Google Vertex AI vs General Compute

Vertex AI is Google Cloud's full AI platform with Gemini and Claude. General Compute is a focused inference cloud serving open models on ASICs.

By The Subconscious Team · Updated October 27, 2026

Google Vertex AI vs General Compute: key differences

Vertex AI, now branded Gemini Enterprise Agent Platform, offers 200+ models in Model Garden, including Gemini 3.8 Flash at $0.75 in and $3.75 out per million tokens with a 1M window through 2026. Custom training, pipelines, vector search and BigQuery integration sit alongside. General Compute only does inference. Its OpenAI-compatible API serves four open models from $0.21 in, with GPU prefill and decode on Cerebras, SambaNova, Positron AI and d-Matrix chips. In its own benchmark it posts over 1,000 tokens per second on GPT-OSS 120B.

Vertex is the pick for enterprises already on Google Cloud that want governed agents, multimodal work and training in one stack. General Compute fits teams that want fast, cheap open-model tokens without adopting a cloud platform, or reserved ASIC capacity under a tokens-per-second SLA. Its catalog is small, tops out at 192K context and runs only in us-west-2.

What Google Vertex AI and General Compute do

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.

Example models: Gemini 3.8, Claude

Full Google Vertex AI profile

General Compute

General Compute is a San Francisco inference cloud built on alternative chips. It splits each request across hardware, with NVIDIA GPUs handling prefill and ASICs from Cerebras, SambaNova, Positron AI and d-Matrix handling decode. In its own open benchmark on GPT-OSS 120B it measured 1,025 to 1,234 output tokens per second against 100 to 121 on an unnamed GPU cloud, and up to 16.2x faster end to end. Founders Finn Puklowski and Jason Goodison raised a $15M seed led by FUSE, an up to $400M debt facility from Upper90 and, on October 27, 2026, a $100M Series A co-led by Blackbird and Abstract.

Example models: MiniMax M2.7, DeepSeek V3.2, GPT-OSS 120B

Full General Compute profile

Should you choose Google Vertex AI or General Compute?

Google Vertex AI

Choose Google Vertex AI for

Enterprises standardized on Google Cloud
Gemini multimodal and 1M context work
Training and MLOps in one stack

General Compute

Choose General Compute for

Fast decode on open models
Simple per-token pricing
Dedicated racks with throughput SLAs

Google Vertex AI vs General Compute at a glance

Attribute	Google Vertex AI	General Compute
Model access	Closed and open, 200+ models	Open weights
Flagship models	Gemini 3.8 Flash, Claude, Gemma	MiniMax M2.7, DeepSeek V3.2, GPT-OSS 120B
Speed	Flash tier built for low latency	1,000+ tok/s on GPT-OSS 120B (vendor)
Price	Gemini 3.8 Flash $0.75 in, $3.75 out until 2027, then $1.50/$7.50	$0.21–$0.28 in, $0.38–$1.20 out per 1M
Customization	Custom training on GPUs or TPUs	Custom LoRA and fine-tuned checkpoints
Deployment	Managed on Google Cloud	Serverless API, dedicated racks, bare metal
Long context	1M on Gemini 3.8 Flash	192K (MiniMax M2.7)

Frequently asked questions

What is the difference between Google Vertex AI and General Compute?

Vertex AI is Google Cloud's full AI platform with Gemini and Claude. General Compute is a focused inference cloud serving open models on ASICs.

When should I choose Google Vertex AI over General Compute?

Enterprises standardized on Google Cloud; Gemini multimodal and 1M context work; Training and MLOps in one stack.

When should I choose General Compute over Google Vertex AI?

Fast decode on open models; Simple per-token pricing; Dedicated racks with throughput SLAs.

Is Google Vertex AI or General Compute cheaper?

Google Vertex AI: Gemini 3.8 Flash $0.75 in, $3.75 out until 2027, then $1.50/$7.50. General Compute: $0.21–$0.28 in, $0.38–$1.20 out per 1M. The cheaper choice depends on the model and workload.

Which has more context, Google Vertex AI or General Compute?

Google Vertex AI: 1M on Gemini 3.8 Flash. General Compute: 192K (MiniMax M2.7).

Related comparisons

Running long-horizon agents?

If your agents run past 200K tokens, compare both against Subconscious. Our inference stack treats a long-horizon trace as the primary workload, so speed, cost, and accuracy hold up deep into the trace.

Subconscious vs Google Vertex AI Subconscious vs General Compute