---
title: "The Triforce of AI Infrastructure: Why Storage, Orchestration, and Retrieval Must Be One System"
date: "2026-03-04"
author: "Pierre Brunelle"
tags:
  - AI Infrastructure
  - Multimodal AI
  - Data Engineering
  - Architecture
  - Storage
  - Orchestration
  - Retrieval
description: "Every AI tool gives you one piece of the puzzle. Vector DBs handle retrieval. Orchestrators coordinate tasks. Object stores hold bytes. But multimodal AI needs all three unified. Here's why, and what happens when you get it."
url: "https://pixeltable.com/blog/triforce-storage-orchestration-retrieval"
---

# The Triforce of AI Infrastructure: Why Storage, Orchestration, and Retrieval Must Be One System

**Summary:** Building multimodal AI applications requires three capabilities: storing diverse data types, orchestrating transformations and model calls, and retrieving results efficiently. Today, each capability lives in a separate system: object stores for storage, DAG runners for orchestration, vector databases for retrieval. The result is a fragmented stack held together by glue code. This post argues that these three pillars form an indivisible unit (a Triforce), and that unifying them under a single abstraction is the architectural breakthrough that makes multimodal AI tractable.

 
## The Three Pillars Every AI Application Needs

 
Strip away the buzzwords and every AI application (whether it's a [RAG pipeline](/blog/production-rag-data-centric), a [video analysis system](/blog/video-keyframe-extraction-pixeltable), or an [autonomous agent](/blog/practical-guide-building-agents)) needs exactly three things from its infrastructure:

 

 - **Storage**: a place to put raw data (videos, images, audio, documents) alongside structured metadata, computed results, and model outputs. Not just bytes in a bucket, but typed, versioned, queryable data.

 - **Orchestration**: a way to define what should happen to that data: extract frames, transcribe audio, generate embeddings, call an LLM. And a way to ensure it happens reliably, incrementally, and in the right order.

 - **Retrieval**: a way to get results back out: similarity search over embeddings, filtered queries over metadata, joins across modalities. Fast, consistent, and always in sync with the latest data.

 

 
These aren't independent concerns. They're deeply coupled. The embeddings you retrieve depend on the orchestration that produced them, which depends on the data in storage. Change any one piece and the other two must respond. **They form a Triforce**: three faces of a single system, not three systems bolted together.

 
## How Teams Build Today: One Tool Per Pillar

 
The industry's default approach is to pick the "best" tool for each pillar and integrate them yourself:

 
| Pillar | Typical Tools | What They Do Well | What They Don't Do |
| --- | --- | --- | --- |
| Storage | S3, GCS, Postgres | Store bytes and rows reliably | No native multimodal types, no computed transforms, no versioning across modalities |
| Orchestration | Airflow, Prefect, Temporal | Schedule and coordinate tasks | Don't own the data, can't do incremental updates, no built-in caching or lineage |
| Retrieval | Pinecone, Weaviate, Qdrant | Fast similarity search | Only store embeddings: know nothing about raw data, pipeline, or lineage |

 
Each tool is excellent in isolation. The problem is the space between them. We've written about this as the [Frankenstein Stack](/blog/deconstructing-ai-frankenstein-stack): a Frankensteined architecture where 40-60% of the codebase is glue code to move data, handle retries, manage state, and keep indexes in sync.

 
## Why Separating the Three Pillars Fails

 
The Triforce metaphor isn't just aesthetic. There are deep technical reasons why these three pillars resist separation:

 
### 1. The Synchronization Problem

 
When storage, orchestration, and retrieval live in different systems, you need to keep them in sync manually. Consider a simple operation: delete a video from your dataset.

 

 - Delete the file from S3.

 - Delete metadata rows from Postgres.

 - Find and delete all derived embeddings in Pinecone.

 - Invalidate cached inference results.

 - Update any downstream views or indexes.

 

 
If step 3 fails, you have [zombie vectors](/blog/deconstructing-ai-frankenstein-stack): embeddings that point to data that no longer exists. Your search returns broken results. Your RAG app hallucinates context from deleted documents. In a unified system, this is one atomic operation.

 
### 2. The Incrementality Problem

 
Multimodal AI is inherently iterative. You change a prompt, swap an embedding model, adjust a chunking strategy, add new data. Each change should propagate efficiently, [recomputing only what's affected](/blog/economics-of-incremental-ai), not re-running the entire pipeline.

 
But incrementality requires the system to understand the dependency graph: which embeddings depend on which frames, which frames came from which video, which transcriptions used which model version. When these relationships span three separate systems, incremental updates are essentially impossible. Teams fall back to expensive full reruns.

 
### 3. The Lineage Problem

 
In regulated industries ([healthcare, security, finance](/blog/audit-ready-ai-compliance-lineage)) you need to trace any output back to its source. Which model version produced this embedding? Which video frame was this detection derived from? Which prompt generated this summary?

 
When data flows through S3 → custom script → Pinecone → LangChain, lineage is a manual reconstruction exercise. When storage, orchestration, and retrieval are one system, lineage is automatic. It's just the dependency graph.

 
### 4. The Experimentation Problem

 
The cost of separated pillars isn't just operational. It kills the feedback loop. Building multimodal AI is an experimental process. Which chunking strategy works best? Which embedding model? What retrieval threshold? You can only make good decisions with fast iteration and comparable results.

 
When your data lives across disconnected services, [nothing is captured consistently](/blog/multimodal-ai-data-bottleneck). There's no versioning across the pipeline. No way to compare runs. Teams spend more time fighting infrastructure than actually experimenting, which is where all the value is.

 
## What the Unified Triforce Looks Like

 
If you designed a system from scratch to unify all three pillars, what would it look like? We think it looks like this:

 
### Storage: Multimodal Types as First-Class Citizens

 
Video, audio, images, and documents can't be opaque blobs. They need to be [first-class column types](https://docs.pixeltable.com/platform/type-system) with native operations. Your files stay where they are (local disk, S3, URLs). The system references them without copying. All computed outputs are stored and versioned automatically.

 
```python
import pixeltable as pxt

# Native multimodal types: not file paths, not BLOBs
media = pxt.create_table('content', {
 'video': pxt.Video,
 'title': pxt.String,
 'metadata': pxt.Json
})

# Insert from anywhere: local, S3, URLs
# Files referenced in place, zero duplication
media.insert([
 {'video': 's3://my-bucket/demo.mp4', 'title': 'Product Demo'},
 {'video': '/local/path/tutorial.mp4', 'title': 'Tutorial'}
])
```

 
### Orchestration: Declarative Computed Columns

 
Instead of writing imperative scripts and DAGs, you declare what you want computed. [Computed columns](https://docs.pixeltable.com/tutorials/computed-columns) trigger automatically when data arrives, with [incremental updates](/blog/economics-of-incremental-ai) when anything upstream changes. Rate limiting, retries, caching, parallelization: all built in.

 
```python
from pixeltable.functions import openai, yolox
from pixeltable.functions.video import frame_iterator

# Extract frames: declarative, not imperative
frames = pxt.create_view('frames', media,
 iterator=frame_iterator(video=media.video, fps=1))

# Object detection: runs automatically on every frame
frames.add_computed_column(
 objects=yolox(frames.frame, model_id='yolox_s')
)

# Vision analysis: API calls parallelized, rate-limited, cached
frames.add_computed_column(
 description=openai.chat_completions(
 messages=[{
 'role': 'user',
 'content': [
 {'type': 'text', 'text': "Describe what's happening in this frame"},
 {'type': 'image_url', 'image_url': {'url': frames.frame}},
 ],
 }],
 model='gpt-4o-mini',
 ).choices[0].message.content
)

# Transcription: audio extracted and transcribed automatically
media.add_computed_column(
 transcript=openai.transcriptions(
 audio=pxt.functions.video.extract_audio(media.video),
 model='whisper-1'
 )
)
```

 
### Retrieval: Built-In Embedding Indexes and Search

 
Vector search isn't a separate service. It's a column operation. [Embedding indexes](https://docs.pixeltable.com/platform/embedding-indexes) stay in sync with the data automatically. No manual index maintenance. No sync jobs. No stale vectors.

 
```python
from pixeltable.functions.huggingface import clip, sentence_transformer

# Image similarity search: one line
frames.add_embedding_index(
 'frame',
 embedding=clip.using(model_id='openai/clip-vit-base-patch32')
)

# Text similarity search on descriptions
frames.add_embedding_index(
 'description',
 string_embed=sentence_transformer.using(
 model_id='all-MiniLM-L12-v2'
 )
)

# Query across modalities: structured + unstructured together
results = frames.order_by(
 frames.frame.similarity(string="person holding a laptop"), asc=False
).select(
 frames.frame,
 frames.objects,
 frames.description
).limit(10).collect()
```

 
## What Changes When the Triforce Is Unified

 
When all three pillars live in one system, things that were impossible become trivial:

 
| Operation | Separated Stack | Unified Triforce |
| --- | --- | --- |
| Delete a video and all derivatives | 5-step distributed operation, risk of zombie vectors | One atomic delete, cascading cleanup |
| Swap the embedding model | Re-run entire pipeline, rebuild all indexes | One line change, incremental recomputation |
| Compare two chunking strategies | Build separate pipelines, manual diffing | Version the column, query both versions |
| Trace an output to its source | Manual reconstruction across 3+ systems | Automatic: follow the dependency graph |
| Add new data | Trigger DAG, wait for each step, hope nothing fails | table.insert(): everything propagates |
| Roll back a bad model update | Restore backups across S3 + Postgres + Pinecone | table.revert() |

 
## The Full Picture: 47 Lines → 12 Lines

 
Here's what this looks like in practice. A video processing pipeline with storage, orchestration, and retrieval, the traditional approach vs. the unified approach:

 
**Traditional approach**, with separate systems for each pillar:

 
```python
# traditional_pipeline.py: 47+ lines, 5 systems
import boto3, pinecone, psycopg2
from airflow import DAG

# 1. Download from S3
s3 = boto3.client('s3')
s3.download_file(bucket, key, local_path)

# 2. Extract frames manually
frames = extract_frames(local_path, fps=1)

# 3. Run model, handle errors
for frame in frames:
 try:
 embedding = model.encode(frame)
 objects = yolox.detect(frame)
 except Exception as e:
 log_error(e)
 continue

 # 4. Store in Pinecone
 pinecone_index.upsert([(id, embedding)])

 # 5. Store metadata in Postgres
 cursor.execute("INSERT INTO frames ...", (id, objects))

# Systems: S3, Pinecone, Postgres, Airflow, Custom ETL
# Not shown: retry logic, caching, DAG config, cleanup scripts
```

 
**Unified approach**, one system for all three pillars:

 
```python
# storage_orchestration_retrieval.py: 1 system
import pixeltable as pxt
from pixeltable.functions import yolox
from pixeltable.functions.huggingface import clip
from pixeltable.functions.video import frame_iterator

videos = pxt.create_table('content', {'video': pxt.Video})

frames = pxt.create_view('frames', videos,
 iterator=frame_iterator(video=videos.video, fps=1))

frames.add_computed_column(objects=yolox(frames.frame))
frames.add_embedding_index(
 'frame', embedding=clip.using(model_id='openai/clip-vit-base-patch32'))

# Insert triggers the full pipeline: storage, orchestration, retrieval
videos.insert([{'video': 'new.mp4'}])
```

 
A fraction of the code. But the real difference isn't the line count. It's that the unified version is **auto-orchestrated**, **incremental**, **versioned**, and **queryable**. The traditional version is none of those things.

 
## Why Existing Tools Can't Just Add the Missing Pieces

 
The natural question: why can't Pinecone add storage? Why can't Airflow add retrieval? Why can't Postgres add orchestration?

 
Because each tool was [architecturally designed for one pillar](/blog/who-owns-the-multimodal-data-plane):

 

 - **Vector databases** started as single-purpose indexes. Adding storage, orchestration, and lineage means [rebuilding as a general-purpose system](/blog/teams-switching-pixeltable-vector-databases), something they weren't designed for.

 - **Orchestrators** coordinate existing systems. They don't own the data. Airflow knows the order of your tasks, but it doesn't know that changing your embedding model means recomputing your search index.

 - **Databases** handle structured metadata brilliantly. But Postgres can't natively process a video, [extract and transcribe audio](/blog/whisper-transcription-pixeltable), [run object detection on frames](/blog/object-detection-videos-yolox), or manage embedding indexes.

 - **LLM frameworks** like [LangChain](/blog/pixeltable-vs-langchain-rag-comparison) are stateless by design. They coordinate calls but don't persist data, don't version anything, and don't handle incremental updates.

 

 
Bolting capabilities onto a system designed for a different purpose creates the worst of both worlds. The Triforce must be designed as a unit from the ground up.

 
## How Pixeltable Implements the Triforce

 
Pixeltable is open-source Python infrastructure for incremental storage, transformation, indexing, and orchestration of multimodal data. It was designed from the start to unify all three pillars under [a multimodal data table](/blog/what-is-a-multimodal-data-table):

 
| Pillar | How Pixeltable Implements It |
| --- | --- |
| Storage | Native multimodal types (Video, Image, Audio, Document) with automatic versioning, four-layer storage architecture, and zero-duplication file references |
| Orchestration | Computed columns with automatic dependency tracking, incremental recomputation, rate limiting, caching, retries, and parallelization across 50+ AI providers |
| Retrieval | Built-in embedding indexes with automatic sync, similarity search, and structured queries: all through the same table interface |

 
Because all three live in one system, you get capabilities that are impossible with separated tools:

 

 - **[Time travel](https://docs.pixeltable.com/platform/version-control)**: query any prior version of your data, schema, and computed results with `table.history()` and `pxt.get_table('my_table:472')`

 - **[Automatic dependency tracking](/blog/dependency-graph-magic)**: the system knows your embedding depends on your frame, which depends on your video. Change anything and only the affected downstream results recompute.

 - **[Prototype-to-production continuity](https://docs.pixeltable.com/howto/cookbooks/core/dev-iterative-workflow)**: test a UDF on a sample with `t.sample(n=5).select(...)`, then commit it as a computed column. Same code, same system, no handoff.

 - **[Native audit trails](/blog/audit-ready-ai-compliance-lineage)**: every row, every computed result, every model call is automatically versioned with full provenance.

 

 
## Video: The Ultimate Test of the Triforce

 
If you want to understand why the Triforce matters, look at video. [A single video generates every other data type](/blog/multimodal-ai-data-bottleneck):

 

 - **Audio**: [extract the audio track](https://docs.pixeltable.com/howto/cookbooks/audio/audio-extract-from-video), chunk it, [transcribe with Whisper](/blog/whisper-transcription-pixeltable). Now you have text.

 - **Images**: [extract keyframes](/blog/video-keyframe-extraction-pixeltable), generating hundreds of images per video.

 - **Metadata**: run [object detection](/blog/object-detection-videos-yolox), scene classification, face recognition on those frames.

 - **Embeddings**: embed frames, audio chunks, and transcript segments into vector space for [similarity search](/blog/video-similarity-search).

 - **LLM outputs**: feed all of it into an LLM for summarization, analysis, or [agentic workflows](/blog/practical-guide-building-agents).

 

 
One input, five derived data types, each requiring different processing (orchestration), different storage, and different retrieval patterns. And you need lineage between all of them.

 
Teams building video pipelines today (in media, security, healthcare) are stitching together 5+ services to handle this. With a unified Triforce, it's one table, a few computed columns, and an embedding index. [Insert a video and everything else happens automatically](/blog/automated-video-translation-voiceover-pipeline).

 
## The Experimentation Loop: Where the Triforce Pays Off Most

 
The biggest payoff of unification isn't operational. It's the feedback loop it enables.

 
Building multimodal AI is an experimental process. Which embedding model? What retrieval threshold? What prompt? These decisions compound, and you can only make good ones with fast iteration and comparable results. In a unified system, swapping a model or testing a new function is a one-line change, and the system handles incremental recomputation automatically:

 
```python
# Swap the embedding model? Drop the old index, add a new one.
chunks.drop_embedding_index('text')
chunks.add_embedding_index(
 'text',
 string_embed=sentence_transformer.using(model_id='all-MiniLM-L12-v2')
)

# Compare results? A query.
chunks.select(chunks.text, chunks.score).where(chunks.score > 0.8).collect()

# Test a UDF on a sample: nothing stored, calls parallelized and cached
chunks.sample(n=5).select(chunks.text, summary=summarize(chunks.text)).collect()

# Happy? One line to commit: runs on full dataset, skips cached rows
chunks.add_computed_column(summary=summarize(chunks.text))
```

 
Every decision is [versioned](https://docs.pixeltable.com/platform/version-control). Every result is queryable. Every change propagates incrementally. This is only possible when storage, orchestration, and retrieval are one system, when the Triforce is complete.

 
## Claim the Triforce

 
The history of data infrastructure is clear: the systems that win are the ones that provide [operational integrity under change](/blog/who-owns-the-multimodal-data-plane) across the full lifecycle. Snowflake didn't just store data. It handled access control, versioning, compute scaling, and governance. Databricks didn't just run Spark. It managed notebooks, lineage, model registries, and feature stores.

 
Multimodal AI needs the same. Storage, orchestration, and retrieval aren't three separate problems. They're one problem with three faces. Solve them together and you get a system where everything just works: atomic updates, incremental recomputation, automatic lineage, and a single interface from prototype to production.

 
Solve them separately and you get the Frankenstein Stack: 5+ services, thousands of lines of glue code, and an engineering team that spends more time on infrastructure than on AI.

 
The Triforce is waiting. `pip install pixeltable`.

 
*Ready to see what unified infrastructure looks like? Start with the [10-Minute Tour](https://docs.pixeltable.com/overview/ten-minute-tour), explore the [video processing cookbooks](https://docs.pixeltable.com/howto/cookbooks/video/video-extract-frames), or dive into the [RAG pipeline guide](https://docs.pixeltable.com/howto/cookbooks/agents/pattern-rag-pipeline). Everything is open source under [Apache 2.0](https://github.com/pixeltable/pixeltable).*