Google Vertex Embeddings isn’t just a model endpoint; it’s an infrastructure decision. While the legacy gecko models confused users with per-character billing, the new gemini-embedding-001 finally standardizes on per-token pricing at $0.15 per 1 million tokens. It brings Google’s internal heavy weaponry—Matryoshka Representation Learning and massive multilingual support—to a public API.
For a RAG application processing 10 million tokens a month (roughly 20,000 documents), you’re looking at a negligible $1.50/month. The cost is effectively zero for most startups. The real cost comes from the "Google tax": complexity. unlike OpenAI where you paste an API key and go, Vertex requires navigating GCP’s IAM permissions, service accounts, and the google-cloud-aiplatform SDK. It’s secure, but it’s heavy.
The star here is gemini-embedding-001. Its Matryoshka capability allows you to generate 3072-dimensional vectors for maximum precision, then truncate them to 768 or smaller for storage without retraining. This is a massive win for vector DB costs. On the flip side, the 2,048 token input limit is a glaring weakness in 2026. While OpenAI offers 8,192 tokens, enabling full-document embedding, Vertex forces you to chunk aggressiveley. If your RAG strategy relies on large parent-document retrieval, this is a dealbreaker.
Performance is what you'd expect from Google: rock-solid availability and excellent multilingual handling (100+ languages). The integration with Vertex AI Vector Search is seamless, allowing you to scale to billions of vectors if you’re building the next Google Search. However, for a simple internal tool, the infrastructure overhead feels like bringing an aircraft carrier to a fishing trip.
Skip this if you are a solo dev or a small team wanting instant implementation; OpenAI or Voyage AI are faster to integrate. Use Vertex Embeddings if your data is already in Google Cloud, you need strict IAM compliance, or you plan to scale your vector index into the billions.
Pricing
Google has largely moved away from the confusing $0.000025/1k characters pricing of the legacy gecko models. The modern gemini-embedding-001 costs $0.15 per 1M tokens, slightly higher than OpenAI's text-embedding-3-large ($0.13/1M). The economy text-embedding-005 is $0.10 per 1M tokens.
There is no permanent free tier for the API specifically, only the standard $300 GCP credit for new accounts. The real cost isn't the API fees (which are trivial for most), but the potential associated costs of Vertex AI Vector Search infrastructure if you choose to use their managed index service, which allows no 'scale-to-zero'.
Technical Verdict
Enterprise-grade reliability wrapped in GCP bureaucracy. Latency is excellent, but initial setup requires fighting IAM policies rather than just generating a key. The Python SDK is robust but verbose. The 2,048 token hard limit is the biggest technical bottleneck, forcing strict chunking strategies compared to competitors' 8k+ windows.
Quick Start
# pip install google-cloud-aiplatform
from vertexai.language_models import TextEmbeddingInput, TextEmbeddingModel
model = TextEmbeddingModel.from_pretrained("gemini-embedding-001")
inputs = [TextEmbeddingInput("Vertex AI handles scale well.", "RETRIEVAL_DOCUMENT")]
embeddings = model.get_embeddings(inputs)
print(embeddings[0].values[:5]) # Prints first 5 dimensionsWatch Out
- Hard limit of 2,048 tokens per input; silent truncation can occur if not handled.
- Requires a GCP Service Account with correct IAM roles; an API key alone often isn't enough.
- Dimensionality truncation (Matryoshka) must be handled at the storage layer; the API returns full vectors by default unless configured.
- Legacy models use character-based pricing; ensure you select
geminior newer005versions to avoid billing surprises.