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Google: Gemini Embedding 2 Preview

Google: Gemini Embedding 2 Preview is a cost-efficient model with standard context support, suited to semantic retrieval and enterprise search for enterprise teams.

Try Google: Gemini Embedding 2 Preview with your team

Last reviewed: 2026-06-09

Google

Preview

Context Window

8,192

Input / 1M

$0.30

Output / 1M

$0.00

Watch Google: Gemini Embedding 2 Preview in Remova

See how a team selects Google: Gemini Embedding 2 Preview, passes policy checks, and routes the request safely through Remova.

Use Google: Gemini Embedding 2 Preview Safely on Remova

Model demo

A 36-second overview showing how teams can select Google: Gemini Embedding 2 Preview inside Remova, pass policy checks, apply it to real-world work, and use advanced AI with redaction, routing, budgets, and audit trails.

Video transcript

Google: Gemini Embedding 2 Preview for enterprise AI. Remova routes model access, long-context analysis, and assistant workflows through governance controls. In the Remova interface, a user selects Google: Gemini Embedding 2 Preview, passes sensitive data redaction, budget threshold, and role access checks, then runs the request safely. Teams can use Google: Gemini Embedding 2 Preview for improve enterprise search, power semantic retrieval, deduplicate knowledge assets, route support requests, rank compliance evidence, measure content similarity. Use Google: Gemini Embedding 2 Preview safely on Remova with redaction, routing, budgets, and audit trails built in. Sign up now.

What can you do with Google: Gemini Embedding 2 Preview?

Practical ways teams can use Google: Gemini Embedding 2 Preview inside governed AI workflows.

Improve enterprise search with Google: Gemini Embedding 2 Preview

Rank documents, answers, and knowledge-base results so teams find the right information faster with Google: Gemini Embedding 2 Preview.

Power semantic retrieval with Google: Gemini Embedding 2 Preview

Match user questions to relevant policies, product docs, tickets, and internal references with Google: Gemini Embedding 2 Preview.

Deduplicate knowledge assets with Google: Gemini Embedding 2 Preview

Cluster related content, similar records, and overlapping documents for cleaner operations with Google: Gemini Embedding 2 Preview.

Route support requests with Google: Gemini Embedding 2 Preview

Classify incoming questions and connect them with the most relevant internal resources with Google: Gemini Embedding 2 Preview.

Rank compliance evidence with Google: Gemini Embedding 2 Preview

Surface the most relevant policies, logs, and documents during audits and reviews with Google: Gemini Embedding 2 Preview.

Measure content similarity with Google: Gemini Embedding 2 Preview

Compare records, tickets, snippets, and documents for matching or recommendation workflows with Google: Gemini Embedding 2 Preview.

Why this model

Google: Gemini Embedding 2 Preview is available in Remova as a standard context option with $0.30 per 1M tokens input pricing, $0.00 per 1M tokens output pricing, and text+image+file+audio+video->embeddings modality support for enterprise AI operations.

Google: Gemini Embedding 2 Preview offers standard context capacity for enterprise prompts and documents.
Current Remova pricing band is cost-efficient: $0.30 per 1M tokens input and $0.00 per 1M tokens output.
Best-fit workloads include: Semantic retrieval, Enterprise search, Knowledge indexing.
Keep role-based access in place before broad rollout.

At a glance

Model ID: google/gemini-embedding-2-preview
Context Window: 8,192 tokens
Modality: text+image+file+audio+video->embeddings
Input Modalities: text, image, file, audio, video
Output Modalities: embeddings
Input Price: $0.30 per 1M tokens
Output Price: $0.00 per 1M tokens
Provider: Google
Listing Date: 2026-04-17

Strengths

Google: Gemini Embedding 2 Preview is suited for semantic retrieval.
Supports standard context for multi-step prompts and larger working sets.
Pricing profile is cost-efficient, enabling predictable workload routing decisions.
Can be paired with policy guardrails for safer deployment at scale.

Tradeoffs

Without workload routing, teams may overuse this model for requests that fit lower-cost tiers.
Standard context limits may require chunking or retrieval strategies for large documents.
Low-cost tiers can still underperform on high-consequence decisions without escalation paths.
Embedding and retrieval systems need benchmark sets to catch ranking drift and stale indexes.

Best for

Google: Gemini Embedding 2 Preview for semantic retrieval, ranking, and enterprise search workflows.
Google: Gemini Embedding 2 Preview for enterprise search across policies, product docs, and support knowledge bases.
Google: Gemini Embedding 2 Preview for indexing internal knowledge assets into searchable vector workflows.
Google: Gemini Embedding 2 Preview for surfacing compliance evidence and related records during audits.

Rollout checklist

Define where Google: Gemini Embedding 2 Preview is default vs. fallback in your routing policy.
Enable role-based access and policy checks before opening access broadly.
Set spend guardrails by team and monitor weekly token consumption.
Start with one workflow, then expand after you verify quality and spend.
Re-run quality and cost benchmarks monthly as newer releases appear.

Related models

Explore adjacent model profiles for routing and benchmarking decisions.

Free Resource

Where Should Your Team Start with AI?

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A shortlist of AI use cases ranked by impact and effort for your situation.

Tuning notes

max_tokens

Set completion limits to avoid unpredictable long-output spend.

response_format

Prefer structured output where responses feed internal systems.

seed

Use this parameter only with tested defaults in production workflows.

temperature

Lower temperature for deterministic policy and compliance tasks.

Free Assessment

What Could Go Wrong?

5 questions about how your company uses AI today. We'll show you the risks most companies miss until it's too late.

You get

A risk breakdown with the 3 things you should fix first.

Knowledge Hub

Google: Gemini Embedding 2 Preview FAQs

Choose Google: Gemini Embedding 2 Preview when the workload aligns with semantic retrieval, enterprise search, knowledge indexing and quality targets justify its pricing profile.

It depends on workload mix. Most organizations use routing policies so routine traffic stays on lower-cost tiers.

Validate quality on real internal prompts, token efficiency, latency, and policy compliance behavior.

Deploy This Model With Governance

Use policy controls, role-based access, and budget guardrails before enabling advanced model tiers at scale.

Try Google: Gemini Embedding 2 Preview with your team