Endpoint Candidates: evaluate and promote inference endpoints

Modal has introduced generally available Endpoint Candidates, a feature for Dedicated Endpoints that lets users stage and evaluate alternative model deployments using mirrored samples of production traffic. Candidates can test new checkpoints, different models, or custom weights from Hugging Face repositories or Modal Volumes while preserving the production endpoint’s responses for users, and they provide separate URLs for quality evaluations alongside dashboard metrics and load benchmarks for latency and throughput. Once validated, a Candidate can be promoted through the existing production endpoint URL, while other Candidates remain available for further testing, with standard Modal compute pricing applying to mirrored workloads. To prevent slow or failing mirrors from affecting production, Modal’s Pingora-based proxy sends request streams through independent bounded buffers, cancels lagging mirrored requests when buffers or shared memory budgets are exceeded, and treats mirroring as best-effort. Modal plans to expand Candidate controls, comparison metrics, and automation, positioning the feature as a testing environment for automated inference optimizations targeting improved latency, throughput, and cost.

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