Guides → Playground & Guide → Multimodal RAG Stack - Vision + Audio + Text Retrieval Cost
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.
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.
Here are the inputs that move the result the most. Play with the sliders and check it out. The number updates live.
Several media pipelines stacked — documents, images, video — plus the LLM answering. ~30 days assumed.
💡Each media stream adds cost: docs (pages), images (vision tokens), video (minutes). Answering queries is often the biggest single slice.
Same calculator, three team sizes. Click a tab to see how the numbers shift.
Image-heavy small app. 10K images/day + small text corpus. Native multimodal LLM. Modest scale. ~$800/mo.
Healthy range: $500-1.2K/mo
Mixed multimodal product. Convert-to-text architecture (cheaper). $20K/mo across all line items. Audio + LLM dominate.
Healthy range: $15-25K/mo total
Consumer-scale multimodal. Cheap-tier LLM mandatory. Multi-vendor routing for vision (Gemini Flash for simple, Claude for complex). Self-hosted vector DB.
Healthy range: $200-500K/mo, multi-vendor mandatory
Same calculator, different applications. Sizes above, workloads here. Pick the one that looks like yours.
Pre-loaded scenarios for the most common applications. Click a tab to see realistic numbers, then hit "Try this scenario" to load it into the calculator above.
Video tutorial Q&A. Transcribe + chunk + embed transcripts. Skip vision (visual content not needed for Q&A). ~$20K/mo.
Healthy range: $15-25K/mo for video-only
Papers with figures. Vision for figures + text for body. Premium LLM for reasoning. Modest query volume. ~$7K/mo.
Healthy range: $5-10K/mo for academic depth
Image-similarity product search. CLIP-style embeddings, no LLM read needed for retrieval. Cheap-tier LLM only for query disambiguation. ~$22K/mo.
Healthy range: $15-30K/mo with cheap multimodal
Medical images + clinical guidelines RAG. Premium tier + HIPAA + self-hosted vector DB. Modest scale. ~$5K/mo.
Healthy range: $3-6K/mo (premium tier mandatory)
Open the full calculator. Pick a model, enter your tokens, see per-call, daily, monthly, and annual cost.
🚀 Open the full calculator →Multimodal RAG combines image embeddings, audio transcription, and text retrieval. Real architecture math for production multimodal apps.
Each input shapes the cost. Click an input on the calculator to set it. The explanations below match the live calculator field by field.
| Input | Default | Typical ballparks |
|---|---|---|
queriesPerDay
|
50,000 | Pilot · ~100/day = 100 · Production · ~1K/day = 1,000 · High traffic · ~10K/day = 10,000 |
workingDaysPerMonth
|
30 | Business days · 22 = 22 · Every day · 30 = 30 |
videoMinutesPerDay
moves the needle
|
10,000 | — |
imagesPerDay
moves the needle
|
100,000 | — |
documentsPerDay
moves the needle
|
5,000 | — |
avgPagesPerDoc
|
30 | — |
llmTier
|
balanced | — |
Ballparks are broad industry starting points (sourced ranges; * = rough estimate) — your result gets more accurate as you replace them with measured numbers.
Try them live in the calculator;
API & agent users get the same data from the MCP resource aicost://input-reference/multimodal-rag-stack.
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.
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.
Cost isn't the only dimension. Click any constraint to see how recommendations change.
Convert-to-text is the cost-effective default. Move to native multimodal only if eval shows quality benefit on your workload.
Multimodal hallucinations are sneakier. User uploads a chart; agent misreads axis values; downstream advice is wrong. Eval each modality.
Compliance is per-modality. Vision-API BAA may differ from text-API BAA. Voice biometrics has its own regulations. Check each.
Audio + images are the highest-PII modalities. Strip metadata, get explicit consent, retention policy.
Multimodal queries are slower. Streaming UI helps. Pre-process modalities in parallel where possible.
Vision and audio APIs across vendors are less standardized than text. Multi-vendor multimodal is harder than multi-vendor text.
Multimodal MLOps is genuinely harder. Eval frameworks for vision-RAG and audio-RAG are less mature. Custom eval is often required.
Tradeoff analysis is where most AI projects go sideways. Talk to a CFO-grade AI cost analyst →
The gaps we just listed are real, and they are the expensive ones: your actual prompts, your switching cost, your MLOps overhead. An AICost expert spends the hour on your AI and cloud costs, not a generic playbook. You leave with a written report: the way forward, in 30 days of concrete steps.
Not sure yet? The $39 AICost Blueprint credits toward a Session, and the Session fee credits toward any plan. You never pay twice for the same ground. See all pricing →
You have seen the shape of it. Open the calculator with your model, your tokens, your volume.
🚀 Open the full calculator →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.
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 →
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.
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 →