Dedicated model deployments
Deploy open-source, custom, proprietary, and fine-tuned models as dedicated inference services for production workloads.
Baseten
Freemium
Baseten is an inference platform for deploying, serving, and scaling open-source, custom, and fine-tuned AI models. It supports managed cloud, self-hosted, hybrid, and dedicated deployments for teams running production AI workloads.
Basetenとは？
Baseten is an AI inference platform for deploying and operating open-source, custom, and fine-tuned models in production. It combines the Baseten Inference Stack with managed infrastructure, model APIs, autoscaling, and deployment options across Baseten Cloud, customer VPCs, or hybrid environments.
The platform supports dedicated model serving, pre-optimized Model APIs for prototyping and evaluation, and training jobs on the same stack. It is aimed at teams that need to move from model development to reliable production inference, including workloads involving LLMs, image generation, transcription, text-to-speech, embeddings, and compound AI systems.
Basetenでできること
Pre-optimized Model APIs
Access supported models through ready-to-use APIs for testing workloads, prototyping products, or evaluating model options without building a serving stack first.
Cross-cloud autoscaling
Scale replicas across clouds and regions according to incoming traffic, with the stated goal of maintaining latency and avoiding unnecessary compute usage.
Inference-optimized infrastructure
The Baseten Inference Stack includes performance work such as custom kernels, decoding techniques, caching, GPU provisioning, and cold-start optimization.
Flexible deployment locations
Run workloads in Baseten Cloud, in a customer VPC through self-hosted deployment, or in a hybrid setup that adds Baseten Cloud capacity.
Training and compound AI tooling
Run training jobs with the Loops SDK and deploy trained models to the same inference stack; Baseten Chains provides infrastructure for compound AI systems.
利用シーン
“Production LLM serving”
Teams building customer-facing applications can deploy custom or open-source language models and scale inference as request volume changes.
“Rapid model evaluation”
Product and engineering teams can use pre-optimized Model APIs to test supported models, prototype a workload, or compare options before committing to a dedicated deployment.
“Real-time audio applications”
Voice-agent, phone-call, translation, and transcription products can use the platform’s stated support for real-time audio streaming, speech-to-text, and text-to-speech workloads.
“Image generation and workflows”
Teams can serve custom image-generation models or ComfyUI workflows, including fine-tuned models for a particular application.
“Training-to-production workflows”
Model teams can train models with Baseten’s Loops SDK and move them onto the Baseten inference stack for deployment, keeping training and serving within the same platform.
よくある質問
What kinds of models can Baseten deploy?
Baseten states that it supports open-source, custom, proprietary, and fine-tuned AI models. Its site also highlights LLM, image-generation, transcription, text-to-speech, embedding, and compound AI workloads.
Where can models run?
Deployments can run in Baseten Cloud, in customer VPCs through self-hosted deployment, or in a hybrid arrangement using both customer infrastructure and Baseten Cloud capacity.
Does Baseten provide ready-made model APIs?
Yes. Baseten offers pre-optimized Model APIs for supported models, intended for testing new workloads, prototyping products, and evaluating models. The pricing page lists Model API charges per one million tokens.
How is dedicated inference priced?
The pricing page describes dedicated deployments as pay-as-you-go compute, billed by usage down to the minute, with volume discounts available. Rates vary by instance type and should be checked on the current pricing page.
Can Baseten also run training jobs?
Yes. The site describes on-demand compute, developer experience, and infrastructure for training jobs, and says models trained with the Loops SDK can be deployed to production inference on the same stack.
クイック情報
- Category
- AI inference platform
- Primary users
- AI, ML, and product engineering teams
- Deployment options
- Baseten Cloud, self-hosted, hybrid, and dedicated deployments
- Model types
- Open-source, custom, proprietary, and fine-tuned models
- Pricing model
- Pay-as-you-go plans; Model APIs priced per 1M tokens and dedicated deployments by compute usage
- Training workflow
- Train with the Loops SDK and deploy on the Baseten inference stack
Basetenの代替品
IBM watsonx.ai
www.ibm.com
IBM watsonx.ai is an enterprise AI development studio for building predictive, prescriptive, and generative AI solutions. It supports AI builders, data scientists, and developers across model development, customization, retrieval-augmented generation, deployment, and lifecycle management.
Together AI
together.ai
Together AIは推論、ファインチューニング、GPUクラスター、サンドボックス、マネージドストレージに対応するAIクラウドプラットフォームです。
Bento
www.bentoml.com
Bento is an inference platform for packaging, deploying, optimizing, and operating AI and machine-learning models at scale. It supports open and custom models across cloud, on-premises, Kubernetes, and bring-your-own-cloud environments.
Prodia
prodia.com
Prodia is a multi-silicon inference platform focused on video generation. It develops AI model implementations across different hardware to balance cost, output quality, and performance.
DigitalOcean
digitalocean.com
本番環境のAIアプリを構築・デプロイ・拡張できるAIネイティブなクラウドプラットフォーム。
ModelArk
www.byteplus.com
ModelArk is BytePlus's one-stop large language model service platform for organizations building, deploying, and scaling AI applications. It is positioned within BytePlus's broader AI-native cloud portfolio.