Choose a Training Path

Make three choices independently:

Workflow: Managed Fine-Tuning or Training API.
Infrastructure: Serverless or dedicated, only when you choose Training API.
Interaction surface: Coding agent, UI, CLI or REST 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 UI, CLI, REST API, or a coding agent	You want to launch through the Python SDK, cookbook, or a coding 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
Coding agent + Fireworks training skill	Chooses the path, validates inputs, shows the full parameters and cost for confirmation, then runs and troubleshoots the workflow
Fireworks UI	Guided creation and monitoring for managed jobs
`firectl` or REST API	Reproducible managed job and resource automation
Python SDK and cookbook	Training API loops on serverless or dedicated infrastructure

Install the Fireworks training skill to bring your own coding agent.

Examples

Standard SFT from labeled JSONL

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

First custom GRPO experiment

Workflow: Training API
Infrastructure: Serverless when the model and LoRA workload are supported
Interface: Cookbook through Python or a coding agent

Sustained full-parameter RL

Workflow: Training API
Infrastructure: Dedicated
Interface: Cookbook or direct SDK, optionally orchestrated by a coding 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.

Get Started

Serverless

Deployments

Models & Inference

Training

Fire Pass

Fireworks for Work

FireConnect

Administration

Security & Compliance

Integrations

Reference

Step 1: Choose the workflow

Managed Fine-Tuning

Training API

Step 2: If Training API, choose infrastructure

Serverless Training

Dedicated Training

Step 3: Choose how to interact

Examples

Standard SFT from labeled JSONL

First custom GRPO experiment

Sustained full-parameter RL

Before launch

​Step 1: Choose the workflow

Managed Fine-Tuning

Training API

​Step 2: If Training API, choose infrastructure

Serverless Training

Dedicated Training

​Step 3: Choose how to interact

​Examples

​Standard SFT from labeled JSONL

​First custom GRPO experiment

​Sustained full-parameter RL

​Before launch

Step 1: Choose the workflow

Step 2: If Training API, choose infrastructure

Step 3: Choose how to interact

Examples

Standard SFT from labeled JSONL

First custom GRPO experiment

Sustained full-parameter RL

Before launch