Groq vs Together AI

Side-by-side comparison of features, pricing, and ratings

Analysis reviewed Live tool data as of 2026-08-15
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At a glance

DimensionGroqTogether AI
PricingFreemium, linear pricing, batch 50% cheaperFreemium, per-token serverless, dedicated plans
Core focusUltra-low-latency LPU inference for real-time appsFull-stack AI cloud: inference, fine-tuning, pre-training
ModelsOpen-source incl. GPT-OSS, Kimi K2, day-zero access100+ open-source, incl. DeepSeek V4 Pro, Llama 4 Maverick, Qwen3.7-Max
Key differentiatorSub-200ms latency, Compound AI systems, Orpheus TTSBatch inference up to 30B tokens, fine-tuning with FlashAttention-4
IntegrationsOpenAI SDK, MCP, BrowserBase, Stripe, TavilyCodeSandbox, Hugging Face, LangChain, Weights & Biases
Best forReal-time agents, voice AI, low-latency appsProduction coding agents, batch processing, fine-tuning

If you need real-time responsiveness under 200ms — chatbots, voice assistants, agentic systems — Groq's LPU is the clear winner, with day-zero model access and a dead-simple switch from OpenAI. But if your workloads are batch-heavy, require fine-tuning, or need massive async token throughput (up to 30B tokens), Together AI's full-stack cloud — from sandbox to AI Factory — offers more flexibility and training depth. Choose Groq for speed, Together AI for scale and customization.

Groq
Groq

Sub-200ms LPU inference for real-time AI apps and agents

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Together AI
Together AI

AI-native cloud for running open-source LLMs at scale—serverless inference, fine-tuning, and GPU clusters.

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Pricing
Freemium
Freemium
Plans
$0/mo
Per-token pricing by model
Custom
Per 1M tokens (variable by model)
Batch API price (per 1M tokens)
Contact sales
Contact sales
Contact sales
Contact sales
Contact sales
Popularity
5.9k views
3.6k views
Skill Level
Intermediate
Intermediate
API Available
Platforms
WebAPI
WebAPI
Categories
🖥️ GPU Cloud & Model Inference
🖥️ GPU Cloud & Model Inference
Features
Sub-200ms LPU inference
OpenAI-compatible API
GroqCloud management console
Day-zero support for open-weight models
Compound AI systems (web search, code execution, browser automation)
Orpheus TTS at 100+ chars/sec
Whisper ASR for speech-to-text
Batch API with 50% cost reduction
Prompt caching (up to 50% savings)
Real-time streaming
Python and JavaScript SDKs
OCR and image recognition
Content moderation
Global data centers including Sydney
Serverless inference for 100+ open-source models
Batch inference up to 30 billion tokens per model
Provisioned Throughput with 99% uptime SLA
Dedicated Model Inference on custom GPU hardware
Dedicated Container Inference for video/audio/image models
GPU Clusters: GB300, GB200, B200, H200, H100
AI Factory custom infrastructure
Fine-tuning with FlashAttention and ATLAS kernels
Custom training from first experiment to production
Managed Storage with zero egress fees
Sandbox development environments via CodeSandbox SDK
Voice Agents for production voice applications
Model evaluations for quality measurement
REST API, Python SDK, Node.js SDK, WebSocket
ISO 27001:2022 certified
Integrations
CodeSandbox
Hugging Face
Weights & Biases
LangChain
LlamaIndex
Python SDK
Node.js SDK
REST API
WebSocket
Jupyter Notebooks

Feature-by-feature

The core difference is architectural: Groq's custom LPU is engineered for sub-200ms latency, making it ideal for real-time AI agents, chatbots, and voice applications. Its recent additions enhance this: Compound AI systems (web search, code execution, browser automation) via a single API call, Orpheus TTS at 100+ chars/s, and remote MCP server integration for external tool connectivity. Together AI, by contrast, is a full-stack AI cloud—it offers serverless inference for 100+ models, batch inference scaling to 30 billion tokens per model, and dedicated GPU clusters (B200, H200, GB300) for heavy workloads. It also supports fine-tuning with advanced kernels (FlashAttention-4, ATLAS) and pre-training via the Together Kernel Collection, plus managed storage with zero egress fees and sandbox environments via CodeSandbox SDK. While Groq provides OpenAI-compatible API for easy switching and prompt caching to cut costs, Together AI's model library and playground (Together Chat) are more extensive for experimentation and comparison. Groq's day-zero support for new models like GPT-OSS and Kimi K2 is a differentiator, but Together AI emphasizes production-grade custom infrastructure and research-driven optimizations.

Pricing compared

Both are freemium, but the cost structures diverge sharply. Together AI uses per-token pricing for serverless inference, with dedicated plans requiring commitment — ideal for predictable but not sporadic usage. Batch inference scales to 30B tokens but careful with token costs. Groq offers linear and predictable pricing with no idle infrastructure costs, and its Batch API cuts cost by 50% for async workloads. Prompt caching on GPT-OSS models yields up to 50% savings on cached tokens. For real-time apps, Groq's sub-200ms latency is bundled with competitive per-token rates, but if you're doing massive batch processing, Together AI's batch inference might be more cost-effective per token, depending on volume. Groq's pricing is transparent for startups; Together AI's dedicated GPU clusters (B200, etc.) have no published prices, so enterprises need to contact sales. Overall, Groq is cheaper for bursty, low-latency needs; Together AI may be cheaper for high-volume batch and training.

Who should pick which

  • Solo founder building a real-time chatbot
    Pick: Groq

    Groq's sub-200ms latency and easy switch from OpenAI SDK make it ideal for quick, responsive MVPs without infrastructure complexity.

  • Enterprise running batch inference on massive datasets
    Pick: Together AI

    Together AI's batch inference scales to 30B tokens per model, handling async heavy loads efficiently with per-token pricing.

  • ML researcher fine-tuning open-source models
    Pick: Together AI

    Together AI offers fine-tuning with FlashAttention-4 and ATLAS kernels, plus pre-training support—Groq only provides inference.

  • Voice AI developer needing instant TTS
    Pick: Groq

    Groq's Orpheus TTS delivers 100+ chars/s for real-time speech, paired with low-latency inference.

  • Startup scaling from prototype to production with custom infrastructure
    Pick: Together AI

    Together AI's sandbox via CodeSandbox and AI Factory custom infrastructure let you grow without migration, unlike Groq's fixed LPU setup.

Frequently Asked Questions

Groq vs Together AI: which should you choose?

If you need real-time responsiveness under 200ms — chatbots, voice assistants, agentic systems — Groq's LPU is the clear winner, with day-zero model access and a dead-simple switch from OpenAI. But if your workloads are batch-heavy, require fine-tuning, or need massive async token throughput (up to 30B tokens), Together AI's full-stack cloud — from sandbox to AI Factory — offers more flexibility and training depth. Choose Groq for speed, Together AI for scale and customization.

Can I use Groq for fine-tuning models?

No, Groq's features listed are inference-focused only; fine-tuning isn't mentioned in its offering. For fine-tuning, Together AI is the go-to.

Which platform supports the latest open-source models first?

Groq claims day-zero support for new models like GPT-OSS and Kimi K2, as shown in its news. Together AI also hosts many models but doesn't stress day-zero in its description.

Does Together AI offer a low-latency option?

Together AI doesn't claim sub-200ms latency like Groq; it emphasizes high TPS and throughput. For real-time critical apps, Groq is the safer bet.

Is there a free tier on both?

Yes, both are freemium, but the free tier limits are not detailed here. Expect trial credits or limited usage.

Which is easier for a developer familiar with OpenAI's API?

Groq's OpenAI-compatible API can be switched in two lines of code, making it the quickest transition. Together AI also integrates with OpenAI SDK but may require more setup.

Can Groq handle heavy batch processing?

Groq offers a Batch API with 50% lower cost, but its focus is on latency, not massive throughput like Together AI's 30B token scaling. For extreme batch volumes, Together AI is stronger.

What about voice and multi-modal support?

Groq has Orpheus TTS and Whisper ASR. Together AI mentions container inference for video, audio, and image models, but no specific TTS/ASR products are listed.

Which platform is better for agentic workflows?

Groq's Compound AI systems integrate web search, code execution, and browser automation, plus MCP server support, making it more agent-ready. Together AI lacks such bundled agent tools.

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Last reviewed: August 3, 2026