a geographic region, and pass the resulting data into your analysis without first downloading an entire scene.

Create a Sentinel-2 RGB image

Query a cloud-free scene and combine its red, green, and blue COG assets.
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Queryable custom dataset fields

Custom dataset schemas can now mark selected fields as queryable. Tilebox evaluates these field expressions on the server together with temporal, spatial, and collection filters, so clients only receive matching datapoints.Filters support comparisons, boolean logic, and null checks.
Queryable fields support strings, booleans, signed and unsigned integers, and floating-point values. Dataset authors select queryable fields before ingesting datapoints.

Filter by custom fields

Define queryable fields and filter matching datapoints.
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Assets and storage access

Dataset datapoints can now describe file assets and their storage locations. The Python SDK resolves that metadata into asset collections, and the asynchronous storage client can stream, download, or open those assets across supported storage providers.The GeoTIFF integration also supports remote COG access and window reads, so you can fetch only the pixels needed for an area of interest.Provider-specific storage clients are now deprecated. Migration guidance for existing integrations will follow.

Assets and storage

Learn how dataset metadata connects to files in object storage.
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Workflow Management and Operations

Tilebox now exposes more workflow operations across the Console, SDKs, CLI, MCP server, and runner deployments. You can manage workflow releases and clusters from the Console, filter jobs by compute location, schedule automations in local time, and start release runners from an official Tilebox container image.

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What changed

  • Workflow management in the Console. Create and edit workflows, inspect release history, tasks, runtime configuration, and artifacts, and deploy or undeploy releases across clusters. You can also delete workflows or unpublish individual releases without deleting existing jobs.
  • Redesigned cluster pages. The Console now provides a clearer cluster overview and dedicated detail pages for deployed workflow releases and tasks. From a cluster page, you can edit the cluster, deploy another workflow or release version, or undeploy a release. Protected default clusters are clearly marked.
  • Job filtering by cluster. Filter jobs by one or more cluster slugs in the Console, Python and Go SDKs, and CLI. This makes it easier to inspect work assigned to specific development, production, or specialized compute infrastructure.
  • Expanded MCP coverage. AI agents using the Tilebox MCP server can manage clusters, workflows, releases, and jobs; inspect logs and spans; view automations; and query decoded dataset datapoints, including spatial queries.
  • Timezone-aware Cron schedules. Cron automations now accept standard five-field expressions with lists, ranges, steps, named weekdays, and named months. Prefix a schedule with CRON_TZ=<timezone> to use an IANA timezone, or use helpers such as @hourly, @daily, @weekly, @monthly, and @yearly. Schedules without a timezone continue to use UTC.
  • Official runner image. The release-runner image is available at ghcr.io/tilebox/runner. It includes the Tilebox CLI, uv, Python 3.12 through 3.14, Git, Git LFS, and common geospatial build dependencies. The image runs tilebox runner start by default and can be configured for a cluster through environment variables.
For example, this schedule runs at 09:00 on weekdays in Vienna and follows daylight saving time automatically:

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Start here

Manage workflow deployments

Deploy workflow releases to clusters and control what release runners can execute.

Configure Cron triggers

Define recurring jobs with standard Cron expressions, timezone prefixes, and schedule helpers.

Deploy to your compute

Run workflow releases on compute environments you control.
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Agentic Workflows

Tilebox Workflows now supports workflow release publishing and cluster deployments. This adds a new release-based execution path to the workflow orchestrator: package a Python workflow project, publish an immutable release, deploy it to a cluster, run it on release runners, and inspect the resulting job logs and execution traces.The release path is designed for closed-loop workflow iteration by developers and agents. An agent can edit workflow code, publish a release, deploy it to a development cluster, submit a test job, inspect the failed task logs or spans, apply a fix, and retry or submit the next job without leaving the same command-line workflow.A typical iteration looks like this:

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What changed

  • Release runners. Release runners run in an environment you control, watch a cluster, load the deployed releases for that cluster, and execute compatible tasks without rebuilding the runner process for every workflow code change.
  • Workflow release publishing. A workflow is now a long-lived object with a stable slug, and each release captures a concrete version of the workflow project, runtime entrypoint, selected files, and discovered task identifiers.
  • Project-local workflow configuration. tilebox.workflow.toml binds a repository directory to a workflow slug, build inputs, runtime entrypoint, and optional deployment targets, so commands can use the local project as context.
  • Cluster deployments. Publishing and deploying are separate steps. A release can be deployed to one or more clusters, and different clusters can run different releases of the same workflow.

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Start here

Iterate on workflow releases with agents

Use an AI coding agent to edit, publish, deploy, run, inspect, and retry workflow releases.

Workflow configuration

Configure tilebox.workflow.toml, release contents, runtime entrypoints, and deployment targets.
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Workflow Observability

Tilebox Workflows now includes built-in observability for jobs and runners. Tilebox captures workflow logs, traces, task status, and runner context, then correlates them with jobs and tasks.The Console includes a built-in explorer for workflow observability, so you can inspect task logs, trace timing, failures, and runner behavior from the job view.See the Workflow observability documentation for examples and integration options.
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Optional Tasks

Subtasks can now be marked as optional when submitting them. If an optional task fails, the job continues instead of being canceled. Failed optional tasks are marked with the state FAILED_OPTIONAL, and any remaining queued sibling tasks in the same optional subtask tree are automatically SKIPPED. Tasks that depend on an optional task still execute even if it failed.This is useful for workflows where certain steps are not critical and their failure should not prevent the rest of the job from completing.See the optional tasks documentation for details and examples.
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MCP Server for Datasets and Workflows

Introducing the Tilebox MCP server, which provides tools for AI agents to access and interact with Tilebox datasets and workflows.
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Job List View: Complete Redesign and Filtering Improvements

The job list view in the Tilebox Console has been completely redesigned:
  • Infinite scrolling for long job lists
  • Improved filtering and search
    • Filter by state
    • Filter by automation
    • Filter by time range
    • Filter by name
    • Or an arbitrary combination of the above
  • Better readability and organization
  • Added progress indicators
  • Added execution stats
  • The same new filter options are also available in our Language SDKs
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Spatio-Temporal Explorer Redesign

The dataset explorer in the Tilebox Console has received a major upgrade for spatio-temporal datasets:
  • Overhaul of the Explorer view for Spatio-temporal datasets
  • Display datapoint footprints directly on the map
  • Display thumbnails and quicklooks of datapoints directly in the Console
  • Added code snippet for storage access to the Export as Code dialog
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Progress Indicators

User defined progress indicators for jobs are now available. See the progress documentation for more details.
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Go Client

Excited to announce the release of the Tilebox Go client!Features
  • Datasets client
  • Statically typed dataset types
  • CLI to generate dataset types
  • Workflows client
  • Go runners
To get started, check out the Go SDK documentation.
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Spatio-Temporal datasets

Spatio-temporal datasets are officially out, fully supported in all languages and available as a category to create in custom datasets!The core problems that spatio-temporal datasets solve are
  • finding relevant data quickly (e.g. all Sentinel 2 granules along the US coastline, last year),
  • storing auxiliary geographically coded data (e.g. weather station data, ground truth data),
  • cataloging higher level data and results
Here’s a short video on performance and core capabilities.We’re excited about this as cataloging has until now been an unsexy but hard problem, and it’s great to finally have a solution out thereMore information
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Custom Datasets

Create your own custom datasets!
  • statically typed
  • with clients in Python and Go
Use it to organize anything from telemetry, raw payload metadata, auxiliary sensor data, configuration data, or internal data catalogs.Quickstart
  1. Specify the data type in the Console
  2. Create a collection
  3. Use client.ingest() to ingest a xarray.Dataset or pandas.DataFrame
  4. Query away!
For detailed instructions, check out the Build a spatio-temporal catalog how-to guide.