Groq is live · free plan; paid price not published · last verified 30 Oct 2026 · viability healthy
Groq
Groq delivers fast LLM inference on its own LPU and LPX silicon through an OpenAI-compatible API.
If latency is the constraint your product fights against, Groq is built around that problem rather than adapted to it. The OpenAI-compatible endpoint means you can measure the difference in an afternoon — swap the base URL, keep your SDK, compare time-to-first-token. Keep a second provider if your stack leans on proprietary frontier models.
Verdict generatedVerified 2h ago · liveness 96/100 · cite: rightaichoice.com/tools/groq
- Real-time AI agents, chatbots, and copilots where latency is a product requirement
- Voice AI pipelines running TTS and ASR inside one latency budget
- Developers on OpenAI-compatible APIs who want a low-effort switch to open-weight models
- Teams that want day-zero access to open-weight releases like Muse Spark and gpt-oss
- Teams that need a proprietary frontier model as their primary inference engine
- Pure overnight batch jobs where throughput per dollar beats tail latency
- Shops deeply standardized on CUDA tooling and GPU-centric deployment pipelines
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Skip Groq if your product's primary engine has to be a proprietary frontier model, or if you cannot commit budget until LPX and Vera Rubin capacity in your own region is confirmed.
Free-tier usage is capped and then converts to per-token on-demand billing, so a prototype that suddenly gets traffic starts costing money per call.
Groq's structure is simple by neocloud standards: a free tier to test the endpoint, then pay-per-token on-demand across hosted models, then custom enterprise capacity. That fits teams whose inference spend is variable and latency-critical. If you are comparing on price per token at low volume, Together AI and Fireworks publish per-token rates worth benchmarking side by side; if you are buying committed capacity at scale, the enterprise contract is the real comparison.
In short
Groq — Groq delivers fast LLM inference on its own LPU and LPX silicon through an OpenAI-compatible API. Best for Real-time AI agents, chatbots, and copilots where latency is a product requirement, Voice AI pipelines running TTS and ASR inside one latency budget, Developers on OpenAI-compatible APIs who want a low-effort switch to open-weight models. Free to start; it also has paid plans whose price we could not confirm — see the pricing table.
What is Groq?
Groq — Groq delivers fast LLM inference on its own LPU and LPX silicon through an OpenAI-compatible API. Best for Real-time AI agents, chatbots, and copilots where latency is a product requirement, Voice AI pipelines running TTS and ASR inside one latency budget, Developers on OpenAI-compatible APIs who want a low-effort switch to open-weight models. Free to start; it also has paid plans whose price we could not confirm — see the pricing table.
Groq sells inference speed as a product. Its GroqCloud platform runs open-weight language models on Groq's own LPU chip, with LPX now working alongside NVIDIA's next-generation GPUs — the pitch being that fast and affordable no longer trade off. The buyer is anyone whose product lives or dies on response time: agents, copilots, voice pipelines, code assistants. Access is through an OpenAI-compatible endpoint at api.groq.com/openai/v1, so moving an existing OpenAI SDK onto Groq is roughly a base-URL and model-name change. Beyond text generation the docs list speech-to-text, Orpheus text-to-speech, OCR and image recognition, reasoning, structured outputs, content moderation, prompt caching, and batch processing, plus built-in tools for code execution and browser search. The model catalog leans open-weight, with day-zero support as releases land — Meta's Muse Spark entered limited preview on GroqCloud on October 15, 2026, and OpenAI's gpt-oss-20b appears in the quickstart. 2026 was a capacity year: a $650M raise in June, a $350M Series A closed in August, and NVIDIA Cloud Partner status from August 12. Groq says it will be among the first to bring NVIDIA Groq 3 LPX and Vera Rubin NVL72 systems to market. Data centers span North America, Europe, the Middle East, and Asia-Pacific. Against GPU-based inference clouds, the distinction is architecture: Groq's pitch rests on custom silicon rather than rented GPUs. The tradeoff is that the catalog is open-weight first — teams needing a proprietary frontier model as their primary engine will run a second provider alongside it.
Key features
- Fast LLM inference on Groq's LPU silicon for latency-sensitive applications
- LPX inference architecture running alongside NVIDIA next-generation GPUs
- OpenAI-compatible API at api.groq.com/openai/v1 for drop-in SDK migration
- Responses API in addition to chat completions
- Orpheus text-to-speech endpoint for voice output
- Whisper-based speech-to-text transcription
- OCR and image recognition for multimodal inputs
- Reasoning model support and structured outputs
- Content moderation endpoints
- Prompt caching for repeated prompt prefixes
- Batch API for asynchronous bulk workloads
- Built-in code execution and browser search tools on GPT OSS models
- Remote tools and MCP connectors plus local tool calling
- Day-zero support for new open-weight releases, including Muse Spark limited preview (Oct 15, 2026)
- GroqCloud console with projects, model permissions, spend limits, and Prometheus metrics
Behind the verdict
We'd reach for Groq when time-to-first-token is the metric that moves your product numbers — a voice agent that feels laggy, a copilot users abandon mid-sentence, an autocomplete that needs to land before the human finishes typing. The architecture argument is easy to test: point your existing OpenAI SDK at api.groq.com/openai/v1, swap the model name, and benchmark your own prompts. That beats reading any benchmark chart. Pass when your workload is overnight batch where throughput per dollar, not tail latency, sets the bill. Pass too if a proprietary frontier model is your primary engine — the catalog here leans open-weight, with day-zero releases like Muse Spark and gpt-oss-20b arriving at launch, and that's a different bet than renting the newest closed model. The closest alternatives are GPU-based inference clouds like Together AI and Fireworks. Those give you a broader model menu and CUDA-familiar tooling; Groq gives you custom silicon tuned for latency. At low volume, do the math on raw open-weight throughput per dollar before assuming speed wins the spreadsheet. One caveat worth flagging: the 2026 momentum — the $650M raise, the $350M Series A, NVIDIA Cloud Partner status, the promised Groq 3 LPX and Vera Rubin NVL72 systems — is capacity news, not a contract. If your deployment depends on LPX or Vera Rubin capacity in a specific region, confirm availability and timelines before you commit. In practice the migration is cheap enough that the real decision is organizational: are you willing to run a second provider for the closed-model workloads you can't move? Teams that answer yes get a meaningful latency lever. Teams that want one provider for everything should look elsewhere.
Models under the hood
as of 2026-10-30
Limitations
- Groq is an inference cloud for fast execution of open-weight models, built on its LPU and LPX architectures working alongside NVIDIA GPUs.
- The live sources name specific models only in places — Llama (via the Meta Llama API collaboration), Whisper for speech-to-text, Orpheus for text-to-speech, GPT OSS models (for browser search and built-in tools), and Meta's Muse Spark in limited preview — so the full catalog is not enumerated here and support beyond those cannot be verified.
- The 2026 capacity announcements (NVIDIA Groq 3 LPX and Vera Rubin NVL72 systems, hundreds of megawatts under construction) come with no pricing or availability dates, so regional capacity should be confirmed before you commit.
- If your product depends on a proprietary frontier model as its primary engine, expect to run a second provider alongside Groq.
as of 2026-10-20
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Is Groq still active?
Groq is live · free plan; paid price not published · last verified 30 Oct 2026 · viability healthy.
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: October 2026
How we score →What's new in Groq
Checked 10 days agoAcross the latest 5 updates: 1 launch and 4 news mentions.
Meta's Muse Spark open-weight release is Now in Limited Preview on GroqCloud
Meta's Muse Spark open-weight model enters limited preview on GroqCloud, giving Groq customers access to the new model at launch via the same OpenAI-compatible endpoint.
Groq Among the First to Bring NVIDIA Groq 3 LPX and Vera Rubin NVL72 to Market
Groq says it will be among the first to offer NVIDIA Groq 3 LPX and Vera Rubin NVL72 hardware. No pricing or availability dates were given.
Groq Closes $350 million Series A, Building the World's Leading AI Inference Cloud
Groq closed a $350M Series A, with proceeds directed toward expanding its inference cloud capacity.
Groq Becomes an NVIDIA Cloud Partner
Groq joined NVIDIA's Cloud Partner program, formalizing its position as an NVIDIA-based inference provider.
Groq Raises $650M to Scale Its AI Inference Cloud Business
Groq raised $650M to expand its AI inference cloud business and datacenter capacity.
What do users say about Groq?
What people actually say about Groq — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
Researched93 mentions across 5 sources (Hacker News, YouTube, Product Hunt, Stack Overflow, Lemmy) · researched Aug 18, 2026.
Average across the 5 sources that answered — each source counts once, not each post.
- +Sub-200ms inference is consistently praised as the fastest in the industry.
- +Free API tier with no credit card is a major draw for developers.
- +OpenAI-compatible API allows migration in just two lines of code.
- +Day-zero support for new open-weight models like Llama 3.3 and Qwen.
- +Batch API cuts async costs by 50%, and prompt caching saves up to 50% more.
- −Model catalog limited to open-weight options; no GPT-4o or Claude.
- −Frequent 429 rate-limit errors in production, especially under load.
- −'Tool use failed' errors with function calling can break agents.
- −Token limits can cause 'Request too large' errors for long prompts.
- −Limited long-context performance compared to competitors like Gemini Flash.
- • Potential rate limit fees or throttling at high volumes.
- • Token-based pricing that can escalate with large context windows.
- • Batch API requires upfront investment but promises long-term savings.
How we verified this
Every listed fact about Groq— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 30 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified Groq 120 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed features, summary, description, pricing tiers, our verdict, who it suits, who should skip it, limitations, models, integrations, use cases, setup time, pricing fit, switching out, switching in, hidden costs
- — changed who it suits, features, description, our verdict, pricing tiers, summary, limitations
- — changed pricing page, limitations, models
- — changed features, who it suits, description, who should skip it, pricing tiers, summary, limitations, pricing page
- — changed our verdict, who should skip it, description, pricing tiers, summary, models, limitations
- — changed who should skip it, our verdict, summary, use cases, hidden costs, description, switching in, setup time, switching out, features, pricing fit, models, pricing tiers, limitations
Showing the 6 most recent of 120 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Groq cost in 2026?
Groq: Free to start; it also has paid plans whose price we could not confirm — see the pricing table. Pricing last verified 30 Oct 2026.
Plans compared
Every published Groq tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Free | Free | — | A developer evaluating whether Groq's latency actually helps their workload before committing budget.Adds: Starting tier: free access to the OpenAI-compatible endpoint and the open-weight model catalog, converting to per-token billing past free limits. |
| On-Demand | Per-token pricing by model | — | A shipping product with variable inference volume — a chatbot, voice agent, or IDE assistant in production.Adds: Adds pay-per-token billing across hosted models, prompt caching, the Batch API's asynchronous discount, and the speech, OCR, reasoning, structured-output, and moderation endpoints. |
| Enterprise | Custom | quote | A team buying reserved capacity at scale that needs contract terms and multi-region deployment.Adds: Adds custom capacity and contract terms, access to LPX and NVIDIA Groq 3 LPX / Vera Rubin NVL72 deployments as they land, and multi-region deployment. |
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.
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 structure is simple by neocloud standards: a free tier to test the endpoint, then pay-per-token on-demand across hosted models, then custom enterprise capacity. That fits teams whose inference spend is variable and latency-critical. If you are comparing on price per token at low volume, Together AI and Fireworks publish per-token rates worth benchmarking side by side; if you are buying committed capacity at scale, the enterprise contract is the real comparison.
Has Groq's pricing changed?
No plan price for Groq has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Groq?
Pick Groq if you are Real-time AI agents, chatbots, and copilots where latency is a product requirement, Voice AI pipelines running TTS and ASR inside one latency budget, Developers on OpenAI-compatible APIs who want a low-effort switch to open-weight models, Teams that want day-zero access to open-weight releases like Muse Spark and gpt-oss.
Look elsewhere if you are Teams that need a proprietary frontier model as their primary inference engine, Pure overnight batch jobs where throughput per dollar beats tail latency, Shops deeply standardized on CUDA tooling and GPU-centric deployment pipelines, Buyers who need verified LPX or Vera Rubin capacity in their own region before committing.
Skip it if Skip Groq if your product's primary engine has to be a proprietary frontier model, or if you cannot commit budget until LPX and Vera Rubin capacity in your own region is confirmed.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Groq actually fits — and what changes day-one when you adopt it.
Swap the base URL in an existing OpenAI SDK for api.groq.com/openai/v1, point it at an open-weight model, and run the existing eval suite against both providers to measure time-to-first-token.
Outcome: A measured latency difference on real prompts within a day, with the OpenAI-compatible endpoint meaning no rewrite of application code.
Chain Whisper speech-to-text and Orpheus text-to-speech in one call path so the user hears a reply inside a single latency budget, with structured outputs constraining the tool call the agent makes.
Outcome: A conversational loop where the perceived delay comes from the model, not from stitching two vendors together.
Set spend limits, per-project model permissions, and Prometheus metric scraping in the GroqCloud console, then move overnight summarization jobs to the Batch API while keeping interactive calls on the performance tier.
Outcome: Cost control and observability on day one, with asynchronous work billed at roughly half the interactive rate.
Use cases
- Build a real-time customer-support chatbot where time-to-first-token decides retention.
- Add code completion to an IDE through the OpenAI-compatible endpoint with Cline, Roo Code, or Kilo Code.
- Pair Orpheus text-to-speech with Whisper speech-to-text in a single voice-agent latency budget.
- Run a multi-step research agent that calls code execution and browser search as built-in tools.
- Cut token spend on repeated context with prompt caching and move overnight jobs to the Batch API.
- Transcribe audio and extract text from images in one pipeline using speech-to-text plus OCR.
- Enforce JSON output shape for downstream systems with structured outputs.
- Add a moderation endpoint in front of user-generated content without standing up a second vendor.
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.
A developer can get a key from the GroqCloud console and make a first call in minutes — the docs quickstart is an OpenAI SDK pointed at api.groq.com/openai/v1. Measuring the latency difference against your current provider is an afternoon. Production readiness (spend limits, model permissions, Prometheus metrics, tier selection) is a day or two depending on your stack. Enterprise capacity
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 the base URL to api.groq.com/openai/v1 and swap the model string — the SDK and call shape stay the same.
- →From another OpenAI-compatible provider: point the client at Groq's base URL and re-run your evals on the models Groq hosts.
- →From a self-hosted open-weight deployment: move to the hosted endpoint and keep the same model family, dropping the GPU operations burden.
- ↗To another OpenAI-compatible provider: swap the base URL back — Groq's endpoint uses the same call shape as OpenAI, so the reverse path is symmetric.
- ↗To a proprietary frontier model: add the provider as a second engine rather than replacing Groq, since the endpoint is already OpenAI-shaped.
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
YouTube returned 6 videos for “Groq”, and we withheld 5: 5 could not be judged, because “Groq” is a single word that other videos use for other things. Showing the 1 we can prove is about Groq.
Official links
What are the best Groq alternatives?
The closest alternatives to Groq we list are DeepInfra, Vllm and Inference Engine by GMI Cloud. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| DeepInfra | Paid | Pricing not published | — |
| Vllm | Free | Free | — |
| Inference Engine by GMI Cloud | Paid | Pricing not published | — |
Alternatives to Groq
View all
DeepInfra
DeepInfra runs serverless AI inference on 100+ open models through one OpenAI-compatible API key.
Vllm
vLLM is the open-source LLM inference engine for high-throughput, OpenAI-compatible serving on your own GPUs.
Inference Engine by GMI Cloud
GMI Cloud Inference Engine serves frontier multimodal models through one OpenAI-compatible API on dedicated NVIDIA GPUs.
Tools that pair well with Groq
Common stack mates teams adopt alongside Groq, with the specific reason each pairing earns its keep.
DeepInfra
DeepInfra runs serverless AI inference on 100+ open models through one OpenAI-compatible API key.
Vllm
vLLM is the open-source LLM inference engine for high-throughput, OpenAI-compatible serving on your own GPUs.
Inference Engine by GMI Cloud
GMI Cloud Inference Engine serves frontier multimodal models through one OpenAI-compatible API on dedicated NVIDIA GPUs.
Groq vs its closest rival
Groq's closest head-to-head is with Hugging Face — If you're building real-time AI applications where sub-200ms latency is non-negotiable, Groq is your engine—especially with compound AI systems and day-zero support for new open-weight models. But if you live in the ML ecosystem—discovering models, sharing research, training custom models—Hugging Face is the undisputed hub. For most teams, they're complementary: use Hugging Face to find and fine-tune, then deploy on Groq for speed. Verdict generated
Head-to-head comparisons
Groq vs Hugging Face
If you're building real-time AI applications where sub-200ms latency is non-negotiable, Groq is your engine—especially with compound AI systems and day-zero support for new open-weight models. But if you live in the ML ecosystem—discovering models, sharing research, training custom models—Hugging Face is the undisputed hub. For most teams, they're complementary: use Hugging Face to find and fine-tune, then deploy on Groq for speed.
GeneratedGemini vs Groq
If you live in Google's ecosystem and need a daily assistant that drafts, researches, and automates across Gmail, Docs, and Maps, Gemini is your copilot. If you're a developer building real-time agents, voice AI, or compound systems where sub-200ms latency and predictable costs matter, Groq's LPU and OpenAI-compatible API are the clear winners. Choose based on your primary need: productivity in Google Workspace vs. high-speed inference for custom applications.
GeneratedGroq vs Together Ai
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.
GeneratedChatgpt vs Groq
If you want a single tool for everything—chat, image gen, browsing, code, voice—ChatGPT is the obvious choice, especially with the free Luna tier. But if you're a developer building AI features that need sub-200ms responses, Groq's speed and API-first design win hands down. Pick your priority: all-in-one convenience vs. raw performance.
GeneratedCerebras vs Groq
If you need raw token throughput for heavy agentic workloads and want the ability to train as well as infer on the same platform, Cerebras is your pick. If you prioritize sub-200ms latency, flexibility with open-source models, and a rich ecosystem of agentic tools, go with Groq. Both are fast, but they target different pain points.
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