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Multimodal RAG Stack - Vision + Audio + Text Retrieval Cost

🚀 Open the calculator

Meet Esme Vasquez. ML Engineer building a video-and-document Q&A product. "Users upload videos + PDFs + images + ask questions. How do we cost-out the full multimodal RAG?"

🔥 Pricing models exist for each modality - never combined cleanly. Need an architecture estimate.

The story

Multimodal RAG combines 3+ pipelines that have different cost models. Image: per-image vision embedding + storage + retrieval. Audio: STT to text → embedding (or specialized audio embedding) + transcript storage. Text: standard chunking + embedding + retrieval. Plus the LLM read at the end with multimodal context.

Esme's product processes 1,000 videos/day (avg 10 min each) + 5K PDFs/day + 100K image queries/day. Audio transcription: 10K min/day → ~$60/day STT + embedding storage. Vision: 100K images × varies = $200-500/day vision processing. Text: 5K PDFs × 30 pages × embedding/storage. LLM read with mixed-modality context: ~$300/day. Total: ~$700-900/day = $21-27K/mo.

Three multimodal architectures. (1) Convert-everything-to-text (transcribe audio, OCR images, then text-only RAG). (2) Native multimodal embeddings (CLIP, multimodal embedding models). (3) Hybrid (image embeddings + text embeddings + audio transcripts). Each has different cost profiles.

📊 CALCULATOR AT A GLANCE
🚀 Open the full calculator ✉️ Email [email protected]
Multimodal RAG Stack - Vision + Audio + Text Retrieval Cost full size

🎛 Inputs you control

Each input shapes the cost. Click an input on the calculator to set it — explanations below match the live calculator field by field.

▸ Corpus tokens (total) — Total tokens across all documents to embed and index.
How to choose: Sum docs times tokens per doc; drives one-time indexing cost.
▸ Queries per day — Daily query volume hitting the pipeline.
How to choose: Use real traffic; recurring retrieval and LLM-read cost scale with this.
▸ Tokens per query — Average query length in tokens before retrieval.
How to choose: Short search queries are 20 to 100 tokens.
▸ Embedding model — Model used to embed chunks and queries.
How to choose: Balance retrieval quality, dimensions/storage, and price per 1M tokens.
▸ Vector database — Database storing and serving the embeddings.
How to choose: Managed is lower ops; self-hosted is cheaper at scale with a team.
▸ LLM read model — Model that writes the answer from retrieved multimodal context.
How to choose: Usually the dominant cost; route cheaper tiers for simple answers.
▸ Retrieved tokens per query — Context tokens fed to the LLM per query.
How to choose: Top-K times chunk size; more context improves recall but adds cost.
▸ LLM output tokens — Answer length the LLM generates per query.
How to choose: Estimate typical answer length; output tokens are priced higher than input.

About this calculator: Multimodal RAG Stack - Vision + Audio + Text Retrieval Cost

Multimodal RAG combines image embeddings, audio transcription, and text retrieval. Real architecture math for production multimodal apps.

Inputs you control

Input Impact on result Range Typical
Video minutes processed per day Total video processed (transcription + key-frame embedding). 0 – 1M 10000
Image queries/embeddings per day Standalone images embedded or queried. 0 – 10M 100000
Documents (PDF/text) processed per day PDFs / text docs. Each may have many pages. 0 – 1M 5000

Outputs computed for you · model: multimodal_stack

Output How inputs affect it
Monthly cost ($) computed from inputs
Annual cost ($) monthlyUsd × 12

Below: live sliders. Move them to see numbers change in real time. * Output uses the generic compute model — for precise numbers use the full calculator below.

What you're looking at

Each input shapes your cost. Move the slider — see the impact.

10,000

Total video processed (transcription + key-frame embedding).

Estimated: —
100,000

Standalone images embedded or queried.

Estimated: —
5,000

PDFs / text docs. Each may have many pages.

Estimated: —

Ready to run the numbers?

Open the full calculator — pick a model, enter your tokens, see per-call, daily, monthly, and annual cost.

🚀 Open the full calculator →

Reading your result

Audio costs are per-minute, dominated by transcription. $0.003-0.006/min STT × 10K min × 30 = $900-1,800/mo. Plus embedding storage: marginal.

Vision costs are per-image, varies wildly. Low-detail classification: $0.001/image. High-detail OCR: $0.01-0.05/image. 100K/day at OCR-quality = $1-5K/mo.

Text RAG is the cheapest line per unit. Standard pipeline. 5K docs × 30 pages × ~1500 tokens/page = 225M tokens/day to embed. ~$5/day = $150/mo. Storage: marginal.

LLM read with multimodal context dominates if not optimized. Vision tokens count toward LLM input - passing images costs $0.02-0.10 per query. At 50K queries/day, this is the highest single line item.

What "good" looks like:
  • Small multimodal app: $1-5K/mo (single-modality dominant)
  • Mid multimodal product: $10-30K/mo (Esme's range)
  • Consumer multimodal: $50-300K/mo, optimization mandatory
  • Convert-to-text architecture: Cheaper, simpler, may lose visual context

Multimodal-capable LLM tiers

Verified 20 hours ago
  1. 1
    GPT-5 Mini
    $0.250 in · $2.00 out ·
  2. 2
    Command
    $1.00 in · $2.00 out ·
  3. 3
    devstral-2
    $0.400 in · $2.00 out ·

Three real scenarios

Same calculator, three different team sizes. Click a tab to see how the numbers shift.

$5,289 / month ≈ $63,463 / year

Image-heavy small app. 10K images/day + small text corpus. Native multimodal LLM. Modest scale. ~$800/mo.

Healthy range: $500-1.2K/mo

See inputs used
videoMinutesPerDay
0
imagesPerDay
10,000
documentsPerDay
200
avgPagesPerDoc
10
queriesPerDay
5,000
architecture
native-multimodal
llmTier
balanced

Trade-offs

Cost isn't the only dimension. Click any constraint — see how recommendations change.

What matters most to you? Click any dimension — recommendations update.

Best fit for "cost":

  1. Convert-to-text architecture Cheapest, simpler ops
  2. Native multimodal embeddings Better cross-modal recall
  3. Hybrid (best of both) Most complex, often optimal

Convert-to-text is the cost-effective default. Move to native multimodal only if eval shows quality benefit on your workload.

Use cases

Pre-loaded scenarios for the most common applications. Click a tab to see realistic numbers — then the "Try this scenario" button to load it into the calculator above.

What this calculator can't tell you

Honest limitations — every model is wrong; some are useful. Where this one falls short:

For these, use: Vision Cost for image detail. Audio Cost for audio detail. RAG Pipeline for text.

Where to go next

Drill into vision-only →

Per-image cost detail.

 Drill into audio-only →

STT + voice + TTS detail.

 Text RAG component →

Standard RAG architecture.

Methodology

Source
/ai-cost-economics
Extraction
Multimodal stack costs from 5 production deployments (anonymized).
Editorial gate
8-layer defense — see aicost.ai/ai-cost-economics
Last verified
6/4/2026, 8:00:00 PM

Author: Subu Vdaygiri, Founder & CEO of CloudIntelligence.ai. 17 years Fortune 100 (Ingram Micro, Siemens). Wharton CTO program · Kellogg CPO program · 10× AWS+Azure certified.

3 years of pricing history

Why this matters: pricing for major vendors has dropped 40-90% in the last 24 months. A budget set 12 months ago is probably wrong by 30%+.

View 3-year history for →
📖 Data sources & methodology 161 text models · 9 embeddings · 24 vision · 41 audio · 8 vector DBs across 10 vendor pages · last verified 2026-06-05

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-06-05.
  • Vendor-published values have no mark. Inferred/extrapolated values are marked with * and listed below.
  • Batch API discounts are 50% off standard rates across providers that offer Batch mode.
  • Prompt caching discounts vary by provider (typically 80-90% off cached input tokens).
  • Regional data-residency surcharges (Anthropic 1.1x, OpenAI 1.1x, Google regional tiers) are NOT included in base rates.
  • Long-context pricing tiers apply when input exceeds model threshold.
  • Embedding prices are input-only (no output tokens generated).

Primary sources

Last-verified date is the most recent successful daily snapshot (aicost_pricing_snapshots) or, when no snapshot exists yet, the latest successful crawler run (aicost_crawler_runs). 10 of 10 vendors are currently verified. Aggregator services (TokenCost, AI Pricing Guru, etc.) are not listed.

Anthropic
2026-06-05
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 578 days captured
Anthropic Docs
2026-06-05
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 578 days captured
OpenAI
2026-06-05
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 579 days captured
Google AI
2026-06-05
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 554 days captured
Google Vertex
2026-06-05
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 554 days captured
DeepSeek
2026-06-05
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 493 days captured
xAI
2026-06-05
https://x.ai/api
Daily snapshot since Nov 2024 · 411 days captured
Mistral
2026-06-05
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 552 days captured
Cohere
2026-06-05
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 578 days captured
Voyage AI
2026-06-05
https://docs.voyageai.com/docs/pricing

Inferred values (marked with * in calculator tables)

Derived from industry conventions, not directly published by the vendor. Typical conventions: cached input = 10% of base (90% off), Batch API = 50% of base (50% off).

Vendor / Model Field Why it’s inferred
Anthropic — Claude Sonnet 4.6 cachedInput Derived at 10% of input rate — Anthropic publishes 90% cache-hit discount on this tier.
Anthropic — Claude Sonnet 4.5 cachedInput Derived at 10% of input rate; same 90% cache-hit convention as Sonnet 4.6.
Anthropic — Claude Sonnet 4.5 batchInput Derived at 50% of standard input — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Sonnet 4.5 batchOutput Derived at 50% of standard output — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Haiku 4.5 cachedInput Derived at 10% of input rate — Anthropic 90% cache-hit discount convention.
OpenAI — GPT-5.4 Mini cachedInput Derived at 10% of input — OpenAI documents automatic 90% discount on cache hits across GPT-5.x tier.
OpenAI — GPT-5.4 Nano cachedInput Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Nano batchInput Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Nano batchOutput Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro cachedInput Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Pro batchInput Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro batchOutput Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.2 cachedInput Derived at 10% of input; no residency uplift.
OpenAI — GPT-5.2 batchInput Derived at 50% of input.
OpenAI — GPT-5.2 batchOutput Derived at 50% of output.
OpenAI — GPT-5 cachedInput Derived at 10% of input.
OpenAI — GPT-5 batchInput Derived at 50% of input.
OpenAI — GPT-5 batchOutput Derived at 50% of output.
OpenAI — GPT-5.5 Pro cachedInput Derived at 10% of input — OpenAI does not publish a cached rate for *-pro models; using the family convention.
OpenAI — GPT-5.5 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5.5 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5.2 Pro cachedInput Derived at 10% of input — pro-tier convention.
OpenAI — GPT-5.2 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5.2 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5.1 batchInput Derived at 50% of input.
OpenAI — GPT-5.1 batchOutput Derived at 50% of output.
OpenAI — GPT-5 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5 Nano cachedInput Derived at 10% of input.
OpenAI — GPT-5 Nano batchInput Derived at 50% of input.
OpenAI — GPT-5 Nano batchOutput Derived at 50% of output.
Google — Gemini 3 Flash cachedInput Derived at 10% of input — Google caching discount convention ~90%.
Google — Gemini 3.1 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 3.1 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 3.1 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Pro cachedInput Derived at 10% of input.
Google — Gemini 2.5 Flash cachedInput Derived at 10% of input.
Google — Gemini 2.5 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 2.5 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash cachedInput Derived at 25% of input per Google 2.0 family caching rates.
Google — Gemini 2.0 Flash batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 2.0 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
xAI — Grok 4 (legacy) cachedInput Extrapolated at 25% of base.

Pricing is cross-verified against the LiteLLM community registry when available. Daily snapshots are kept in aicost_pricing_snapshots; every change is logged to aicost_price_changelog with old & new values for full audit trail. Read the full methodology →