Deploying to the Timefold Platform (Preview)

Explainability: Score analysis breaks a solution down constraint-by-constraint, so consumers can see why the solver made the decisions it did, and what the impact would be of overriding one.

Recommendations: the Recommendation API suggests the best fit for a new assignment without a full re-solve.

Performance: multithreaded solving, nearby selection, and constraint profiling let the solver scale to larger datasets.

Hosting and scaling: the platform builds, stores, and runs the container image, scaling it to the number of concurrent solve requests, so you’re not managing a Kubernetes deployment or a fleet of containers.

Compliance: the platform is ISO and SOC certified, so you don’t have to pursue and maintain those certifications yourself for your model’s hosting. See trust.timefold.ai for details.

A multi-tenant REST gateway: consumers of your model authenticate with API keys scoped to a tenant, so authentication, rate limiting, and per-customer isolation come built in.

Role-based access control: member management and roles gives tenants a built-in User/Administrator permission model, distinguishing who can view datasets from who can manage your model’s configuration profiles, API keys, and members, so you don’t have to build your own authorization layer.

Solve queue: when concurrent solve requests exceed provisioned resources, the solve queue automatically queues and runs them as capacity frees up, so you don’t have to implement your own request throttling or backpressure.

Audit logging: configuration changes to your model, such as updates to a configuration profile, are automatically recorded in the audit log, with who made the change and when, without you building your own change-tracking system.

Result delivery: consumers receive results via webhooks, server-sent events, or polling, so you don’t have to design your own asynchronous job-notification mechanism.

Self-service configuration profiles: consumers can add their own configuration profiles (termination limits, thread counts, constraint weight overrides) without opening a pull request against your model.

Parallel versions and staggered releases: because a model’s identifier includes its API version, you can run multiple major versions of your model side by side and let consumers migrate at their own pace. Within a version, you can stagger a release the same way Timefold does for its own models: register a new implementation under its own registration key, restricted to a subset of tenants (for example a staging tenant), while other tenants keep running the previous implementation. See Registration key for how registration keys make this possible.

Incremental replanning: the /from-patch endpoint lets consumers apply small, targeted changes to an existing dataset instead of resubmitting the full input, with built-in traceability and versioning between revisions, so you don’t have to design your own patch protocol or dataset lineage tracking.

Secrets management: if your model calls out to external systems, for example through webhooks, secrets management lets consumers store credentials encrypted and write-only, instead of you building your own credential storage.

Maps service: for routing models, the maps service calculates and caches distance and travel matrices for you, with incremental updates, throttling, and concurrency guarding against the map provider, instead of you integrating and managing a map provider yourself. It supports multiple providers out of the box, and consumers can configure which one to use through your model’s configuration profile.

A UI for your Model API consumers: the platform UI lets consumers submit datasets, inspect the resulting plan, and read a constraint-by-constraint score analysis, without you building any of that tooling yourself.

Dataset organization: consumers can name, tag, search, and filter their datasets out of the box, as described in categorizing datasets, instead of you building dataset management UI.

Dataset comparison: comparing datasets lets consumers compare metrics, scores, and constraint breakdowns across multiple datasets side by side, including radar charts and saved comparisons, without you building any comparison tooling.

Insights: Insights tracks metric trends across datasets over time, so consumers can spot whether operational planning is improving or degrading, without you building your own analytics dashboard.

Experiments: experiments let consumers run many dataset and configuration-profile combinations in one batch and compare the results, including benchmarking different model versions against each other, instead of you building your own scenario-testing or benchmarking harness.