Databricks Mosaic vs Vertex AI: 2026 comparison

Side-by-side

Databricks Mosaic vs Vertex AI — at a glance

Dimension	Databricks Mosaic AI	Vertex AI
Parent cloud	Multi-cloud (AWS, Azure, GCP)	Google Cloud only
Flagship in-house model	DBRX (132B MoE)	Gemini 2.5 Pro
Model catalog	~25 foundation models	300+ (Model Garden)
Pricing unit	DBUs ($0.07-0.40) + GPU	Per token / node-hour
Gemini 2.5 Pro price	N/A	~$1.25 / $10 per 1M tok
Fine-tuning strength	Best-in-class (Mosaic heritage)	Solid (SFT, RLHF on Gemini)
Data integration	Delta Lake native, Unity Catalog	BigQuery, Cloud Storage
Best for	Fine-tuning on lakehouse data, governance	Gemini APIs, broad catalog, GCP teams

When to choose which

Pick Databricks Mosaic AI or Vertex AI?

When to choose Databricks Mosaic AI

Choose Mosaic AI when your enterprise data already lives in a Databricks Lakehouse and you need to fine-tune or RAG against it without copying data out. Mosaic inherited the team that wrote MPT-7B, DBRX, and the OG fine-tuning playbooks; their continued-pretraining and instruction-tuning pipelines remain best-in-class. Unity Catalog gives you row-level governance directly on top of model-serving endpoints.

You already use Databricks for data engineering
Strongest enterprise fine-tuning pipeline (continued pretraining, DPO)
DBRX 132B MoE for high-quality open-source baseline
Multi-cloud — runs on AWS, Azure, or GCP
Unity Catalog governance on training and serving

When to choose Vertex AI

Choose Vertex AI when you want first-party access to Gemini 2.5 Pro/Flash, the broadest model catalog on a major cloud, and tight integration with BigQuery and the rest of Google Cloud. Vertex covers everything from prompt design to model serving and managed pipelines, plus an Agent Builder for retrieval-augmented chatbots. It is the default for teams on GCP.

First-party Gemini 2.5 Pro at $1.25 / $10 per 1M tok
300+ models in Model Garden (Claude via partnership, Llama, Mistral, Gemma)
BigQuery, GCS, and Cloud Run native integration
Agent Builder for grounded RAG chatbots
Strong on regulated EU/US regions with VPC-SC

Why not both?

Route Mosaic AI and Vertex AI through one endpoint

VerticalAPI exposes Vertex AI Gemini models through a single OpenAI-compatible endpoint, and Databricks Mosaic AI endpoints via custom HTTPS routes. Use the same SDK across both. BYOK means no markup on tokens — you pay Google and Databricks directly.

from openai import OpenAI
client = OpenAI(base_url="https://api.verticalapi.com/v1", api_key="vapi_...")

# Vertex AI Gemini 2.5 Pro — via VerticalAPI BYOK
resp_a = client.chat.completions.create(
    model="vertex/gemini-2.5-pro",
    messages=[{"role": "user", "content": "Hello"}],
    extra_headers={"X-Provider-Key": "ya29..."},
)

# Databricks Mosaic AI DBRX — same SDK
resp_b = client.chat.completions.create(
    model="databricks/dbrx-instruct",
    messages=[{"role": "user", "content": "Hello"}],
    extra_headers={"X-Provider-Key": "dapi-..."},
)

Try VerticalAPI free →

VerticalAPI verdict

Pick Databricks Mosaic AI when fine-tuning on lakehouse data is the core workload. Pick Vertex AI when you need first-party Gemini and a wide model catalog on Google Cloud. For agent platforms that need both, route through VerticalAPI: Vertex for fast Gemini inference, Mosaic for fine-tuned domain experts. One OpenAI-compatible endpoint, BYOK, no markup.

Get started — BYOK both providers →

FAQ

Frequently asked questions

What is the difference between Databricks Mosaic AI and Google Vertex AI?

Databricks Mosaic AI is an enterprise LLM platform built on top of the Databricks Lakehouse — it integrates fine-tuning, RAG, model serving, and governance directly on top of Delta Lake data. Vertex AI is Google Cloud's managed AI platform, offering Gemini 2.5 Pro and Flash, AutoML, Model Garden (300+ models), and tight BigQuery integration. Mosaic AI focuses on bringing models to data; Vertex AI focuses on a Google-curated model catalog and GCP-native MLOps.

How does pricing compare between Mosaic AI and Vertex AI?

Databricks Mosaic AI charges in DBUs (Databricks Units) consumed by model serving and training — roughly $0.07-0.40 per DBU depending on tier, plus underlying cloud GPU costs. Vertex AI bills per token for Gemini (Gemini 2.5 Pro at approximately $1.25/$10 per 1M input/output) and per node-hour for custom models. Mosaic AI tends to be cheaper when you already own data in Databricks; Vertex AI is cheaper for pure inference on Gemini models without data-platform commitments.

What models does each platform offer?

Mosaic AI hosts DBRX (Databricks' open-source 132B MoE model), Llama 3.3, Mistral, plus custom fine-tunes — about 25 foundation models via Model Serving. Vertex AI Model Garden lists 300+ models including Gemini 2.5 Pro/Flash, Claude (via partnership), Llama, Mistral, Gemma, plus open and partner models. Vertex has the broader catalog; Mosaic has tighter integration with proprietary data.

Which is better for fine-tuning?

Databricks Mosaic AI is widely regarded as the strongest enterprise fine-tuning platform in 2026 — it inherited Mosaic's training expertise and offers continued pretraining, instruction tuning, and DPO on customer data without leaving the Lakehouse. Vertex AI supports supervised fine-tuning and RLHF on Gemini, but with a more rigid pipeline. For LLM customization on proprietary data, Mosaic AI is typically the choice.

Can I access both through VerticalAPI?

VerticalAPI provides OpenAI-compatible BYOK routing to Vertex AI today (Gemini 2.5 Pro, Flash, and Model Garden endpoints) via https://api.verticalapi.com/v1. Databricks Mosaic AI endpoints can be added as custom HTTPS providers when teams need both unified. There is zero markup on tokens — you pay Google and Databricks directly with your own credentials.

Caveats

Limitations of this comparison

Mosaic AI DBU pricing varies by SKU (Standard, Premium, Enterprise) and cloud — the $0.07-0.40 range is illustrative not authoritative.
Vertex AI Gemini list prices change frequently; 2026 figures reflect mid-year pricing and exclude committed-use discounts (often 20-40%).
"Best fine-tuning" depends heavily on use case — Vertex's supervised tuning on Gemini may outperform Mosaic for tasks where Gemini is already the strongest baseline.
Vertex AI Model Garden includes many models in preview or limited regional availability; effective catalog size is smaller than 300 for production use.
Cross-cloud egress fees can dominate total cost when Mosaic AI runs in one cloud and applications in another.

Outlook

What may change in 12-24 months

Databricks is expected to ship a DBRX 2 successor and tighter Unity Catalog enforcement on model outputs.
Vertex AI will likely expand Agent Builder into a full agent runtime competing with OpenAI Assistants and AWS Bedrock Agents.
Cross-platform fine-tuning portability (LoRA adapters, GGUF) will let teams train on Mosaic and serve on Vertex or vice versa.
Pricing pressure from open-weight + serverless GPU providers will continue pushing both platforms' per-token costs down 20-40% per year.

Keep reading