Curiosity · AI Model
Vertex AI textembedding-gecko
textembedding-gecko is Google Cloud's flagship managed text-embedding endpoint on Vertex AI. Built on the Gecko family of small dense encoders distilled from larger Gemini models, it exposes a stable 768-dimensional embedding API optimised for RAG, semantic search, and classification at enterprise scale.
Model specs
- Vendor
- Family
- Gecko embeddings
- Released
- 2023-07
- Context window
- 2,048 tokens
- Modalities
- text
- Input price
- $0.025/M tok
- Output price
- n/a
- Pricing as of
- 2026-04-20
Strengths
- Managed endpoint with Google Cloud SLA and IAM
- Integrated with Vertex AI Search and BigQuery
- Distilled from larger Gemini teacher models
Limitations
- Closed — you cannot run it outside Google Cloud
- English-centric — use textembedding-gecko-multilingual for other languages
- Below research-grade open-weights LLM embedders on peak MTEB scores
Use cases
- Enterprise RAG over Google Cloud Storage / BigQuery corpora
- Semantic search in Vertex AI Search applications
- Classification and clustering pipelines via Dataflow
- Hybrid search combining BM25 and dense embeddings
Benchmarks
| Benchmark | Score | As of |
|---|---|---|
| MTEB English average (Gecko technical report) | strong vs. comparable-size encoders | 2024-03 |
Frequently asked questions
What is textembedding-gecko?
textembedding-gecko is Google Cloud's managed text-embedding endpoint on Vertex AI. It exposes a stable 768-dimensional embedding API for retrieval, semantic search, and classification.
How is it priced?
Vertex AI charges per 1,000 input characters or per million input tokens (current rates around a few cents per million tokens). Check the Vertex AI pricing page for the latest rates.
How does Gecko compare to NV-Embed or SFR-Embedding?
Gecko is smaller, cheaper, and managed — ideal for enterprise integration on Google Cloud. NV-Embed and SFR-Embedding are larger open-weights models with higher peak retrieval quality.
Sources
- Google Cloud — Text embeddings API — accessed 2026-04-20
- arXiv — Gecko embeddings paper — accessed 2026-04-20