Together.ai Needs a 4x Accelerator to Keep Up — NexaAPI Was Already Fast & Cheap
Together.ai announced ATLAS, an adaptive ML system to speed up their LLM inference. However, this reveals the complexity and cost of their infrastructure. In contrast, NexaAPI offers a simpler, cheaper API access to top AI models without the overhead.
Why it matters
The article highlights the tradeoffs between complex, high-performance AI systems and simpler, more accessible API solutions, which is an important consideration for developers choosing AI platforms.
Key Points
- 1Together.ai built a complex adaptive inference system (ATLAS) to improve their performance
- 2ATLAS uses speculative decoding, runtime learning, and automatic tuning to achieve up to 2.65x faster inference
- 3Together.ai's infrastructure is optimized for enterprise use, not indie developers or small teams
- 4NexaAPI provides a simpler, more affordable API access to top AI models without the overhead of Together.ai's system
Details
Together.ai announced ATLAS, an adaptive ML system that uses speculative decoding, runtime learning, and automatic tuning to achieve up to 500 tokens/second on DeepSeek-V3.1 and 460 TPS on Kimi-K2. This reveals that Together.ai's standard inference was slow enough that they needed to build a complex system to improve it. The article suggests that this complexity comes with a cost, both in terms of infrastructure management and pricing. In contrast, NexaAPI focuses on providing the simplest, most affordable API access to top AI models, without the need for custom training pipelines, runtime-learning systems, or GPU cluster management. NexaAPI's approach is better suited for solo developers and small teams who don't want to deal with the overhead of enterprise-grade AI infrastructure.
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