Baseten vs Together AI

Baseten emphasizes bleeding-edge performance with custom kernels, advanced caching, and decoding techniques for low-latency inference. It offers pre-optimized Model APIs (e.g., Kimi K2.6, DeepSeek V4) for rapid prototyping and dedicated inference for custom models. Unique features include Baseten Loops training SDK for frontier RL and Baseten Chains for compound AI, as well as specialized services like ultra-low-latency transcription and real-time audio streaming. In contrast, Together AI focuses on a full-stack AI cloud with serverless inference, batch inference (up to 30B tokens), and dedicated model/container inference on GPU clusters (GB300, H100). Together AI provides a model library with open-source models (DeepSeek V3.1, Llama 4, Qwen3-VL) and fine-tuning platform with research techniques. Its kernel optimization includes FlashAttention-4 and ATLAS runtime-learning accelerators. Together AI also offers sandbox development environments and managed storage with zero egress fees. Baseten is better for custom model deployment and real-time audio/image generation, while Together AI excels in batch workloads and fine-tuning open-source models.

Both Baseten and Together AI use contact-based pricing, meaning costs are not publicly transparent and likely tailored to usage and scale. Baseten's pricing likely reflects premium features like custom kernels and real-time low-latency inference, which may be more expensive but optimized for speed. Together AI's pricing for serverless and batch inference may be more cost-effective for large-scale token processing (up to 30B tokens per model). Both platforms are not ideal for small hobby projects due to lack of transparent pay-as-you-go options. Enterprise users can negotiate dedicated infrastructure and support. Together AI's claim of '60% lower costs' through optimizations suggests aggressive pricing for competitive workloads, while Baseten's value is in achieving the fastest runtimes for custom models.