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.
Summary
What you get out of the box
built in·partial / manual· not supported·— n/a
Setup & developer experience
Multimodal data types
Computed columns & pipelines
Embedding indexes & search
AI integrations & agents
Views, serving & history
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.
The glue you stop maintaining
Stacks Pixeltable replaces with one import.
pgvector + Edge Functions + cronYou 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 + AirflowThe 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 backendIt 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 truthYour 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.