Availability modes

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When to use each mode

• Self-serve. Start runs, and start more of them, without contacting Baseten.
• Billed for usage. You pay for the tokens you train and sample, so time spent reading results or revising a reward function between steps costs nothing.
• Nothing to manage. You create a run and train, with no deployments to size, start, or shut down.

• Models and configurations serverless doesn’t support. Every model in the catalog trains in the dedicated mode, including models, sequence lengths, and LoRA ranks that serverless doesn’t support.
• Scaling out a run, especially asynchronous RL. Add trainer replicas for update throughput and sampler replicas for rollout throughput, and use spot capacity for interruptible runs that resume from checkpoints.
• Planned, sustained runs. In the dedicated mode, a run’s deployments belong to it for its whole duration. To plan for a large run, request more access.

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How Loops chooses a mode

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Choose a mode

from baseten.loops import ServiceClient

training_client = ServiceClient().create_lora_training_client(
    base_model="zai-org/GLM-5.3-Flash",
    scope="serverless",
)

BASETEN_LOOPS_SCOPE=serverless uv run python train_loops.py

curl -s https://api.baseten.co/v1/loops/trainers \
  -H "Authorization: Bearer $BASETEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "session_id": "<session_id>",
    "base_model": "zai-org/GLM-5.3-Flash",
    "scope": "serverless"
  }'