Pixeltable

Pixeltable integration guides for OpenAI, Anthropic, Gemini, Hugging Face, Ollama, Whisper, Langfuse, Amazon S3, and FiftyOne, with setup steps and working samples.

The page covers AI use cases and guides for building video search engines, video analysis, document processing, and RAG with Pixeltable.

A backend for AI agents is one Python file: tables, computed columns, and HTTP routes. The agent writes app.py, insert runs the models, you review one diff.

Define AI automation workflows as computed columns on a multimodal table. Insert a row; models, indexes, and HTTP update. No extra orchestrator.

The page explains AI agent architecture, state management, LLM reasoning, and building production-ready agents with Pixeltable.

Pixeltable’s CEO and cofounder, Pierre Brunelle, discusses building production-ready multimodal AI agents using declarative syntax.

29 runnable example apps, each one app.py: the tables it declares, the columns that compute on insert, and the routes that serve them. Open any of them in GitHub Codespaces.

Question-first definitions for multimodal databases, RAG, video search, and embeddings, plus 2026 and 2027 platform roundups and what the stack costs.

Pixeltable offers career opportunities in developing multimodal AI data infrastructure.

The same workloads on every Pixeltable release since 0.2, on one machine: inserts, video frames, embeddings, search and updates.

Pixeltable offers free local and Community tiers, with Pro plans starting at $29/month for production cloud databases and Deploy endpoints.

The page compares free tiers of Neon, Supabase, Turso, Prisma, Convex, Railway, Render, Vercel, Modal, Cloudflare, and Pixeltable, including limits and AWS runtimes.

Fair LanceDB alternatives: Deep Lake, Chroma, Weaviate, Milvus, Qdrant, pgvector, and an application schema. Last reviewed October 2026; vendor docs linked.

Fair Pinecone alternatives: Weaviate, Qdrant, Milvus, pgvector, LanceDB, Chroma, and a source-table index. Last reviewed October 2026; vendor docs linked.

Same app, three implementations. Pixeltable is 129 lines in one file; Supabase is 548; Convex is 671. All three beat realtime on this laptop. Feature matrix, real code, ingest and evolve timings from a published benchmark.

Pixeltable provides a Python framework for incremental storage, transformation, indexing, and orchestration of multimodal data to support production AI applications.

Pixeltable provides contact information for support, partnerships, and inquiries about its multimodal AI data infrastructure.

The unified multimodal backend agents build with. Database, orchestration, and serving in one Python file. Apply locally, serve, then publish the same file to Pixeltable Cloud.

How Pixeltable collects account information, processes Customer Content in Cloud, and uses subprocessors.

Find Pixeltable's Terms of Service, Privacy Policy, Security practices, and how to request an Enterprise DPA.

Pixeltable’s Terms of Service outline user rights and responsibilities for its AI data infrastructure and services.

GitHubRepository owner

Pixeltable develops infrastructure to support multimodal AI training as text data scarcity looms. Research by Epoch AI projects exhaustion of high-quality text data by 2026–2028. OpenAI co-founder Ilya Sutskever recently stated that useful online data for model training is already depleted. Over 35% of top websites now block AI scrapers, forcing companies like OpenAI to negotiate direct data partnerships with publishers.

Nov 2025Backblaze Blog | Cloud Storage & Cloud BackupNews article

Pixeltable introduces **Pixeltable Cloud Data Sharing**, a platform enabling users to publish and replicate multimodal AI datasets. The service allows sharing raw media (images, videos, audio, documents, JSON), computed results, and embedding indexes as a single queryable table. Teammates can instantly replicate the dataset for direct use in their workflows.

Nov 2025pixeltable.comNews article

Pixeltable secures $5.5 million in seed funding to advance its AI-driven data infrastructure platform. The San Francisco-based startup leverages the round to scale operations and enhance its technology for enterprise data management. The funding highlights growing investor interest in AI infrastructure solutions.

Dec 2024The SaaS NewsNews article