Choose a Training Path

Choose the Fireworks workflow, infrastructure, and interaction surface for your training task.

Make three choices independently:

1. Workflow: Managed Fine-Tuning or Training API.
2. Infrastructure: Serverless or dedicated, only when you choose Training API.
3. Interface: Your agent, the Fireworks UI, CLI or API, or Python SDK.

Step 1: Choose the workflow

Choose Managed Fine-Tuning when	Choose the Training API when
You need a standard SFT, DPO, ORPO, or RFT job	You need a custom loss, reward, rollout, trajectory, or optimizer-step loop
You want Fireworks to own the training loop and checkpoint lifecycle	You want to fork or write Python training logic
A supported model and managed configuration cover the task	You need inference in the loop, distillation, per-step diagnostics, or research algorithms
You want to launch through the UI, CLI or API, or your agent	You want to launch through the Python SDK or your agent

Managed Fine-Tuning

Standard jobs with a platform-managed loop.

Training API

Programmable loops built from cookbook recipes or the SDK.

Step 2: If Training API, choose infrastructure

Choose Serverless Training when	Choose Dedicated Training when
Supported LoRA SFT or RL covers the workload	You need full-parameter, DPO, ORPO, distillation, or broader model support
You want shared pooled compute with no trainer or sampler deployment provisioning	You need explicit trainer, deployment, checkpoint, reconnect, or promotion control
Per-token billing fits a small or bursty experiment	A sustained, highly utilized time-based run fits the workload
In-session sampling is sufficient	You need provisioned rollout or evaluation deployments

Serverless Training

Shared pooled trainer, no provisioning, per-token billing.

Dedicated Training

Provisioned trainer and deployment resources with explicit lifecycle control.

See the detailed serverless versus dedicated comparison.

Step 3: Choose how to interact

The interface does not determine the workflow or infrastructure:

Interface	What it does
Your agent	Uses the Fireworks training skill to configure, run, and troubleshoot training
Fireworks UI	Guided creation and monitoring for managed jobs
CLI or API	Reproducible managed job and resource automation
Python SDK	Custom Training API loops on serverless or dedicated infrastructure

The Fireworks CLI is called firectl. Cookbook recipes are recommended starting points for the Python SDK and can also be used by your agent.

Install the Fireworks training skill to use your agent.

Examples

Standard SFT from labeled JSONL

• Workflow: Managed Fine-Tuning
• Infrastructure: Managed by the platform; no Training API infrastructure choice
• Interface: Your agent, UI, CLI, or API

First custom GRPO experiment

• Workflow: Training API
• Infrastructure: Serverless when the model and LoRA workload are supported
• Interface: Python SDK with a Cookbook recipe, or your agent

Sustained full-parameter RL

• Workflow: Training API
• Infrastructure: Dedicated
• Interface: Python SDK with a Cookbook recipe, optionally orchestrated by your agent

Before launch

Verify current model support, shapes, access status, pricing, limits, and quota in the linked live pages. A coding agent asks for confirmation before upload, registration, paid inference, job creation, promotion, deployment, or another mutation. Material changes require approval again; promotion and deployment are confirmed separately.