| Ecosystem | 0 | 0 |
| 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
Store and automatically index high-dimensional vector embeddings in a managed, scalable database without manual infrastructure provisioning. The system handles index optimization and partitioning transparently.
Query stored vectors to find semantically similar items by computing distance metrics between query embeddings and indexed vectors. Returns ranked results based on relevance in sub-100ms latency.
Serve as the retrieval component in Retrieval-Augmented Generation pipelines, providing relevant context documents to language models for grounded responses.
Provide a generous free tier (1 pod, 100K vectors) enabling teams to build and test real applications before committing to paid plans.
Execute searches using both dense vector embeddings and sparse keyword-based vectors simultaneously, combining results to improve relevance by capturing both semantic and lexical similarity.
Filter vector search results based on metadata attributes (tags, categories, timestamps, custom fields) before or during similarity search, enabling faceted and conditional retrieval.
Insert, update, or replace multiple vectors and their metadata in a single batch operation, optimizing throughput for bulk data ingestion without individual API calls.
Pinecone scores higher at 71/100 vs Teachable Machine at 52/100. Teachable Machine leads on ecosystem, while Pinecone is stronger on adoption and quality.
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Partition vector data within a single index using namespaces, enabling logical separation of data (by user, tenant, or dataset) without creating separate indexes.
+4 more capabilities