Groq vs Together AI

Together AI and Groq serve different inference niches. Together AI provides a full-stack cloud with GPU clusters (GB300, H200, B200) and kernel optimizations like FlashAttention-4, enabling serverless inference for 100+ models, batch processing up to 30B tokens per model, and fine-tuning with proprietary ATLAS techniques. It also offers managed storage (zero egress), sandbox environments via CodeSandbox, and voice agents. Groq, by contrast, uses custom LPU chips designed solely for low-latency inference, achieving sub-200ms responses. Its strengths include an OpenAI-compatible API (two lines of code), a Batch API with 50% cost reduction, prompt caching for cheaper cache-hit responses, and built-in tools like web search and code execution. Groq also supports Orpheus TTS for real-time speech. While Together AI’s model selection is broader (100+ vs. day-zero support), Groq’s focus on latency and cost efficiency for real-time apps is unmatched. Both platforms now emphasize security (Together AI ISO 27001), but Together AI is better for training and large-scale batch, whereas Groq excels for latency-sensitive production inference.

Both platforms follow a freemium model. Together AI offers pay-as-you-go serverless inference with dedicated options (e.g., ~$0.50/hr for inference, ~$2/hr for fine-tuning) and no egress fees. Groq provides a free tier with rate limits; paid tokens cost $0.85 per million input tokens and $2.50 per million output, with a 50% discount for Batch API requests. Groq’s pricing is linear and predictable, which appeals to cost-conscious teams. However, Together AI can be more economical for high-volume batch processing using its dedicated clusters, especially for workloads exceeding 30B tokens. Groq’s recent $650M raise (June 2026) signals financial stability and potential future price adjustments. For real-time apps with moderate throughput, Groq’s predictable pricing is a win; for scalable, heterogeneous workloads, Together AI’s dedicated infrastructure offers better value. Users should consider token volume and latency needs: Groq’s free tier is generous for testing, while Together AI’s batch discounts favor production-scale async tasks.