Groq
Sub-200ms LPU inference for real-time AI apps.
Groq delivers the lowest-latency inference we've seen for open-weight models, at prices that undercut many GPU providers. The trade-off is a narrower model catalog—no GPT-4o or Claude—so it's not a universal replacement. If your app lives or dies on response time, Groq is worth a serious look.
Verified 9h ago · liveness 97/100 · cite: rightaichoice.com/tools/groq
- Real-time AI agents, chatbots, and copilots where sub-200ms latency is critical
- Voice AI applications using Orpheus TTS for instant speech generation
- Developers building with OpenAI-compatible APIs who want to switch quickly
- Enterprises deploying at scale with predictable, linear pricing
- Teams that require proprietary models like GPT-4o or Claude
- Use cases needing fine-tuned or niche models (not offered as standard)
- Heavy batch inference where raw throughput beats latency
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Skip Groq if you need proprietary models like GPT-4o or Claude, or if you prioritize raw throughput for massive batch workloads over per-request latency.
Exceeding free tier rate limits forces you to On-Demand pricing, which may catch early-stage projects off guard.
Groq's per-token pricing is competitive with GPU-based providers like Together AI and Fireworks, especially for smaller models like Llama 3.1 8B ($0.05/M input). For startups with latency-sensitive workloads, the predictable linear pricing avoids the surprise bills common with elastic GPU rentals. Enterprises with dedicated capacity get custom pricing.
In short
Groq — Sub-200ms LPU inference for real-time AI apps. Best for Real-time AI agents, chatbots, and copilots where sub-200ms latency is critical, Voice AI applications using Orpheus TTS for instant speech generation, Developers building with OpenAI-compatible APIs who want to switch quickly. Free to use.
What's new in Groq
Checked yesterdayAcross the latest 5 updates: 5 feature updates.
Remote Model Context Protocol (MCP) added
Remote MCP server integration is now available in Beta on GroqCloud, connecting AI models to thousands of external tools through Anthropic's open MCP standard. Developers can connect any remote MCP server to models hosted on GroqCloud with zero code changes from OpenAI.
Moonshot AI Kimi K2 Instruct 0905 added
Kimi K2-0905 brings Moonshot AI's model to GroqCloud with day zero support, featuring a 256K context window, prompt caching, and enhanced agentic coding capabilities. Priced at $1.00/M input tokens and $3.00/M output tokens.
Groq Compound and Compound Mini added
Compound and Compound Mini are production-ready agentic AI systems integrating web search, code execution, and browser automation into a single API call. Now GA, they outperform competing systems on SimpleQA and RealtimeEval benchmarks.
Canopy Labs' Orpheus TTS is live on GroqCloud
Orpheus text-to-speech model added to GroqCloud, enabling real-time speech generation at 100+ characters per second. Priced at $22.00 per million characters for English.
GPT-OSS Improvements: Prompt Caching & Lower Pricing
GPT-OSS models on GroqCloud now support prompt caching for up to 50% cost savings on cached tokens, with lower pricing for both input and output tokens.
Viability Score
How well maintained and how widely used is Groq? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: August 2026
How we score →Key Features
- Custom LPU architecture for sub-200ms inference latency
- OpenAI-compatible API, switch in two lines of code
- GroqCloud console for managing inference globally
- Day-zero support for new open-source models like GPT-OSS and Kimi K2
- Compound AI systems: web search, code execution, browser automation via one API call
- Remote MCP server integration (beta) for external tool connectivity
- Orpheus TTS for real-time text-to-speech at 100+ chars/s
- Whisper ASR models for automatic speech recognition
- Batch API with 50% lower cost for async workloads
- Prompt caching for up to 50% savings on cached tokens
- Linear and predictable pricing with no idle infrastructure costs
- Global data center deployment for low-latency responses
- Multi-language SDKs: Python and JavaScript
- Real-time streaming API support
- Built-in tools: web_search, code_interpreter, browser automation
About Groq
Groq is an inference-as-a-service platform built on its custom Language Processing Unit (LPU), custom silicon designed from the ground up in 2016 for AI inference rather than training. The result is extremely low-latency responses — often under 200 milliseconds — at prices that are linear and predictable, with no surprise bills. It's aimed at developers and enterprises building real-time applications like chatbots, copilots, voice assistants, and agentic systems that can't afford lag or cost spikes. With an OpenAI-compatible API that you can switch to in just two lines of code, the barrier to entry is low if you're already using OpenAI's SDK. Over 3 million developers and teams use Groq, including the McLaren F1 Team and the PGA of America, who rely on it for performance-critical workloads. The platform, GroqCloud, is the console where you manage inference across globally distributed data centers, keeping responses local to your users. It supports a growing list of open-weight models, including OpenAI's GPT-OSS, Llama 3.3, Qwen, and Moonshot AI's Kimi K2, often with day-zero availability when new open models launch. Beyond raw text inference, Groq offers Compound AI systems that bundle web search, code execution, and browser automation into a single API call—now production-ready. It also includes Orpheus text-to-speech for real-time speech generation, Whisper-based ASR models for transcription, and a Batch API that cuts costs by 50% for asynchronous workloads. Recent additions extend the platform further. Remote MCP (Model Context Protocol) integration is in beta, letting you connect Groq-hosted models to thousands of external tools via Anthropic's open standard. Prompt caching reduces costs by up to 50% on cached input tokens, and pricing on popular models has been dropping. Groq also announced a $750 million funding round in September 2025, signaling strong demand. Groq's edge over GPU-based providers is speed per dollar for latency-sensitive inference.
Behind the Verdict
Let's be direct: if your AI app stalls waiting for a response, Groq is the fix. Its LPU architecture is built for inference speed, and the benchmarks show it — sub-200ms responses are real, and the per-token costs are often a fraction of what you'd pay for GPU-based inference. If you're running a chatbot, copilot, or voice assistant where latency kills the experience, Groq's performance per dollar is tough to beat. But it's not for everyone. The model selection is the first thing you'll notice — it's open-weight only. You won't find GPT-4o, Claude, or other proprietary heavyweights. If your product depends on those specific models, Groq is a no-go. The platform also doesn't support fine-tuned models as a standard offering, though you can contact sales for custom requests. For developers who need niche or brand-new models beyond what Groq supports, you'll be waiting or looking elsewhere. Compared to GPU-based inference from providers like Together AI or Fireworks, Groq wins on raw speed, especially for small-to-medium open models. Where it can lag is throughput for massive batch jobs — the Batch API helps, but it's designed for async, not real-time peaks. If you're churning through terabytes of offline data, a GPU farm might still be the better fit. What impresses us most is the ecosystem that's grown around the core API. Remote MCP in beta is a big deal — it lets you plug Groq models into thousands of tools without a ton of glue code. Compound AI systems that bundle search, code execution, and browsing into one call are a practical time-saver for building agents. And the prompt caching is a no-extra-fee discount on cache hits, which can slash costs on repetitive workloads. Watch out for a few things. The token per second speeds are impressive, but they're
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Real-world workflow fit
Concrete scenarios for the personas Groq actually fits — and what changes day-one when you adopt it.
You sign up for a free GroqCloud account, copy the OpenAI-compatible API snippet into your Node.js app, and within minutes your chatbot responds to user queries in under 200ms using Llama 3.1 8B.
Outcome: Production-ready latency at zero upfront cost; you can scale to the On-Demand tier when traffic grows.
You switch your app's base URL from api.openai.com to api.groq.com/openai/v1 with zero code changes, cut API costs by 50-80% on comparable models, and maintain sub-200ms response times.
Outcome: Predictable pricing and lower costs; your premium plan becomes more affordable for end users.
You use Groq's Whisper ASR for speech-to-text, route to Llama 3.3 70B for reasoning, and output with Orpheus TTS—all through a single API integration with latencies under 500ms end-to-end.
Outcome: A real-time voice experience that delights users without the complexity of multi-provider orchestration.
Use Cases
- Build a real-time chatbot with sub-100ms latency for customer support.
- Implement code completion in an IDE using Llama models.
- Transcribe audio to text at high speed with Whisper models.
- Add text-to-speech to apps with Orpheus TTS.
- Power a multi-lingual assistant for global users.
- Create a cost-efficient content generation pipeline for marketing.
- Develop an AI research agent that uses web search and code execution.
- Automate browser interactions with Compound AI systems.
Models Under the Hood
as of 2026-07-31
Limitations
- Models are limited to open-source options; no proprietary models.
- Free tier has rate limits not explicitly detailed.
- Some features like Remote MCP are in beta.
- Pricing is per-token with varying speeds and costs per model.
as of 2026-07-30
Verification history
We have re-verified Groq 45 times since . Each pass re-reads the vendor's own pages and updates only what actually changed.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
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- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
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Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Groq tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
Developers prototyping real-time AI apps or evaluating Groq's latency and model quality at no cost.
What this tier adds
Free tier provides rate-limited access to all available models (up to 30 requests per minute, varies by model) with community support.
On-Demand
Per-token pricing by model
Ideal for
Production apps with variable workloads that need predictable per-token pricing and access to features like prompt caching and Batch API.
What this tier adds
Pay-as-you-go per-token pricing with no monthly commitment; includes prompt caching discounts and 50% lower Batch API costs.
Enterprise
Custom
Ideal for
Large organizations needing dedicated capacity, custom model deployment, on-premises options, and SLA-backed reliability.
What this tier adds
Custom pricing with dedicated capacity, custom fine-tuning, on-prem deployment, priority support, and SLAs.
Where the pricing makes sense
The company stage and team size where Groq's pricing actually pencils out — and where peers do it cheaper.
Groq's per-token pricing is competitive with GPU-based providers like Together AI and Fireworks, especially for smaller models like Llama 3.1 8B ($0.05/M input). For startups with latency-sensitive workloads, the predictable linear pricing avoids the surprise bills common with elastic GPU rentals. Enterprises with dedicated capacity get custom pricing.
Setup time & first value
How long it actually takes to get something useful out of Groq — broken out by persona, not the marketing-page minute.
For solo developers: 5 minutes to get an API key and make your first request using the Python/JS snippet. For teams migrating from OpenAI: under 1 hour to update the base URL and test model compatibility. Enterprise deployment with dedicated capacity: days to weeks depending on custom model fine-tuning and on-prem requirements.
Switching to or from Groq
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From OpenAI: change base URL to api.groq.com/openai/v1 and update your API key—no code changes needed for most apps.
- →From Together AI or Fireworks AI: similar migration path; update endpoint and key, adjust model names to Groq's supported list.
- ↗To OpenAI: revert base URL and API key; models may differ, so test for compatibility.
- ↗To self-hosted inference (vLLM, TGI): download open-source weights and deploy on your own GPU infrastructure.
Integrations
Resources & Guides
- Documentationgroq.com
Overview
Fast LLM inference, OpenAI-compatible. Simple to integrate, easy to scale. Start building in minutes.
- Resourcegroq.com
Changelog
The Groq LPU delivers inference with the speed and cost developers need.
- Resourcegroq.com
Blog
The Groq LPU delivers inference with the speed and cost developers need.
- Resourcegroq.com
Groq On-demand Pricing for Tokens-as-a-Service
Groq powers leading openly-available AI models. View the pricing of our core models including GPT-OSS, Kimi K2, Qwen3 32B, and more.
Tutorials & Learning
Official links
Tools that pair well with Groq
Common stack mates teams adopt alongside Groq, with the specific reason each pairing earns its keep.
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