Data Panda lands your business data in one warehouse and ships it to GroqCloud for inference. Llama, Mistral, GPT-OSS, Qwen and Whisper run on Groq's custom LPU chip at hundreds of tokens per second, so batch classification, RAG and agent loops finish before users notice they started.
Groq was founded in 2016 in Mountain View, California by Jonathan Ross, who designed the original Tensor Processing Unit at Google before starting the company. The architecture began life as the Tensor Streaming Processor and was rebranded to LPU (Language Processing Unit) once large language models took over the workload. Headquarters sits in Mountain View, with offices in San Jose, Liberty Lake, Toronto and London. Note for clarity: Groq the inference company is not the same thing as Grok the chatbot from xAI; the names sound alike, the products are unrelated.
The product is GroqCloud, a pay-per-token API that hosts open-weight models on LPU hardware instead of GPUs. The current line-up includes Llama 3.1 8B Instant at around 560 tokens per second, Llama 3.3 70B Versatile at around 280, Llama 4 Scout 17B in preview at around 750, OpenAI's GPT-OSS 120B and 20B at 500 and 1000 respectively, Qwen3-32B at around 400, plus Whisper Large V3 and V3 Turbo for speech-to-text. Groq Compound bundles a model with built-in tools (web search, code execution) at around 450 tokens per second for agent workloads. The LPU is single-architecture, deterministic and built only for inference, which is where the headline tokens-per-second numbers come from. The company licensed its inference technology to Nvidia in late 2025, with Jonathan Ross moving over to lead inference at Nvidia.
Tokens per second, end-to-end latency and spend per workflow on top of GroqCloud, joined to the warehouse content the prompts read.
Pipe Groq output straight into the systems where the work happens, while the user is still on the page.
Open-weight models on LPU read what is in the warehouse, decide, and answer fast enough to keep a human in the loop.
Internal tools that sit on warehouse data and call Groq for the language work without making the user wait.
A list of concrete reports, automations and AI features we have built on Groq data. Pick the one that matches your situation.
Are our agent loops fast enough to keep a user on the page?
End-to-end latency per agent task split into model time, tool-call time and warehouse-fetch time, with the per-call tokens-per-second curve underneath. Catches the agent that picked up an extra retrieval step after a release and now spends three seconds in tool calls between every Groq response, so the speed gain on the LPU is being eaten on the orchestration side instead of by the model.
Which model on Groq is the right one for this workflow?
Tokens per second, output-token spend and quality feedback per model and per workflow side by side. Shows the support-bot workflow that runs fine on Llama 3.1 8B at 560 tokens per second and the contract-analysis workflow that needs Llama 3.3 70B at 280 tokens per second to keep its answer quality, so the choice is no longer a guess.
Is the speed advantage paying off in something the business notices?
Time-to-answer per user-facing workflow before and after moving inference to Groq, joined to the engagement metric the workflow targets (ticket time-to-first-response, deal-update latency, search abandonment). Surfaces the workflows where shaving four seconds turns a feature people tolerated into one they really use, and the workflows where speed was never the bottleneck.
Token spend per Groq endpoint, per workflow and per business unit, joined to the workflow's measured outcome. Speed on its own is not a P&L line; speed plus the support-time saved or proposals shipped is.
Real-time deal-prep and call-summary copilot on Whisper plus Llama 3.3 70B that produces the briefing before the next meeting starts. Reps stop arriving with last quarter's notes because the warehouse handed them the current ones in time.
Tokens per second, end-to-end latency and answer-quality feedback per workflow on one screen, refreshed daily. The Groq pipeline is followed as a curve, not rediscovered the morning a deploy added a retrieval step that ate the speed advantage.
You keep the reporting tool you already have. We connect it to the warehouse where your Groq data lives.
OAuth authentication. Read-only by default. We sign a DPA and your admin keeps the keys.
Data flows into your warehouse on your schedule. Near real time or nightly, your call. You own the data.
We build the first dashboard, workflow or AI feature with you, then hand over the keys. Or we stay on for ongoing delivery.
We set up the foundation. Your team builds on top.
Best fit Teams that already have a BI analyst or data engineer and want to own the build.
We build the whole thing, end to end.
Best fit Teams without in-house BI or dev capacity. You tell us what you need and we deliver it.
You do. It lands in your warehouse, on your cloud account. We don't resell or aggregate it. If you stop working with us, the warehouse stays yours and keeps running.
Near real time for most operational systems. For heavier sources we schedule hourly or nightly. You pick based on what the reports need.
No. If you don't have one, we help you pick one and set it up as part of the first delivery. Common starting points are Snowflake, Microsoft Fabric, or a small Postgres start.
No. Groq (with a q) is the AI inference company founded in 2016 by Jonathan Ross, building the LPU chip and operating GroqCloud. Grok (with a k) is a chatbot product from Elon Musk's xAI. The names sound alike, the products are unrelated, and Groq predates the Grok chatbot by several years.
The LPU (Language Processing Unit) is custom silicon Groq designed specifically for LLM inference, originally introduced as the Tensor Streaming Processor and rebranded once large language models took over the workload. It is single-architecture and deterministic, without the memory hierarchy of a GPU, which is what gives GroqCloud its headline tokens-per-second numbers on models like Llama 3.3 70B. Practically, the LPU is built for inference only; training still happens on GPU clusters elsewhere.
Production models include Llama 3.1 8B Instant (around 560 tokens per second), Llama 3.3 70B Versatile (around 280), GPT-OSS 120B and 20B from OpenAI (around 500 and 1000), and Qwen3-32B (around 400). Llama 4 Scout 17B is in preview at around 750 tokens per second. Whisper Large V3 and V3 Turbo cover speech-to-text. Groq Compound bundles a model with built-in web search and code execution at around 450 tokens per second for agent workloads. The catalogue moves; current speeds and availability are on console.groq.com/docs/models.
Groq is not a replacement for the frontier closed models on every task. The fit is the workloads where speed and open-weight access matter more than the absolute top-of-the-leaderboard answer: bulk classification, real-time agent loops, RAG with long-context Llama or Qwen models, code suggestions inside an IDE, real-time meeting transcription with Whisper. Many teams run a frontier provider for the hardest open-ended generation work and route the high-volume or latency-sensitive calls to Groq, with the warehouse reporting on which workflow is on which provider and why.
We review your Groq setup and the systems around it. Together we pick the first thing worth building.