Groq vs Together AI
Side-by-side comparison of features, pricing, and ratings
At a glance
| Dimension | Groq | Together AI |
|---|---|---|
| Best for | Developers needing ultra-low-latency inference (<100ms) for real-time applications like chatbots, code completion, and speech processing | Teams that want fine-tuning, batch inference, and a broad model library (100+ models) for production deployment and customization |
| Pricing | Free tier with rate-limited access; pay-as-you-go usage-based pricing for higher limits and all models | Free tier with $5 free credits; pay-as-you-go for serverless, fine-tuning, and dedicated instances |
| Setup complexity | Swap OpenAI-compatible endpoint URL and API key; minimal code changes required | Similar OpenAI-compatible API; additional steps for fine-tuning and dedicated instances |
| Strongest differentiator | Custom LPU hardware delivering up to 1,000 TPS and sub-100ms latency, ideal for latency-sensitive apps | Full-stack platform with fine-tuning, batch inference, and GPU clusters for production-scale open-source model deployment |
Groq vs Together AI: Groq wins for ultra-low-latency, real-time inference (e.g., chatbots, speech processing) thanks to its custom LPU hardware, delivering up to 1,000 tokens per second with sub-100ms latency. Together AI wins for teams needing flexibility—fine-tuning, batch inference, and a curated library of 100+ open-source models. Decide based on whether raw speed or model customization matters more for your use case.
Feature-by-feature
Core Capabilities: Groq vs Together AI
Groq specializes in ultra-fast inference via its custom Language Processing Unit (LPU), achieving up to 1,000 tokens per second and sub-100ms latency. It supports popular open-source models like Llama and Qwen, plus Whisper and Orpheus for speech. Together AI offers a broader set of capabilities: serverless inference on 100+ models, fine-tuning, batch inference, and dedicated GPU clusters (B200, H200, H100, etc.). While Groq focuses on speed for real-time use, Together AI provides more control over training and deployment. Groq wins for latency-sensitive apps; Together AI wins for teams needing fine-tuning and model variety.
AI/Model Approach: Groq vs Together AI
Groq runs only open-source models (Llama, Qwen, GPT-Oss, Kimi, etc.) optimized for its LPU hardware, with no fine-tuning support. Together AI also runs open-source models but adds a fine-tuning platform with longer context support, evaluations, and FlashAttention-4 for up to 4x faster inference. Groq includes speech models (Whisper, Orpheus) out of the box, while Together AI focuses on LLMs with voice agent building tools. Together AI wins for customization; Groq wins for speech and real-time inference.
Integrations & Ecosystem
Both platforms offer OpenAI-compatible APIs and integrate with LangChain, LlamaIndex, and Vercel AI SDK. Groq also works with the OpenAI SDK directly, making migration trivial. Together AI integrates with Hugging Face and provides a developer sandbox. Groq's built-in web search tools (Basic and Advanced) extend its utility for RAG applications. Tie on integrations; Groq has a slight edge with built-in web search.
Performance & Scale
Groq's LPU delivers unparalleled single-token latency (as low as 0.3ms) and up to 1,000 TPS, with claims of 7.41x speed improvements and 89% cost reduction versus GPUs. Together AI uses GPU clusters (B200, H200, H100) optimized with FlashAttention, achieving up to 4x faster inference than standard GPUs. For high-throughput batch processing, Together AI's batch inference is suited for millions of tokens. Groq wins for real-time latency; Together AI wins for batch throughput and fine-tuning at scale.
Developer Experience
Both provide OpenAI-compatible APIs, JSON mode, and function calling. Groq's free tier is rate-limited but generous; Together AI offers $5 free credits. Groq's prompt caching has no extra fee, reducing costs for repeated prompts. Together AI's managed storage and evaluations are valuable for ML teams. Switching from one to the other is straightforward due to API compatibility. Groq wins for simplicity and cost-effective caching; Together AI wins for MLOps features.
Pricing compared
Groq pricing (2026)
Groq offers a Free tier with rate-limited access to popular models, suitable for prototyping. The Pay-as-you-go plan is usage-based, with higher rate limits and access to all models. No hidden costs are documented; prompt caching is included with no extra fee. Enterprise deployments require contacting sales for dedicated pricing.
Together AI pricing (2026)
Together AI provides a Free tier that includes $5 free credits to get started. The Pay-as-you-go plan charges based on usage across serverless inference, batch inference, and fine-tuning. Dedicated instances and GPU clusters (B200, H200, H100) have custom pricing. There are no listed overage fees, but fine-tuning costs vary by model and duration.
Value-per-dollar: Groq vs Together AI
For real-time applications with low latency requirements, Groq's free tier and pay-as-you-go pricing deliver exceptional value due to its hardware-accelerated inference and free prompt caching. Together AI is more cost-effective for batch inference and fine-tuning workloads where raw speed is less critical. Groq wins for cost-sensitive real-time apps; Together AI wins for high-volume batch processing and model customization.
Who should pick which
- Solo developer building a real-time chatbotPick: Groq
Groq's sub-100ms latency and free tier make it ideal for prototyping and deploying a responsive chatbot with minimal cost.
- ML team fine-tuning Llama on custom dataPick: Together AI
Together AI offers a fine-tuning platform with longer context support and managed storage, essential for customization.
- Startup needing cost-efficient high-speed inferencePick: Groq
Groq's LPU delivers up to 7.41x speed improvements and 89% cost reduction compared to GPUs, benefiting budget-conscious startups.
- Enterprise with high-throughput batch processingPick: Together AI
Together AI's batch inference and dedicated GPU clusters handle millions of tokens efficiently for data processing workloads.
- Developer adding text-to-speech to an appPick: Groq
Groq includes built-in TTS models (Orpheus) and low-latency speech recognition, reducing integration complexity.
Frequently Asked Questions
Does Groq or Together AI offer a free tier?
Both offer free tiers. Groq provides rate-limited access to popular models at no cost. Together AI gives $5 free credits upon signup.
Can I fine-tune a model on Groq or Together AI?
Together AI supports fine-tuning on its platform. Groq does not offer fine-tuning; it focuses on inference of pre-trained open-source models.
How easy is it to migrate from OpenAI to Groq or Together AI?
Both use OpenAI-compatible APIs, so migration typically requires only a change of endpoint URL and API key. Groq's API is designed to be a drop-in replacement.
Which platform has more models?
Together AI offers over 100 open-source models including Llama, Mistral, DeepSeek, and Qwen. Groq supports a smaller selection (Llama, Qwen, GPT-Oss, Kimi, etc.) but adds speech models.
Can I use Groq for batch processing of millions of tokens?
Groq is optimized for real-time, low-latency inference. For high-volume batch processing, Together AI's batch inference API is more appropriate.
Does Groq or Together AI support custom models?
Together AI allows you to deploy custom models via dedicated container inference. Groq only runs supported open-source models on its LPU hardware.
Which platform is better for voice applications?
Groq includes built-in Whisper (ASR) and Orpheus (TTS) models with low latency, making it stronger for voice apps. Together AI has voice agent building tools but no dedicated speech models.
What are the latency differences between Groq and Together AI?
Groq's LPU can achieve sub-100ms token generation with up to 1,000 TPS. Together AI with FlashAttention-4 delivers up to 4x faster inference than standard GPUs, but not as low latency as Groq's custom hardware.
Last reviewed: May 12, 2026