Google Vertex AI vs Relace

A general enterprise AI platform and a toolkit of small coding-agent models. Vertex runs the reasoning model; Relace handles apply, search and compaction around it.

By The Subconscious Team · Updated September 30, 2026

Google Vertex AI vs Relace: key differences

Relace is not trying to be Vertex AI. It trains small, fast models that act as tools for coding agents: relace-apply-3 merges a frontier model's lazy edit into the original file at about 10,000 tokens per second, an agentic search model explores large codebases in parallel, and a compaction model runs at 50,000 tokens per second. Vertex AI is where the frontier model itself might live, with Gemini 3.8 and Claude in Model Garden next to training, evaluation and an agent runtime on Google Cloud. A coding product could call both within one task.

The pairing works because each covers the other's gap. Relace says its apply step runs over 3x faster and cheaper than having the big model rewrite the file, and it offers self-hosted deployment for enterprises that keep code in-house. It returns an error past 128K tokens, so very large files need a fallback model, which a Vertex-hosted model could provide. Vertex brings the general-purpose models and governance Relace lacks, but no specialist apply or compaction model.

What Google Vertex AI and Relace 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

Relace

Relace trains small, fast models that act as tools for coding agents. Its best-known product is Instant Apply: a frontier model writes a lazy edit snippet, and relace-apply-3 merges it into the original file at about 10,000 tokens per second with 128K tokens of input and output. Relace says this runs over 3x faster and cheaper than having the big model rewrite the file. It exposes both a REST endpoint and an OpenAI-compatible one, and the model is also listed on OpenRouter.

Example models: relace-apply-3, Relace agentic search

Full Relace profile

Should you choose Google Vertex AI or Relace?

Google Vertex AI

Choose Google Vertex AI for

The frontier model that plans and writes code changes
A fallback for files past Relace's 128K limit
Enterprise governance on Google Cloud

Relace

Choose Relace for

Merging lazy edits into files quickly and cheaply
Parallel agentic search over large repos
Self-hosted code tooling for in-house codebases

Google Vertex AI vs Relace at a glance

Attribute	Google Vertex AI	Relace
Model access	Closed and open, 200+ models	Specialist models
Flagship models	Gemini 3.8 Flash, Claude, Gemma	relace-apply-3, agentic search
Speed	Flash tier built for low latency	~10,000 tok/s apply
Price	Gemini 3.8 Flash $0.75 in, $3.75 out	3x+ cheaper than full rewrites
Customization	Custom training on GPUs or TPUs	Unknown
Deployment	Managed on Google Cloud	Hosted API or self-hosted
Long context	1M on Gemini 3.8 Flash	128K max

Frequently asked questions

What is the difference between Google Vertex AI and Relace?

A general enterprise AI platform and a toolkit of small coding-agent models. Vertex runs the reasoning model; Relace handles apply, search and compaction around it.

When should I choose Google Vertex AI over Relace?

The frontier model that plans and writes code changes; A fallback for files past Relace's 128K limit; Enterprise governance on Google Cloud.

When should I choose Relace over Google Vertex AI?

Merging lazy edits into files quickly and cheaply; Parallel agentic search over large repos; Self-hosted code tooling for in-house codebases.

Is Google Vertex AI or Relace cheaper?

Google Vertex AI: Gemini 3.8 Flash $0.75 in, $3.75 out. Relace: 3x+ cheaper than full rewrites. The cheaper choice depends on the model and workload.

Which has more context, Google Vertex AI or Relace?

Google Vertex AI: 1M on Gemini 3.8 Flash. Relace: 128K max.

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 Relace