Multiple model-training workflows
Choose a guided run, configure the model, data, and method directly, or write custom training logic including a loss function, trainer, and reinforcement-learning loop.
Fireworks AI
Paid
A model training and inference platform for teams building and serving specialized AI.
什么是 Fireworks AI？
Fireworks is an AI model training and inference platform for developing specialized systems from open models and serving them in production. Teams can start with hosted models, adapt models using their own data or training logic, and deploy checkpoints through serverless or dedicated GPU options. The platform is aimed at developers and organizations building applications that need customized model behavior, scalable inference, or control over the training-to-production workflow.
Fireworks AI 能做什么？
Fine-tuning open models
Customize supported open models with supervised or preference fine-tuning, including LoRA and full-parameter options. Vision-language-model supervised fine-tuning with images is billed by training tokens.
Serverless inference
Use hosted models without managing dedicated GPUs, with prepaid per-token billing. Standard, Priority and Fast serving paths are available for supported models.
Dedicated and reserved serving
Use on-demand GPU deployments for dedicated capacity, or arrange reserved capacity and higher quotas.
Production handoff and deployment
Training checkpoints can be deployed to production, and the platform supports serving open models as well as versions trained by the customer.
OpenAI- and Anthropic-compatible inference
The serverless inference interface is described as compatible with OpenAI and Anthropic APIs, enabling use through familiar API patterns.
使用场景
“Domain-specific enterprise assistants”
Fine-tune models on company language, taxonomies, and workflows, then use them to classify queries, summarize internal material, and produce actionable responses.
“Knowledge search across company sources”
Use model APIs to classify queries and summarize relevant material in an enterprise search or retrieval-augmented generation application.
“AI coding products”
Serve or train models for code assistants and editing workflows where inference throughput, latency, and reinforcement-learning workloads are relevant.
“High-volume production AI”
Use serverless serving for token-based usage or dedicated GPU deployments when an application needs dedicated capacity, higher rate limits, or deployment controls.
常见问题
Can I use hosted models without deploying dedicated GPUs?
Yes. Serverless inference offers hosted models with usage-based billing. Dedicated on-demand and reserved deployment options are also available.
Can I train on my own data?
Yes. Supported models can use supervised or preference fine-tuning with LoRA or full-parameter methods. Training can follow a guided, configuration-led or custom-code workflow. Model and method availability varies by offering.
How is inference billed?
Serverless input, output and cached tokens are charged against a prepaid balance. Auto Reload can replenish the balance, or a monthly spend limit can cap usage. On-demand deployments charge per GPU second; region-restricted deployments carry a premium.
How is training billed?
Managed supervised/preference fine-tuning is charged per million training tokens. Serverless Training API usage charges for prefill, sampling and training tokens, while dedicated training charges by GPU time. Model-specific promotions can have an expiry date.
What is needed to call the API?
Create a Fireworks API key through the dashboard or CLI and send it as a Bearer token in the Authorization header. OpenAI- and Anthropic-compatible inference APIs are documented.
Can I serve custom checkpoints?
Yes. The platform supports deploying training checkpoints and serving customer-trained model versions. Choose the model and serving mode for the workload.
快速信息
- Product category
- AI model training and inference platform
- Platform domain
- fireworks.ai
- Core users
- Developers, product teams, and AI infrastructure teams
- Inference options
- Serverless, on-demand, and reserved deployments
- Training options
- Guided, configuration-led, and custom-logic workflows
- Pricing model
- Paid usage-based inference and training; dedicated GPU and reserved capacity options
Fireworks AI 流量分析
流量数据仅供参考。
- 域名评分
- 80
Fireworks AI 替代品
UbiOps
ubiops.com
Model serving and orchestration for AI teams deploying workloads across cloud and private infrastructure.
SGLang
www.sglang.io
SGLang is an open-source serving framework for large language and multimodal models. It helps developers deploy model servers and query them through OpenAI-compatible endpoints across supported GPU, CPU, TPU, NPU, and XPU platforms.