# Google Vertex AI vs Groq

> A hyperscale AI platform against a speed chip. Vertex covers models, training and agents on Google Cloud; Groq serves a few open models very fast with tight tail latency.

Canonical: https://www.subconscious.dev/compare/google-vertex-vs-groq · By The Subconscious Team · Updated September 30, 2026

## How they compare

These two are rarely substitutes. Groq runs a small set of open models on its own LPU chip and publishes 500 tokens per second on GPT-OSS 120B and 1,000 on GPT-OSS 20B, with latency that stays close between median and tail. That is a narrow, sharp tool for voice agents and multi-step loops where every call has to come back fast. Vertex AI is the opposite shape: 200+ models including Gemini 3.8 and Claude, custom training on GPUs or TPUs, a managed agent runtime, and BigQuery integration. It is the place to build and govern an enterprise AI program, not a latency play.

Limits push the decision further. Groq's catalog is shrinking (Llama 3.3 70B and 3.1 8B shut down in August 2026), context caps around 131K, and it does not host fine-tuned models. Its roadmap is an open question since NVIDIA licensed the LPU and hired most of its engineers. Vertex carries the opposite risk: sprawl, product names in flux and pricing split across services. A sensible split is Vertex for the main reasoning model and data work, with Groq on latency-critical steps like Whisper transcription or quick tool calls.

## What each one does

### Google Vertex AI

Vertex AI is Google Cloud's enterprise AI platform. At Google Cloud Next on April 22, 2026, Google rebranded it the Gemini Enterprise Agent Platform with an agent-first structure, though the API endpoint and most docs still say Vertex. Model Garden offers 200+ models, including Google's Gemini 3.8 family, Anthropic's Claude models and open models like Gemma, alongside Imagen, Veo and Chirp for media and speech. Google's own TPUs sit underneath much of its first-party serving.

### Groq

Groq serves open models on its own chip, the LPU, which keeps model weights in on-chip SRAM and runs a deterministic schedule instead of waiting on GPU memory. Groq publishes 1,000 tokens per second on GPT-OSS 20B and 500 on GPT-OSS 120B. Latency stays tight between median and tail, which matters for strict SLAs. The API is OpenAI-compatible and also hosts Whisper for speech to text plus an agentic system called Groq Compound with built-in search and code execution.

## Which is best, and when

### Choose Google Vertex AI for

- Long-context and multimodal work on Gemini
- Training and serving custom models in one governed stack
- Enterprise agent programs on Google Cloud

### Choose Groq for

- Voice agents where any pause reads as awkward
- Fast agent steps on GPT-OSS or Qwen 3.6
- Strict SLAs that depend on tail latency

## At a glance

| Attribute | Google Vertex AI | Groq |
|---|---|---|
| Model access | Closed and open, 200+ models | Open weights |
| Flagship models | Gemini 3.8 Flash, Claude, Gemma | GPT-OSS 120B, Qwen 3.6 27B |
| Speed | Flash tier built for low latency | 500–1,000 tok/s |
| Price | Gemini 3.8 Flash $0.75 in, $3.75 out | Near the floor on small models |
| Customization | Custom training on GPUs or TPUs | No fine-tuned model hosting |
| Deployment | Managed on Google Cloud | GroqCloud API |
| Long context | 1M on Gemini 3.8 Flash | Around 131K max |

## FAQ

### What is the difference between Google Vertex AI and Groq?

A hyperscale AI platform against a speed chip. Vertex covers models, training and agents on Google Cloud; Groq serves a few open models very fast with tight tail latency.

### When should I choose Google Vertex AI over Groq?

Long-context and multimodal work on Gemini; Training and serving custom models in one governed stack; Enterprise agent programs on Google Cloud.

### When should I choose Groq over Google Vertex AI?

Voice agents where any pause reads as awkward; Fast agent steps on GPT-OSS or Qwen 3.6; Strict SLAs that depend on tail latency.

### Is Google Vertex AI or Groq cheaper?

Google Vertex AI: Gemini 3.8 Flash $0.75 in, $3.75 out. Groq: Near the floor on small models. The cheaper choice depends on the model and workload.

### Which has more context, Google Vertex AI or Groq?

Google Vertex AI: 1M on Gemini 3.8 Flash. Groq: Around 131K max.

## Running long-horizon agents?

If your agents run past 200K tokens, compare both against Subconscious: [Subconscious vs Google Vertex AI](https://www.subconscious.dev/compare/subconscious-vs-google-vertex.md), [Subconscious vs Groq](https://www.subconscious.dev/compare/subconscious-vs-groq.md).

Full profiles: [Google Vertex AI](https://www.subconscious.dev/providers/google-vertex.md), [Groq](https://www.subconscious.dev/providers/groq.md).