Compare AI backends
Pixeltable is the only backend where AI transformations are part of the schema, not bolted on top.
Supabase and Convex are excellent app backends. Modal is excellent GPU compute. Pixeltable is the multimodal AI data layer they make you assemble by hand. Read the Convex developer's guide for a query / mutation / action mapping.
Summary
| Feature | PixeltableAI transformations in the schema | SupabasePostgres + auth + storage | ConvexReactive TypeScript backend |
|---|---|---|---|
| Lines of code | ~50 one import | 550–800 SQL + functions | 400–550 TypeScript |
| External services | 1 just Pixeltable | 3–5 DB + functions + compute | 2–3 backend + compute |
| Languages | Python | SQL · TS · Py | TS · Py |
| Time to a pipeline | Minutes | Hours–days | Hours |
| Offline development | Yes embedded Postgres | Docker 10+ containers | No cloud only |
| Multimodal-native | Yes image · video · audio · doc | No URLs only | No storage IDs |
What you get out of the box
Built in, partial, not supported, or not applicable: side by side.
built in·partial / manual· not supported·— n/a
| Feature | Pixeltable | Supabase | Convex |
|---|---|---|---|
| Setup & developer experience | |||
| Install | pip install (~1 min) | signup + project + keys (~5 min) | npm create + GitHub auth (~3 min) |
| Cloud account required | |||
| Offline development | embedded Postgres | Docker (10+ containers) | cloud only |
| Languages in production | 1 (Python) | 2–3 (SQL + TS + Python) | 1–2 (TS + Python) |
| Multimodal data types | |||
| Native image columns | pxt.Image | URL to Storage | storage ID |
| Native video columns | pxt.Video, frames extractable | URL + external ffmpeg | |
| Native audio columns | pxt.Audio | URL | |
| Native document columns | PDF/DOCX, splittable | URL + external chunker | |
| N-dimensional arrays (NumPy-backed) | |||
| Computed columns & pipelines | |||
| Auto-execute on insert | 1 line | webhooks + Edge Functions | convex-helpers triggers |
| Incremental recomputation (changed rows only) | full re-scan | ||
| Dependency cascade (downstream auto-recompute) | wire trigger chains | chain triggers manually | |
| Per-cell error tracking | col.errormsg | ||
| Built-in rate limiting for API calls | resource_pool | ratelimiter component | |
| Embedding indexes & search | |||
| One-line embedding index | add_embedding_index | SQL index + manual embed | schema + manual embed |
| Auto index maintenance on data change | manual re-embed | index auto, embed manual | |
| Multi-modal search (text, image, audio, video) | text only (pgvector) | text only | |
| View lineage in results (chunk → parent) | zero JOINs | manual JOINs | separate queries |
| Vector search result limit | unlimited | unlimited | 256 max |
| AI integrations & agents | |||
| Built-in AI providers | 25+ (OpenAI, Anthropic, CLIP, Whisper…) | manual SDK wrappers | manual SDK wrappers |
| LLM tool calling / agents | pxt.tool(), auto JSON schema | ~100 LOC | ~80 LOC |
| Built-in media processing (ffmpeg, Whisper, CLIP) | 1 line each | external compute | cannot run natively |
| Views, serving & history | |||
| Document chunking / video frames (1 line) | iterators | ~60–100 LOC + libs | |
| Zero-code API serving | pxt service update | write Edge Functions | write httpRouter |
| Configurable storage destination (S3/GCS/R2/B2/Azure) | per table/column, BYOB | single provider | proprietary |
| Version history / snapshots | built-in time travel | manual (pg_dump) | |
| Managed deploy to production | self-host today, managed coming | managed (strong) | managed (strong) |
| Auth / RLS & real-time subscriptions | BYO | built-in (strong) | built-in (strong) |
Real code, not marketing diagrams
The same task, written in each platform's idiomatic style.
Ingest video, extract frames + audio, transcribe, embed visually and semantically. On insert, automatically.
import pixeltable as pxtfrom pixeltable.functions.video import frame_iterator, extract_audiofrom pixeltable.functions.audio import audio_splitterfrom pixeltable.functions.huggingface import clipfrom pixeltable.functions.whisper import transcribefrom pixeltable.functions.openai import embeddingsvideos = pxt.create_table('media.videos', {'video': pxt.Video, 'title': pxt.String})frames = pxt.create_view('media.frames', videos,iterator=frame_iterator(videos.video, fps=1))frames.add_embedding_index('frame',embedding=clip.using(model_id='openai/clip-vit-base-patch32'))videos.add_computed_column(audio=extract_audio(videos.video, format='mp3'))chunks = pxt.create_view('media.audio_chunks', videos,iterator=audio_splitter(videos.audio, duration=30.0))chunks.add_computed_column(transcript=transcribe(chunks.audio_segment, model='base.en'))chunks.add_embedding_index('transcript',embedding=embeddings.using(model='text-embedding-3-small'))videos.add_computed_column(scenes=videos.video.scene_detect_content(fps=2.0))# Insert a video -> frames, audio, transcripts, embeddings, scenes: all automatic.
# Same pipeline on Supabase:- 4 SQL tables with foreign keys and vector columns- 5-6 Deno Edge Functions (ingest, frames, audio, search x2, agent)- External compute service for ffmpeg, Whisper, CLIP(Deno Edge Functions cannot run them)- Webhook triggers to chain the pipeline- ~500+ lines across TypeScript and SQL
Deno Edge Functions cannot run ffmpeg, Whisper, or CLIP.
When to use what
These tools solve different problems. Many teams run an app backend and Pixeltable together.
- CRUD app with auth + real-timeSupabase or Convex
- React app with live subscriptionsConvex
- Multimodal AI pipeline (images, video, audio, docs)Pixeltable
- RAG with automatic index maintenancePixeltable
- AI agent with persistent memory + tool callingPixeltable
The glue you stop maintaining
Stacks Pixeltable replaces with one import.
pgvector + Edge Functions + cron
You started with Supabase for the DX. Then you added pgvector, wrote a webhook, built an Edge Function, set up pg_cron for backfill. Now you maintain 5 Postgres extensions to do what Pixeltable does in 1 line.
LangChain + Pinecone + S3 + Airflow
The 4-service RAG stack. Four bills, four dashboards, four failure modes. Pixeltable replaces all four with one import.
for row in data: call_openai(row)
The imperative loop. No rate limiting, no error tracking, no incremental recomputation. Pixeltable's computed columns handle all three declaratively.
pandas DataFrame as your AI backend
It worked in the notebook. It breaks in production. Pixeltable gives you the same ergonomics with persistence, versioning, and serving built in.
A separate vector DB that drifts from your source of truth
Your embeddings are in Pinecone but your data is in Postgres. They're already out of sync. Pixeltable keeps embeddings and data in the same table, updated automatically.
Frequently asked questions
One import. The whole AI data layer.
Stop stitching together a vector DB, an orchestrator, and a chunking framework. Declare it as a table.