Groq vs Together AI

When to pick Groq

• Latency is the product — voice agents, live chat, and real-time tools feel instant at LPU speeds of hundreds to 1,000+ tokens per second, a gap GPU-based providers don’t close.
• You want the cheapest path to testing: a genuine free tier issues an API key with no credit card, and the OpenAI-compatible endpoint means switching is roughly a two-line change.
• Your workload is batch-heavy — the Batch API and prompt caching each cut costs by 50%, which compounds on large summarization, classification, or enrichment jobs.

When to pick Together AI

• You need a model Groq doesn’t host — Together’s 200+ catalog spans DeepSeek, Mistral, Qwen, and heavyweights like Llama 3.1 405B, where Groq curates a shorter speed-optimized menu.
• Fine-tuning is on the roadmap: LoRA and full fine-tunes are self-serve and priced per training token, with the result deployable to a dedicated endpoint — on Groq this requires an Enterprise contract.
• You’ll eventually need raw compute — H100/H200/B200 clusters from about $2.99/hour let training, batch jobs, and inference live on one platform instead of a second vendor.

Bottom line

Groq and Together AI both serve open models through OpenAI-compatible APIs at similar per-token rates, so neither wins on price alone — they win on different axes. Groq is a specialist: its custom LPU hardware makes it the default for anything conversational or real-time, and its free tier plus batch discounts make it the cheapest sandbox. Together AI is a platform: the largest open-model catalog, self-serve fine-tuning, and GPU rental mean a team can prototype, customize, and scale without leaving. A common pattern is starting on Groq for speed and adding Together when fine-tuning or exotic models enter the picture — the shared API standard makes running both nearly free in engineering terms.