Together AI vs Amazon SageMaker

Side-by-side comparison to help you choose the best tool.

Together AI

freemium
4.4 / 5.0

Together AI is an AI cloud platform for training and running open-source models at enterprise scale. It provides high-throughput inference for LLaMA, Mistral, FLUX, and other models, along with fine-tuning as a service. Together is used by AI startups and enterprises that want the economics of open-source models with the reliability of managed cloud infrastructure.

Best for: AI startups and enterprises wanting high-throughput open-source LLM inference with fine-tuning features at competitive cloud pricing
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Amazon SageMaker

paid
4.4 / 5.0

Amazon SageMaker is the leading fully managed ML platform for building, training, and deploying ML models at scale on AWS. Its features span data labeling, feature engineering, model training, automated tuning, and deployment - with SageMaker JumpStart providing pre-built models and tools. Used by thousands of enterprises for production ML workloads across every industry.

Best for: Enterprise data science teams on AWS needing a fully managed ML platform for the complete model development and deployment lifecycle
Visit Amazon SageMaker
Feature Comparison
Feature Together AI Amazon SageMaker
Pricing freemium paid
Category - -
Rating ★★★★☆ 4.4 ★★★★☆ 4.4
Best For AI startups and enterprises wanting high-throughput open-source LLM inference with fine-tuning features at competitive cloud pricing Enterprise data science teams on AWS needing a fully managed ML platform for the complete model development and deployment lifecycle
Views 64 65
Pros & Cons — Together AI
Pros
  • Best open-source LLM inference price-performance
  • Fine-tuning as a service is turnkey
  • High throughput for production workloads
Cons
  • Requires model knowledge — not plug-and-play like OpenAI
  • Support response times vary
Pros & Cons — Amazon SageMaker
Pros
  • Most mature managed ML platform
  • JumpStart provides hundreds of pre-built solutions
  • Scales to enterprise-level training workloads
Cons
  • Complex pricing with many components
  • Steep learning curve for full feature utilisation
Key Features — Together AI
  • High-throughput open-source LLM inference
  • Fine-tuning as a service
  • Serverless & dedicated deployments
  • LLaMA, Mistral & FLUX APIs
  • Batch inference
Key Features — Amazon SageMaker
  • Managed ML training & deployment
  • SageMaker JumpStart (pre-built models)
  • Automated hyperparameter tuning
  • Real-time & batch inference
  • Feature Store & data processing

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