Groq vs Together AI (2026)
Updated March 9, 2026
Overview
Groq builds custom AI inference chips (Language Processing Units / LPUs) designed for extremely fast token generation. Groq's cloud platform offers the fastest inference speeds in the market, generating hundreds of tokens per second for models like Llama and Mixtral. The company's hardware architecture eliminates the memory bandwidth bottleneck that limits GPU-based inference, making it ideal for real-time and latency-sensitive AI applications.
Together AI provides a cloud platform for running, fine-tuning, and training open-source AI models. The platform hosts popular models like Llama, Mistral, and Stable Diffusion with optimized inference that delivers fast generation at competitive prices. Together AI also offers GPU clusters for custom training jobs and has contributed to several breakthrough open-source AI research projects.
Key features
Core capabilities each platform advertises.
- Custom LPU inference chips
- Ultra-low latency inference
- Fastest tokens-per-second performance
- OpenAI-compatible API
- Free tier for experimentation
- Inference and training cloud
- Open-source model hosting
- Serverless inference endpoints
- Fine-tuning as a service
- Competitive GPU pricing
Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- Exceptional inference speed with ultra-low latency using custom LPU hardware
- Highly competitive pricing—among the lowest per-token costs in the market
- Batch API and Prompt Caching offer significant cost savings (50% discounts)
- Linear, predictable pricing model with no hidden costs or idle infrastructure fees
Cons
- Limited model fine-tuning options compared to training-focused platforms
- Smaller model selection compared to comprehensive cloud providers
- Enterprise features like GroqRack require custom contracts and sales engagement
- Newer platform with less extensive documentation than established providers
Pros
- Competitive pricing starting at USD 0.10 per million tokens
- 50 percent discounts on batch inference and prompt caching
- Strong backing from NVIDIA and Salesforce Ventures
- Flexible deployment with both serverless and dedicated options
Cons
- Focused on open-source models may limit access to proprietary models
- Per-minute billing for dedicated GPUs can be complex to predict
- Newer platform with less extensive documentation than established providers
- Pricing varies significantly by model making cost planning difficult
Groq or Together AI — which should you choose?
Choose Groq if you wantChoose if you want
- Real-time AI applications needing lowest latency
- Interactive conversational AI
- High-throughput batch inference
- Cost-efficient inference for open-source models
- Latency-sensitive production deployments
Choose Together AI if you wantChoose if you want
- Hosting open-source LLMs in production
- Fine-tuning models on custom data
- Cost-efficient inference at scale
- Training custom models
- Rapid model prototyping
Compare the models
How Groq's and Together AI's models score on public benchmarks, and their live API status.
Compare Groq and Together AI on your own traffic
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