Deployment Brief

Text Embedding Ada 002 (batch)

Text Embedding Ada 002 (batch) is a cost-efficient model with standard context support, suited to semantic retrieval and enterprise search for enterprise teams.

Try Text Embedding Ada 002 (batch) with your team

Last reviewed: 2026-09-02

OpenAI

Stable

Context Window

8,192

Input / 1M

$0.08

Output / 1M

$0.00

What can you do with Text Embedding Ada 002 (batch)?

Practical ways teams can use Text Embedding Ada 002 (batch) inside governed AI workflows.

Improve enterprise search with Text Embedding Ada 002 (batch)

Rank documents, answers, and knowledge-base results so teams find the right information faster with Text Embedding Ada 002 (batch).

Power semantic retrieval with Text Embedding Ada 002 (batch)

Match user questions to relevant policies, product docs, tickets, and internal references with Text Embedding Ada 002 (batch).

Deduplicate knowledge assets with Text Embedding Ada 002 (batch)

Cluster related content, similar records, and overlapping documents for cleaner operations with Text Embedding Ada 002 (batch).

Route support requests with Text Embedding Ada 002 (batch)

Classify incoming questions and connect them with the most relevant internal resources with Text Embedding Ada 002 (batch).

Rank compliance evidence with Text Embedding Ada 002 (batch)

Surface the most relevant policies, logs, and documents during audits and reviews with Text Embedding Ada 002 (batch).

Measure content similarity with Text Embedding Ada 002 (batch)

Compare records, tickets, snippets, and documents for matching or recommendation workflows with Text Embedding Ada 002 (batch).

Why this model

Text Embedding Ada 002 (batch) is available in Remova as a standard context option with $0.08 per 1M tokens input pricing, $0.00 per 1M tokens output pricing, and text input to embeddings output support.

Text Embedding Ada 002 (batch) offers standard context capacity for enterprise prompts and documents.
Current Remova pricing band is cost-efficient: $0.08 per 1M tokens input and $0.00 per 1M tokens output.
Best-fit workloads include: Semantic retrieval, Enterprise search, Knowledge indexing.
Route requests by policy tier so teams do not overuse capability.

At a glance

Model ID: openai/text-embedding-ada-002:batch
Context Window: 8,192 tokens
Modality: Text input to embeddings output
Input Modalities: Text
Output Modalities: Embeddings
Input Price: $0.08 per 1M tokens
Output Price: $0.00 per 1M tokens
Provider: OpenAI
Listing Date: 2025-10-30

Strengths

Text Embedding Ada 002 (batch) 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

Policy exceptions should be monitored and reviewed on a fixed cadence.
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

Text Embedding Ada 002 (batch) for semantic retrieval, ranking, and enterprise search workflows.
Text Embedding Ada 002 (batch) for enterprise search across policies, product docs, and support knowledge bases.
Text Embedding Ada 002 (batch) for indexing internal knowledge assets into searchable vector workflows.
Text Embedding Ada 002 (batch) for surfacing compliance evidence and related records during audits.

Rollout checklist

Define where Text Embedding Ada 002 (batch) 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.
Define escalation rules to premium models before launch.
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

frequency_penalty

Tune repetition control for long responses in multi-step workflows.

logit_bias

Use this parameter only with tested defaults in production workflows.

logprobs

Use this parameter only with tested defaults in production workflows.

max_tokens

Set completion limits to avoid unpredictable long-output spend.

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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.

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

Knowledge Hub

Text Embedding Ada 002 (batch) FAQs

Choose Text Embedding Ada 002 (batch) 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 Text Embedding Ada 002 (batch) with your team