Groq vs Together AI
Groq and Together AI both serve open-source LLMs at lower cost than OpenAI/Anthropic. The choice is between Groq's specialised speed (LPU hardware) and Together's broader model catalog and feature set.
Side-by-side
| Groq | Together AI | |
|---|---|---|
| Category | Agent infrastructure | Agent infrastructure |
| Free tier | Yes | Trial only |
| Entry price | $0/mo | Usage-based |
| Setup | Light config | Light config |
| Public API | Yes | Yes |
| MCP server | No | No |
| Zapier | No | No |
| SOC 2 | Unknown | Unknown |
| GDPR | Unknown | Unknown |
| Founded | 2016 | 2022 |
Pick Groq if
- Latency is the constraint: Groq's LPUs deliver 500-1,000 tokens/sec on most models
- You're building real-time experiences (voice, autocomplete, streaming chat)
- You want the cheapest tokens for the popular open-source models: Llama 3.1 8B at $0.05/M input is hard to beat
Pick Together AI if
- You need a wider model catalog (Llama, Mistral, Qwen, DeepSeek, FLUX, audio models, video gen)
- You'll fine-tune as well as serve: Together's fine-tuning starts at $0.48/M tokens
- You need image, audio, or video generation alongside text
The verdict
These are complementary more than competitive; most builders running open-source models at scale end up with both. Groq is the speed specialist: their LPU hardware delivers token throughput that GPU-based competitors can't match (Llama 3.1 8B at 840 tokens/sec is 5-10× standard GPU inference). For voice agents, real-time autocomplete, or any user-facing latency-sensitive feature, Groq is the right call. Together AI is the breadth specialist: their model catalog is wider, their dedicated GPU options handle workloads Groq doesn't, and they're the better choice for fine-tuning and multi-modal (image/audio/video) generation. Pricing is competitive at the popular-model end (Llama 3.3 70B is ~$0.79/M output on Groq vs ~$0.88/M on Together). Both expose OpenAI-compatible APIs so swapping is mechanical. The honest framework: if your bottleneck is latency, start with Groq. If your bottleneck is model variety or fine-tuning capability, start with Together. If you can't tell yet, build with Groq and migrate to Together when you hit a feature it can't serve.
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