--- title: "Supabase Alternative: Pixeltable vs Supabase" description: "Supabase alternative for AI apps with images, video, and embeddings: data, media, model calls, retrieval, and the API in one Python backend." keywords: - Pixeltable vs Supabase - Supabase alternative - Supabase AI - pgvector alternative - Supabase Edge Functions AI - Python AI backend - multimodal AI backend url: "https://www.pixeltable.com/compare/pixeltable-vs-supabase" --- # Supabase Alternative: Pixeltable vs Supabase Updated: 2026-10-20 Pixeltable is a Supabase alternative for AI apps that work with images, video, and embeddings: media, model calls, retrieval, and the API live in one Python schema, so the app runs on one backend instead of Supabase plus a media service. Supabase still wins on realtime subscriptions and row-level security, which Pixeltable does not have, and on ingest throughput in the benchmark below. ## 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 - 302 app lines plus 246 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 at both tiers (16.89× realtime at 100 videos) and fastest evolve wall-clock at the larger corpus ## Comparison | Feature | Pixeltable | Supabase | | --- | --- | --- | | Total code for this app | 129 lines, 1 file | 548 lines (302 + 246 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) | 0.96s at 23 videos, 5.22s at 123; schema change and backfill are the same step | 2.71s at 23, 3.51s at 123; the winner flips with corpus | | Ingest, 20 videos / 10 min | 62.3s, 9.7× realtime | 45.8s, 13.2× realtime | | Ingest, 100 videos / 63 min | 361.6s, 10.45× realtime | 223.7s, 16.89× realtime | | Frame search p50 (203 videos) | 22.2ms | 17.3ms | | Agent query p50 (local model) | 206ms; the model runs where the data is | 700ms; three compute-service round trips per question | | Reads at 203 videos, p50 | 5.1ms list, 0.7ms frame fetch | 7.8ms list, 2.1ms frame fetch; every request traverses Kong | | Search under 8 clients, p50 | 119.5ms; the in-process embedding serializes | 67.0ms; the stateless layer stays parallel | | Hosted model, 12 agent questions (paid endpoint) | 12/12; 8.4s p50, the request-rate scheduler serializes pacing | 12/12; 2.3s p50; zero recorded retries | | Hosted swap, lines of retry code written | 7; the provider scheduler paces and retries | 36; pacing, Retry-After and backoff by hand | | 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 - **A Supabase alternative for media, embeddings, and model calls**: 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 want one backend, not two**: A Supabase app that handles media grows a second service, because Edge Functions cannot run ffmpeg, Whisper, or CLIP. Pixeltable keeps the rows, the media, the model calls, and the routes in one schema, so there is one thing to deploy and one place to change it. ## When to choose Supabase - **Realtime or row-level security is the requirement**: The UI has to update through realtime subscriptions, or tenant isolation has to be enforced by Postgres row-level security. Pixeltable has neither. Media work then lives 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 of the three, and again at 100 videos. If that is the constraint, follow it rather than the sponsor. ## Making the right choice ### What this page does not measure - Cost in dollars, multi-tenant authorization against auth.uid(), p99 latency, and on-call. - Reproduce the numbers: https://github.com/pixeltable/pixeltable-vs-supabase-vs-convex ### Related - [What a multimodal backend is](https://www.pixeltable.com/learn/what-is-a-multimodal-backend) - [What an application database is (Postgres, Auth, CRUD)](https://www.pixeltable.com/learn/what-is-an-application-database) - [Multimodal application development](https://www.pixeltable.com/use-cases/multimodal-ai-application-development) ## FAQ ### Is Pixeltable a Supabase alternative? For AI apps that work with images, video, and embeddings, yes: Pixeltable is the unified multimodal AI backend (storage, computed columns, retrieval, and serving in one Python file), so it replaces Supabase and the media service a Supabase app would need beside it. It has no realtime subscriptions or row-level security. Last reviewed: Oct 2026. ### What's a good Supabase alternative for AI apps that work with images, video and embeddings? Pixeltable. Media, embeddings, and model calls live on the table; insert runs ffmpeg, Whisper, and CLIP in the schema, and the same file serves the API. Pixeltable Cloud runs it on a hosted database: https://docs.pixeltable.com/cloud. ### When is Supabase the better choice? When the app depends on realtime subscriptions or on row-level security for tenant isolation, which Pixeltable does not have, or when ingest throughput at this scale is the binding constraint: Supabase ingests fastest at both tiers here. Media work then lives in a second service. That is the trade this page measures. ### What about sign-in and auth? Pixeltable does not handle sign-in. Use an auth provider such as Clerk or WorkOS, verify the session on your server, and scope Pixeltable queries by tenant: https://www.pixeltable.com/blog/auth-multitenancy-pixeltable-backend ### 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 20.9ms against Supabase’s 20.4ms at 23 videos, and 22.2ms against 17.3ms at 203 videos. Both are interactive, and most of every number is embedding the query rather than the index. At 7,689 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.