Teachable Machine vs vectra
Side-by-side comparison to help you choose.
| Feature | Teachable Machine | vectra |
|---|---|---|
| Type | Web App | Repository |
| UnfragileRank | 32/100 | 41/100 |
| Adoption | 0 | 0 |
| Quality | 1 | 0 |
| Ecosystem |
| 0 |
| 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 14 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Train custom image classification models by uploading or capturing images from a webcam, organizing them into labeled classes, and training a model in the browser without writing code.
Train custom audio classification models by recording or uploading sound samples, labeling them by category, and generating a model that recognizes audio patterns without coding.
Upload pre-existing image or audio files from a device's file system to use as training data, supporting batch imports of multiple files organized by class.
Organize training samples into labeled classes or categories, with visual feedback showing sample counts and distribution across classes to ensure balanced training data.
Train and export models without requiring account creation or login, enabling immediate access to the platform for casual users and classroom demonstrations.
Display prediction results with confidence scores showing how certain the model is about each classification, providing transparency into model decision-making.
Train custom pose recognition models by capturing body positions via webcam, labeling different poses or movements, and creating a model that identifies human body positions and gestures.
Test trained models in real-time within the browser interface using live webcam feed or uploaded samples, with immediate visual feedback on predictions and confidence scores.
+6 more capabilities
Stores vector embeddings and metadata in JSON files on disk while maintaining an in-memory index for fast similarity search. Uses a hybrid architecture where the file system serves as the persistent store and RAM holds the active search index, enabling both durability and performance without requiring a separate database server. Supports automatic index persistence and reload cycles.
Unique: Combines file-backed persistence with in-memory indexing, avoiding the complexity of running a separate database service while maintaining reasonable performance for small-to-medium datasets. Uses JSON serialization for human-readable storage and easy debugging.
vs alternatives: Lighter weight than Pinecone or Weaviate for local development, but trades scalability and concurrent access for simplicity and zero infrastructure overhead.
Implements vector similarity search using cosine distance calculation on normalized embeddings, with support for alternative distance metrics. Performs brute-force similarity computation across all indexed vectors, returning results ranked by distance score. Includes configurable thresholds to filter results below a minimum similarity threshold.
Unique: Implements pure cosine similarity without approximation layers, making it deterministic and debuggable but trading performance for correctness. Suitable for datasets where exact results matter more than speed.
vs alternatives: More transparent and easier to debug than approximate methods like HNSW, but significantly slower for large-scale retrieval compared to Pinecone or Milvus.
Accepts vectors of configurable dimensionality and automatically normalizes them for cosine similarity computation. Validates that all vectors have consistent dimensions and rejects mismatched vectors. Supports both pre-normalized and unnormalized input, with automatic L2 normalization applied during insertion.
vectra scores higher at 41/100 vs Teachable Machine at 32/100. Teachable Machine leads on quality, while vectra is stronger on adoption and ecosystem.
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Unique: Automatically normalizes vectors during insertion, eliminating the need for users to handle normalization manually. Validates dimensionality consistency.
vs alternatives: More user-friendly than requiring manual normalization, but adds latency compared to accepting pre-normalized vectors.
Exports the entire vector database (embeddings, metadata, index) to standard formats (JSON, CSV) for backup, analysis, or migration. Imports vectors from external sources in multiple formats. Supports format conversion between JSON, CSV, and other serialization formats without losing data.
Unique: Supports multiple export/import formats (JSON, CSV) with automatic format detection, enabling interoperability with other tools and databases. No proprietary format lock-in.
vs alternatives: More portable than database-specific export formats, but less efficient than binary dumps. Suitable for small-to-medium datasets.
Implements BM25 (Okapi BM25) lexical search algorithm for keyword-based retrieval, then combines BM25 scores with vector similarity scores using configurable weighting to produce hybrid rankings. Tokenizes text fields during indexing and performs term frequency analysis at query time. Allows tuning the balance between semantic and lexical relevance.
Unique: Combines BM25 and vector similarity in a single ranking framework with configurable weighting, avoiding the need for separate lexical and semantic search pipelines. Implements BM25 from scratch rather than wrapping an external library.
vs alternatives: Simpler than Elasticsearch for hybrid search but lacks advanced features like phrase queries, stemming, and distributed indexing. Better integrated with vector search than bolting BM25 onto a pure vector database.
Supports filtering search results using a Pinecone-compatible query syntax that allows boolean combinations of metadata predicates (equality, comparison, range, set membership). Evaluates filter expressions against metadata objects during search, returning only vectors that satisfy the filter constraints. Supports nested metadata structures and multiple filter operators.
Unique: Implements Pinecone's filter syntax natively without requiring a separate query language parser, enabling drop-in compatibility for applications already using Pinecone. Filters are evaluated in-memory against metadata objects.
vs alternatives: More compatible with Pinecone workflows than generic vector databases, but lacks the performance optimizations of Pinecone's server-side filtering and index-accelerated predicates.
Integrates with multiple embedding providers (OpenAI, Azure OpenAI, local transformer models via Transformers.js) to generate vector embeddings from text. Abstracts provider differences behind a unified interface, allowing users to swap providers without changing application code. Handles API authentication, rate limiting, and batch processing for efficiency.
Unique: Provides a unified embedding interface supporting both cloud APIs and local transformer models, allowing users to choose between cost/privacy trade-offs without code changes. Uses Transformers.js for browser-compatible local embeddings.
vs alternatives: More flexible than single-provider solutions like LangChain's OpenAI embeddings, but less comprehensive than full embedding orchestration platforms. Local embedding support is unique for a lightweight vector database.
Runs entirely in the browser using IndexedDB for persistent storage, enabling client-side vector search without a backend server. Synchronizes in-memory index with IndexedDB on updates, allowing offline search and reducing server load. Supports the same API as the Node.js version for code reuse across environments.
Unique: Provides a unified API across Node.js and browser environments using IndexedDB for persistence, enabling code sharing and offline-first architectures. Avoids the complexity of syncing client-side and server-side indices.
vs alternatives: Simpler than building separate client and server vector search implementations, but limited by browser storage quotas and IndexedDB performance compared to server-side databases.
+4 more capabilities