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Google: Gemini Embedding 2

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

Try Google: Gemini Embedding 2 with your team

Last reviewed: 2026-05-31

Google: Gemini Embedding 2

Google

Stable
Context Window
8,192
Input / 1M
$0.30
Output / 1M
$0.00

What can you do with Google: Gemini Embedding 2?

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

01

Improve enterprise search with Google: Gemini Embedding 2

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

02

Power semantic retrieval with Google: Gemini Embedding 2

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

03

Deduplicate knowledge assets with Google: Gemini Embedding 2

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

04

Route support requests with Google: Gemini Embedding 2

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

05

Rank compliance evidence with Google: Gemini Embedding 2

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

06

Measure content similarity with Google: Gemini Embedding 2

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

Why this model

Google: Gemini Embedding 2 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 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
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
2026-05-20

Strengths

  • Google: Gemini Embedding 2 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

  • Prompt standards are still needed to keep output quality consistent across teams.
  • 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 for semantic retrieval, ranking, and enterprise search workflows.
  • Google: Gemini Embedding 2 for enterprise search across policies, product docs, and support knowledge bases.
  • Google: Gemini Embedding 2 for indexing internal knowledge assets into searchable vector workflows.
  • Google: Gemini Embedding 2 for surfacing compliance evidence and related records during audits.

Rollout checklist

  • Define where Google: Gemini Embedding 2 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.

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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.

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Google: Gemini Embedding 2 FAQs

Choose Google: Gemini Embedding 2 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 with your team