Baseten vs Together AI
Detailed side-by-side comparison to help you choose the right tool
Baseten
Infrastructure
Inference platform for deploying AI models in production with high-performance infrastructure, cross-cloud availability, and optimized developer workflows.
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Starting Price
CustomTogether AI
đ´DeveloperAI Models
Cloud platform for running open-source AI models with serverless inference, fine-tuning, and dedicated GPU infrastructure optimized for production workloads.
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Starting Price
$0.02/1M tokensFeature Comparison
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đĄ Our Take
Choose Baseten if you need to deploy custom or fine-tuned models with dedicated infrastructure, full control over scaling, and enterprise compliance. Choose Together AI if you want a shared serverless API for popular open-source LLMs with transparent per-token pricing and no infrastructure management.
Baseten - Pros & Cons
Pros
- âIndustry-leading inference performance with reported 1500+ tokens/sec on optimized LLMs and sub-100ms latency for audio models
- âCross-cloud GPU availability across AWS, GCP, Azure, Oracle, and Coreweave reduces capacity bottlenecks during demand spikes
- âOpen-source Truss framework lets teams package any custom Python or PyTorch model without vendor lock-in
- âEnterprise-grade compliance including SOC 2 Type II and HIPAA, suitable for regulated industries like healthcare and finance
- âStrong support for compound AI applications via Chains, enabling multi-model pipelines with shared autoscaling
- âBacked by $135M+ in funding with proven customers including Descript, Writer, Patreon, and Bland AI
Cons
- âPricing is enterprise-oriented and not transparent on the public site, making cost estimation difficult for smaller teams
- âSteeper learning curve than simpler platforms like Replicate for developers new to model deployment
- âLimited free tier â only $30 in trial credits compared to more generous free tiers from competitors
- âPrimarily focused on inference, not training, so teams needing end-to-end MLOps must combine it with other tools
- âSome advanced optimizations (custom kernels, speculative decoding) require Baseten engineering involvement rather than self-serve configuration
Together AI - Pros & Cons
Pros
- âDramatically lower costs (5-20x) compared to proprietary models while maintaining quality
- âSuperior inference performance through custom optimizations and ATLAS acceleration
- âComprehensive fine-tuning capabilities with automatic deployment and scaling
- âOpenAI-compatible API enables seamless migration from existing applications
- âAccess to latest open-source models often before other hosting platforms
- âFull-stack platform covering inference, training, and GPU infrastructure
Cons
- âOpen-source models may not match GPT-4/Claude on highly complex reasoning tasks
- âOccasional capacity constraints during peak usage on popular models
- âFine-tuning requires ML expertise to achieve optimal results for specialized use cases
- âLimited proprietary model access (no GPT-4 or Claude integration)
- âDocumentation and community support less extensive than major cloud providers
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