--- title: "Pixeltable vs Supabase: measured video-intelligence comparison" description: "Same app, two implementations. Pixeltable is 129 lines in one file; Supabase is 546 with a compute service. Supabase wins ingest, RLS, and realtime. Pixeltable wins in-platform media and incremental schema changes." keywords: - Pixeltable vs Supabase - Supabase alternative - Supabase AI - pgvector alternative - Supabase Edge Functions AI - Python AI backend - multimodal AI backend url: "https://pixeltable.com/compare/pixeltable-vs-supabase" --- # Pixeltable vs Supabase Same video-intelligence app, two implementations. Pick Supabase when you want Postgres, row-level security, realtime, and a managed database. Pick Pixeltable when the pipeline is the product: video, frames, transcripts, embeddings, and retrieval in one Python file. They are not mutually exclusive. ## Summary ### Pixeltable - 129 lines, one file, one process: ffmpeg, Whisper, and CLIP run in the schema - A row inserted by anything at all gets processed - Adding a title-embedding index is one line; the table backfills in place - Per-cell errors, lineage, and revert live in the catalog ### Supabase - 294 app lines plus 252 in compute-service, because Deno cannot run ffmpeg - RLS enabled on every table; one config line authenticates the Edge Function - Realtime, PITR, branching, and a managed database your team already operates - Fastest ingest (11.64× realtime) and fastest evolve wall-clock on this corpus ## Comparison | Feature | Pixeltable | Supabase | | --- | --- | --- | | Total code for this app | 129 lines, 1 file | 546 lines (294 + 252 compute-service), 7 files | | ffmpeg, Whisper, CLIP | Computed columns, same process | compute-service; three of seven endpoints have no hosted-API substitute | | Processing for any writer | Yes — the pipeline is the schema | No, unless you add database triggers | | Add a derived column (lines) | 1 line, 1 file, one command | 24 lines, 2 files: ALTER TABLE plus a backfill script | | Add a derived column (wall time) | 3.88s (schema change and backfill are the same step) | 1.6s — fastest of the three at two dozen rows | | Ingest, 20 videos / 10 min | 66.7s, 9.04× realtime | 51.8s, 11.64× realtime | | Frame search p50 | 39.0ms | 26.1ms | | Authenticated endpoints | Open in this repo | One line: withSupabase({ auth: 'secret' }) | | Row-level security | None here | Enabled and verified on all five tables | | Realtime push | None here | Built in | | Per-cell errors and lineage | errormsg / errortype; pxt dashboard draws what produced a column | A failed step leaves NULL; Studio does not record lineage | | Vendor checker in CI | ruff — generic Python; no Pixeltable conformance checker | deno lint and supabase db advisors on a live database | ## Ingest a video Insert a video. Frames, audio, transcripts, embeddings, and scenes have to exist after that. On Pixeltable they are the schema. On the other two they live in the ingest path and in a second service. ### Pixeltable ```python class Videos(TableModel, name='videos'): video: pxt.Video title: pxt.String audio = extract_audio(video, format='mp3') duration_sec = pxtf.video.get_duration(video) scenes = video.scene_detect_content(threshold=8.0) class Frames(TableModel, name='frames', base=Videos, iterator=frame_iterator(Videos.video, fps=1.0)): still = pxtf.image.resize(frame, (320, 180)) __indexes__ = [pxt.EmbeddingIndex(frame, embedding=VISUAL)] class Chunks(TableModel, name='chunks', base=Videos, iterator=audio_splitter(Videos.audio, duration=10.0)): transcript = transcribe(audio_segment, model='base.en').text.astype(pxt.String) __indexes__ = [pxt.EmbeddingIndex(transcript, embedding=SEMANTIC)] Videos.insert([{'video': 'lecture.mp4', 'title': 'CS101'}]) ``` ### Supabase ```typescript const { frames } = await compute("/extract-frames", { video_url, fps: FRAME_FPS }); const { embeddings } = await compute("/embed-clip", { images_b64: frames }); const frameRows = await Promise.all(frames.map(async (b64, i) => { const path = `videos/${videoId}/frame_${i}.jpg`; await supabase.storage.from("frames").upload(path, decodeBase64(b64), { contentType: "image/jpeg", upsert: true, }); return { video_id: videoId, frame_idx: i, embedding: embeddings[i] }; })); await supabase.from("frames").insert(frameRows); ``` ## Search frames Find frames of a whiteboard. Pixeltable asks the index. The other two embed the query themselves, then join or fetch rows in a second step. ### Pixeltable ```python sim = Frames.frame.similarity(string=query) return ( Frames.order_by(sim, asc=False) .limit(limit) .select( frame_url=Frames.still, frame_idx=Frames.pos, video_title=Frames.title, similarity=sim, ) ) ``` ### Supabase ```sql CREATE FUNCTION search_frames(query_embedding vector(512), match_count INT) RETURNS TABLE(frame_url TEXT, frame_idx INT, video_title TEXT, similarity FLOAT) AS $$ SELECT f.frame_url, f.frame_idx, v.title, 1 - (f.embedding OPERATOR(public.<=>) query_embedding) FROM public.frames f JOIN public.videos v ON f.video_id = v.id ORDER BY f.embedding OPERATOR(public.<=>) query_embedding LIMIT match_count; $$ LANGUAGE sql STABLE; ``` ## Add a column to live data Make the video title semantically searchable. The rows already exist. An embedding is not derivable in SQL, so every existing row has to be read, sent to a model, and written back. ### Pixeltable ```python class Videos(TableModel, name='videos'): ... __indexes__ = [pxt.EmbeddingIndex(title, embedding=SEMANTIC)] # pxt schema update app.py media # updated media/videos # unchanged media/frames, media/chunks, media/conversations ``` ### Supabase ```typescript ALTER TABLE videos ADD COLUMN IF NOT EXISTS title_embedding vector(384); CREATE INDEX IF NOT EXISTS videos_title_embedding_idx ON videos USING hnsw (title_embedding vector_cosine_ops); // then a script, because Postgres cannot call a model: const { data: rows } = await db.from("videos") .select("id,title").is("title_embedding", null); for (let i = 0; i < rows.length; i += BATCH) { /* embed the batch, update each row */ } ``` ## When to choose Pixeltable - **The pipeline is the product**: Video, audio, images, documents, embeddings, and a retrieval step over them. The whole backend is one file, and nothing extra has to exist to run ffmpeg. - **Schema changes have to stay cheap**: Adding a column backfills only that column. A row inserted by anything at all gets processed. That compounds; a line-count difference does not. - **You already have an app backend**: Pixeltable as the media and retrieval layer behind a Supabase application is a coherent architecture, and for a team that already runs Postgres it is likely cheaper than moving. ## When to choose Supabase - **You want Postgres and the things around it**: Realtime subscriptions, row-level security for multi-tenancy, an auto-generated REST API, PITR, database branching, or a managed database your team already knows how to operate. Media work will live in a second service. That is the trade. - **Throughput at this scale is the binding constraint**: On 20 videos and 10 minutes of footage, Supabase ingests fastest and answers frame search fastest. If that is the constraint, follow it rather than the sponsor. ## FAQ ### Can I keep Supabase Auth and use Pixeltable for media? Yes. The benchmark’s conditional recommendation is pick more than one. Keep Auth, RLS, and OLTP where they are. Put bytes, frames, CLIP, and transcripts in a Pixeltable table. ### Why can’t Edge Functions run this pipeline? Deno has no subprocess, so no ffmpeg. Whisper and CLIP do not run there either. The managed path still forced a Python process we own. Three of compute-service’s seven endpoints have no hosted-API substitute. ### Is Pixeltable faster than pgvector? On this laptop frame search p50 is 39ms against Supabase’s 26ms — both interactive, and most of every number is embedding the query. At 603 vectors no index is working hard, and hosted Cloud was not in this run. The structural claim is that Pixeltable does not make you embed the query yourself or join back to the parent video.