Alternatives to Universal Sentence Encoder
Compare Universal Sentence Encoder alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Universal Sentence Encoder in 2026. Compare features, ratings, user reviews, pricing, and more from Universal Sentence Encoder competitors and alternatives in order to make an informed decision for your business.
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1
Qdrant
Qdrant
Qdrant is a vector similarity engine & vector database. It deploys as an API service providing search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more! Provides the OpenAPI v3 specification to generate a client library in almost any programming language. Alternatively utilise ready-made client for Python or other programming languages with additional functionality. Implement a unique custom modification of the HNSW algorithm for Approximate Nearest Neighbor Search. Search with a State-of-the-Art speed and apply search filters without compromising on results. Support additional payload associated with vectors. Not only stores payload but also allows filter results based on payload values. -
2
word2vec
Google
Word2Vec is a neural network-based technique for learning word embeddings, developed by researchers at Google. It transforms words into continuous vector representations in a multi-dimensional space, capturing semantic relationships based on context. Word2Vec uses two main architectures: Skip-gram, which predicts surrounding words given a target word, and Continuous Bag-of-Words (CBOW), which predicts a target word based on surrounding words. By training on large text corpora, Word2Vec generates word embeddings where similar words are positioned closely, enabling tasks like semantic similarity, analogy solving, and text clustering. The model was influential in advancing NLP by introducing efficient training techniques such as hierarchical softmax and negative sampling. Though newer embedding models like BERT and Transformer-based methods have surpassed it in complexity and performance, Word2Vec remains a foundational method in natural language processing and machine learning research.Starting Price: Free -
3
E5 Text Embeddings
Microsoft
E5 Text Embeddings, developed by Microsoft, are advanced models designed to convert textual data into meaningful vector representations, enhancing tasks like semantic search and information retrieval. These models are trained using weakly-supervised contrastive learning on a vast dataset of over one billion text pairs, enabling them to capture intricate semantic relationships across multiple languages. The E5 family includes models of varying sizes—small, base, and large—offering a balance between computational efficiency and embedding quality. Additionally, multilingual versions of these models have been fine-tuned to support diverse languages, ensuring broad applicability in global contexts. Comprehensive evaluations demonstrate that E5 models achieve performance on par with state-of-the-art, English-only models of similar sizes.Starting Price: Free -
4
Arctic Embed 2.0
Snowflake
Snowflake's Arctic Embed 2.0 introduces multilingual capabilities to its text embedding models, enhancing global-scale retrieval without compromising English performance or scalability. Building upon the robust foundation of previous releases, Arctic Embed 2.0 supports multiple languages, enabling developers to create stream-processing pipelines that incorporate neural networks and complex tasks like tracking, video encoding/decoding, and rendering, facilitating real-time analytics on various data types. The model leverages Matryoshka Representation Learning (MRL) for efficient embedding storage, allowing for significant compression with minimal quality degradation. This advancement ensures that enterprises can handle demanding workloads such as training large-scale models, fine-tuning, real-time inference, and high-performance computing tasks across diverse languages and regions.Starting Price: $2 per credit -
5
EmbeddingGemma
Google
EmbeddingGemma is a 308-million-parameter multilingual text embedding model, lightweight yet powerful, optimized to run entirely on everyday devices such as phones, laptops, and tablets, enabling fast, offline embedding generation that protects user privacy. Built on the Gemma 3 architecture, it supports over 100 languages, processes up to 2,000 input tokens, and leverages Matryoshka Representation Learning (MRL) to offer flexible embedding dimensions (768, 512, 256, or 128) for tailored speed, storage, and precision. Its GPU-and EdgeTPU-accelerated inference delivers embeddings in milliseconds, under 15 ms for 256 tokens on EdgeTPU, while quantization-aware training keeps memory usage under 200 MB without compromising quality. This makes it ideal for real-time, on-device tasks such as semantic search, retrieval-augmented generation (RAG), classification, clustering, and similarity detection, whether for personal file search, mobile chatbots, or custom domain use. -
6
Exa
Exa.ai
The Exa API retrieves the best content on the web using embeddings-based search. Exa understands meaning, giving results search engines can’t. Exa uses a novel link prediction transformer to predict links which match the meaning of a prompt. For queries that need semantic understanding, search with our SOTA web embeddings model over our custom index. For all other queries, we offer keyword-based search. Stop learning how to web scrape or parse HTML. Get the clean, full text of any page in our index, or intelligent embeddings-ranked highlights related to a query. Select any date range, include or exclude any domain, select a custom data vertical, or get up to 10 million results..Starting Price: $100 per month -
7
Cohere Embed
Cohere
Cohere's Embed is a leading multimodal embedding platform designed to transform text, images, or a combination of both into high-quality vector representations. These embeddings are optimized for semantic search, retrieval-augmented generation, classification, clustering, and agentic AI applications. The latest model, embed-v4.0, supports mixed-modality inputs, allowing users to combine text and images into a single embedding. It offers Matryoshka embeddings with configurable dimensions of 256, 512, 1024, or 1536, enabling flexibility in balancing performance and resource usage. With a context length of up to 128,000 tokens, embed-v4.0 is well-suited for processing large documents and complex data structures. It also supports compressed embedding types, including float, int8, uint8, binary, and ubinary, facilitating efficient storage and faster retrieval in vector databases. Multilingual support spans over 100 languages, making it a versatile tool for global applications.Starting Price: $0.47 per image -
8
txtai
NeuML
txtai is an all-in-one open source embeddings database designed for semantic search, large language model orchestration, and language model workflows. It unifies vector indexes (both sparse and dense), graph networks, and relational databases, providing a robust foundation for vector search and serving as a powerful knowledge source for LLM applications. With txtai, users can build autonomous agents, implement retrieval augmented generation processes, and develop multi-modal workflows. Key features include vector search with SQL support, object storage integration, topic modeling, graph analysis, and multimodal indexing capabilities. It supports the creation of embeddings for various data types, including text, documents, audio, images, and video. Additionally, txtai offers pipelines powered by language models that handle tasks such as LLM prompting, question-answering, labeling, transcription, translation, and summarization.Starting Price: Free -
9
Codestral Embed
Mistral AI
Codestral Embed is Mistral AI's first embedding model, specialized for code, optimized for high-performance code retrieval and semantic understanding. It significantly outperforms leading code embedders in the market today, such as Voyage Code 3, Cohere Embed v4.0, and OpenAI’s large embedding model. Codestral Embed can output embeddings with different dimensions and precisions; for instance, with a dimension of 256 and int8 precision, it still performs better than any model from competitors. The dimensions of the embeddings are ordered by relevance, allowing users to choose the first n dimensions for a smooth trade-off between quality and cost. It excels in retrieval use cases on real-world code data, particularly in benchmarks like SWE-Bench, which is based on real-world GitHub issues and corresponding fixes, and Text2Code (GitHub), relevant for providing context for code completion or editing. -
10
Mixedbread
Mixedbread
Mixedbread is a fully-managed AI search engine that allows users to build production-ready AI search and Retrieval-Augmented Generation (RAG) applications. It offers a complete AI search stack, including vector stores, embedding and reranking models, and document parsing. Users can transform raw data into intelligent search experiences that power AI agents, chatbots, and knowledge systems without the complexity. It integrates with tools like Google Drive, SharePoint, Notion, and Slack. Its vector stores enable users to build production search engines in minutes, supporting over 100 languages. Mixedbread's embedding and reranking models have achieved over 50 million downloads and outperform OpenAI in semantic search and RAG tasks while remaining open-source and cost-effective. The document parser extracts text, tables, and layouts from PDFs, images, and complex documents, providing clean, AI-ready content without manual preprocessing. -
11
voyage-code-3
Voyage AI
Voyage AI introduces voyage-code-3, a next-generation embedding model optimized for code retrieval. It outperforms OpenAI-v3-large and CodeSage-large by an average of 13.80% and 16.81% on a suite of 32 code retrieval datasets, respectively. It supports embeddings of 2048, 1024, 512, and 256 dimensions and offers multiple embedding quantization options, including float (32-bit), int8 (8-bit signed integer), uint8 (8-bit unsigned integer), binary (bit-packed int8), and ubinary (bit-packed uint8). With a 32 K-token context length, it surpasses OpenAI's 8K and CodeSage Large's 1K context lengths. Voyage-code-3 employs Matryoshka learning to create embeddings with a nested family of various lengths within a single vector. This allows users to vectorize documents into a 2048-dimensional vector and later use shorter versions (e.g., 256, 512, or 1024 dimensions) without re-invoking the embedding model. -
12
TensorBoard
Tensorflow
TensorBoard is TensorFlow's comprehensive visualization toolkit designed to facilitate machine learning experimentation. It enables users to track and visualize metrics such as loss and accuracy, visualize the model graph (operations and layers), view histograms of weights, biases, or other tensors as they change over time, project embeddings to a lower-dimensional space, and display images, text, and audio data. Additionally, TensorBoard offers profiling capabilities to optimize TensorFlow programs. These features collectively provide a suite of tools to understand, debug, and optimize TensorFlow programs, enhancing the machine learning workflow. In machine learning, to improve something you often need to be able to measure it. TensorBoard is a tool for providing the measurements and visualizations needed during the machine learning workflow. It enables tracking experiment metrics, visualizing the model graph, and projecting embeddings to a lower dimensional space.Starting Price: Free -
13
VectorDB
VectorDB
VectorDB is a lightweight Python package for storing and retrieving text using chunking, embedding, and vector search techniques. It provides an easy-to-use interface for saving, searching, and managing textual data with associated metadata and is designed for use cases where low latency is essential. Vector search and embeddings are essential when working with large language models because they enable efficient and accurate retrieval of relevant information from massive datasets. By converting text into high-dimensional vectors, these techniques allow for quick comparisons and searches, even when dealing with millions of documents. This makes it possible to find the most relevant results in a fraction of the time it would take using traditional text-based search methods. Additionally, embeddings capture the semantic meaning of the text, which helps improve the quality of the search results and enables more advanced natural language processing tasks.Starting Price: Free -
14
GloVe
Stanford NLP
GloVe (Global Vectors for Word Representation) is an unsupervised learning algorithm developed by the Stanford NLP Group to obtain vector representations for words. It constructs word embeddings by analyzing global word-word co-occurrence statistics from a given corpus, resulting in vector spaces where the geometric relationships reflect semantic similarities and differences among words. A notable feature of GloVe is its ability to capture linear substructures within the word vector space, enabling vector arithmetic to express relationships. The model is trained on the non-zero entries of a global word-word co-occurrence matrix, which records how frequently pairs of words appear together in a corpus. This approach efficiently leverages statistical information by focusing on significant co-occurrences, leading to meaningful word representations. Pre-trained word vectors are available for various corpora, including Wikipedia 2014.Starting Price: Free -
15
Gemini Embedding
Google
Gemini Embedding’s first text model (gemini-embedding-001) is now generally available via the Gemini API and Vertex AI, having held a top spot on the Massive Text Embedding Benchmark Multilingual leaderboard since its experimental launch in March, thanks to superior performance across retrieval, classification, and other embedding tasks compared to both legacy Google and external proprietary models. Exceptionally versatile, it supports over 100 languages with a 2,048‑token input limit and employs the Matryoshka Representation Learning (MRL) technique to let developers choose output dimensions of 3072, 153,6, or 768 for optimal quality, performance, and storage efficiency. Developers can access it through the existing embed_content endpoint in the Gemini API, and while legacy experimental versions will be deprecated later in 2025, migration requires no re‑embedding of existing content.Starting Price: $0.15 per 1M input tokens -
16
Aquarium
Aquarium
Aquarium's embedding technology surfaces the biggest problems in your model performance and finds the right data to solve them. Unlock the power of neural network embeddings without worrying about maintaining infrastructure or debugging embedding models. Automatically find the most critical patterns of model failures in your dataset. Understand the long tail of edge cases and triage which issues to solve first. Trawl through massive unlabeled datasets to find edge-case scenarios. Bootstrap new classes with a handful of examples using few-shot learning technology. The more data you have, the more value we offer. Aquarium reliably scales to datasets containing hundreds of millions of data points. Aquarium offers solutions engineering resources, customer success syncs, and user training to help customers get value. We also offer an anonymous mode for organizations who want to use Aquarium without exposing any sensitive data.Starting Price: $1,250 per month -
17
voyage-3-large
Voyage AI
Voyage AI has unveiled voyage-3-large, a cutting-edge general-purpose and multilingual embedding model that leads across eight evaluated domains, including law, finance, and code, outperforming OpenAI-v3-large and Cohere-v3-English by averages of 9.74% and 20.71%, respectively. Enabled by Matryoshka learning and quantization-aware training, it supports embeddings of 2048, 1024, 512, and 256 dimensions, along with multiple quantization options such as 32-bit floating point, signed and unsigned 8-bit integer, and binary precision, significantly reducing vector database costs with minimal impact on retrieval quality. Notably, voyage-3-large offers a 32K-token context length, surpassing OpenAI's 8K and Cohere's 512 tokens. Evaluations across 100 datasets in diverse domains demonstrate its superior performance, with flexible precision and dimensionality options enabling substantial storage savings without compromising quality. -
18
Nomic Embed
Nomic
Nomic Embed is a suite of open source, high-performance embedding models designed for various applications, including multilingual text, multimodal content, and code. The ecosystem includes models like Nomic Embed Text v2, which utilizes a Mixture-of-Experts (MoE) architecture to support over 100 languages with efficient inference using 305M active parameters. Nomic Embed Text v1.5 offers variable embedding dimensions (64 to 768) through Matryoshka Representation Learning, enabling developers to balance performance and storage needs. For multimodal applications, Nomic Embed Vision v1.5 aligns with the text models to provide a unified latent space for text and image data, facilitating seamless multimodal search. Additionally, Nomic Embed Code delivers state-of-the-art performance on code embedding tasks across multiple programming languages.Starting Price: Free -
19
Meii AI
Meii AI
Meii AI is a global leader in AI solutions, offering industry-trained Large Language Models that can be tuned accordingly with company-specific data and hosted privately or in your cloud. Our RAG ( Retrieval Augmented Generation ) based AI approach uses Embedded Model and Retrieval context ( Semantic Search ) while processing a conversational query to curate Insightful response that is specific for an Enterprise. Blended with our unique skills and decade long experience we had gained in Data Analytics solutions, we combine LLMs and ML Algorithms that offer great solutions for Mid level Enterprises. We are engineering a future that allows people, businesses, and governments to seamlessly leverage technology. With a vision to make AI accessible for everyone on the planet, our team is constantly breaking the barriers between machines and humans. -
20
Amazon S3 Vectors
Amazon
Amazon S3 Vectors is the first cloud object store with native support for storing and querying vector embeddings at scale, delivering purpose-built, cost-optimized vector storage for semantic search, AI agents, retrieval-augmented generation, and similarity-search applications. It introduces a new “vector bucket” type in S3, where users can organize vectors into “vector indexes,” store high-dimensional embeddings (representing text, images, audio, or other unstructured data), and run similarity queries via dedicated APIs, all without provisioning infrastructure. Each vector may carry metadata (e.g., tags, timestamps, categories), enabling filtered queries by attributes. S3 Vectors offers massive scale; now generally available, it supports up to 2 billion vectors per index and up to 10,000 vector indexes per bucket, with elastic, durable storage and server-side encryption (SSE-S3 or optionally KMS). -
21
Neum AI
Neum AI
No one wants their AI to respond with out-of-date information to a customer. Neum AI helps companies have accurate and up-to-date context in their AI applications. Use built-in connectors for data sources like Amazon S3 and Azure Blob Storage, vector stores like Pinecone and Weaviate to set up your data pipelines in minutes. Supercharge your data pipeline by transforming and embedding your data with built-in connectors for embedding models like OpenAI and Replicate, and serverless functions like Azure Functions and AWS Lambda. Leverage role-based access controls to make sure only the right people can access specific vectors. Bring your own embedding models, vector stores and sources. Ask us about how you can even run Neum AI in your own cloud. -
22
LexVec
Alexandre Salle
LexVec is a word embedding model that achieves state-of-the-art results in multiple natural language processing tasks by factorizing the Positive Pointwise Mutual Information (PPMI) matrix using stochastic gradient descent. This approach assigns heavier penalties for errors on frequent co-occurrences while accounting for negative co-occurrences. Pre-trained vectors are available, including a common crawl dataset with 58 billion tokens and 2 million words in 300 dimensions, and an English Wikipedia 2015 + NewsCrawl dataset with 7 billion tokens and 368,999 words in 300 dimensions. Evaluations demonstrate that LexVec matches or outperforms other models like word2vec in terms of word similarity and analogy tasks. The implementation is open source under the MIT License and is available on GitHub.Starting Price: Free -
23
Gensim
Radim Řehůřek
Gensim is a free, open source Python library designed for unsupervised topic modeling and natural language processing, focusing on large-scale semantic modeling. It enables the training of models like Word2Vec, FastText, Latent Semantic Analysis (LSA), and Latent Dirichlet Allocation (LDA), facilitating the representation of documents as semantic vectors and the discovery of semantically related documents. Gensim is optimized for performance with highly efficient implementations in Python and Cython, allowing it to process arbitrarily large corpora using data streaming and incremental algorithms without loading the entire dataset into RAM. It is platform-independent, running on Linux, Windows, and macOS, and is licensed under the GNU LGPL, promoting both personal and commercial use. The library is widely adopted, with thousands of companies utilizing it daily, over 2,600 academic citations, and more than 1 million downloads per week.Starting Price: Free -
24
Marengo
TwelveLabs
Marengo is a multimodal video foundation model that transforms video, audio, image, and text inputs into unified embeddings, enabling powerful “any-to-any” search, retrieval, classification, and analysis across vast video and multimedia libraries. It integrates visual frames (with spatial and temporal dynamics), audio (speech, ambient sound, music), and textual content (subtitles, overlays, metadata) to create a rich, multidimensional representation of each media item. With this embedding architecture, Marengo supports robust tasks such as search (text-to-video, image-to-video, video-to-audio, etc.), semantic content discovery, anomaly detection, hybrid search, clustering, and similarity-based recommendation. The latest versions introduce multi-vector embeddings, separating representations for appearance, motion, and audio/text features, which significantly improve precision and context awareness, especially for complex or long-form content.Starting Price: $0.042 per minute -
25
AISixteen
AISixteen
The ability to convert text into images using artificial intelligence has gained significant attention in recent years. Stable diffusion is one effective method for achieving this task, utilizing the power of deep neural networks to generate images from textual descriptions. The first step is to convert the textual description of an image into a numerical format that a neural network can process. Text embedding is a popular technique that converts each word in the text into a vector representation. After encoding, a deep neural network generates an initial image based on the encoded text. This image is usually noisy and lacks detail, but it serves as a starting point for the next step. The generated image is refined in several iterations to improve the quality. Diffusion steps are applied gradually, smoothing and removing noise while preserving important features such as edges and contours. -
26
NVIDIA NeMo Retriever
NVIDIA
NVIDIA NeMo Retriever is a collection of microservices for building multimodal extraction, reranking, and embedding pipelines with high accuracy and maximum data privacy. It delivers quick, context-aware responses for AI applications like advanced retrieval-augmented generation (RAG) and agentic AI workflows. As part of the NVIDIA NeMo platform and built with NVIDIA NIM, NeMo Retriever allows developers to flexibly leverage these microservices to connect AI applications to large enterprise datasets wherever they reside and fine-tune them to align with specific use cases. NeMo Retriever provides components for building data extraction and information retrieval pipelines. The pipeline extracts structured and unstructured data (e.g., text, charts, tables), converts it to text, and filters out duplicates. A NeMo Retriever embedding NIM converts the chunks into embeddings and stores them in a vector database, accelerated by NVIDIA cuVS, for enhanced performance and speed of indexing. -
27
Cohere
Cohere AI
Cohere is an enterprise AI platform that enables developers and businesses to build powerful language-based applications. Specializing in large language models (LLMs), Cohere provides solutions for text generation, summarization, and semantic search. Their model offerings include the Command family for high-performance language tasks and Aya Expanse for multilingual applications across 23 languages. Focused on security and customization, Cohere allows flexible deployment across major cloud providers, private cloud environments, or on-premises setups to meet diverse enterprise needs. The company collaborates with industry leaders like Oracle and Salesforce to integrate generative AI into business applications, improving automation and customer engagement. Additionally, Cohere For AI, their research lab, advances machine learning through open-source projects and a global research community.Starting Price: Free -
28
BGE
BGE
BGE (BAAI General Embedding) is a comprehensive retrieval toolkit designed for search and Retrieval-Augmented Generation (RAG) applications. It offers inference, evaluation, and fine-tuning capabilities for embedding models and rerankers, facilitating the development of advanced information retrieval systems. The toolkit includes components such as embedders and rerankers, which can be integrated into RAG pipelines to enhance search relevance and accuracy. BGE supports various retrieval methods, including dense retrieval, multi-vector retrieval, and sparse retrieval, providing flexibility to handle different data types and retrieval scenarios. The models are available through platforms like Hugging Face, and the toolkit provides tutorials and APIs to assist users in implementing and customizing their retrieval systems. By leveraging BGE, developers can build robust and efficient search solutions tailored to their specific needs.Starting Price: Free -
29
Context Data
Context Data
Context Data is an enterprise data infrastructure built to accelerate the development of data pipelines for Generative AI applications. The platform automates the process of setting up internal data processing and transformation flows using an easy-to-use connectivity framework where developers and enterprises can quickly connect to all of their internal data sources, embedding models and vector database targets without having to set up expensive infrastructure or engineers. The platform also allows developers to schedule recurring data flows for refreshed and up-to-date data.Starting Price: $99 per month -
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Voyage AI
Voyage AI
Voyage AI delivers state-of-the-art embedding and reranking models that supercharge intelligent retrieval for enterprises, driving forward retrieval-augmented generation and reliable LLM applications. Available through all major clouds and data platforms. SaaS and customer tenant deployment (in-VPC). Our solutions are designed to optimize the way businesses access and utilize information, making retrieval faster, more accurate, and scalable. Built by academic experts from Stanford, MIT, and UC Berkeley, alongside industry professionals from Google, Meta, Uber, and other leading companies, our team develops transformative AI solutions tailored to enterprise needs. We are committed to pushing the boundaries of AI innovation and delivering impactful technologies for businesses. Contact us for custom or on-premise deployments as well as model licensing. Easy to get started, pay as you go, with consumption-based pricing. -
31
Mu
Microsoft
Mu is a 330-million-parameter encoder–decoder language model designed to power the agent in Windows settings by mapping natural-language queries to Settings function calls, running fully on-device via NPUs at over 100 tokens per second while maintaining high accuracy. Drawing on Phi Silica optimizations, Mu’s encoder–decoder architecture reuses a fixed-length latent representation to cut computation and memory overhead, yielding 47 percent lower first-token latency and 4.7× higher decoding speed on Qualcomm Hexagon NPUs compared to similar decoder-only models. Hardware-aware tuning, including a 2/3–1/3 encoder–decoder parameter split, weight sharing between input and output embeddings, Dual LayerNorm, rotary positional embeddings, and grouped-query attention, enables fast inference at over 200 tokens per second on devices like Surface Laptop 7 and sub-500 ms response times for settings queries. -
32
Google AI Edge
Google
Google AI Edge offers a comprehensive suite of tools and frameworks designed to facilitate the deployment of artificial intelligence across mobile, web, and embedded applications. By enabling on-device processing, it reduces latency, allows offline functionality, and ensures data remains local and private. It supports cross-platform compatibility, allowing the same model to run seamlessly across embedded systems. It is also multi-framework compatible, working with models from JAX, Keras, PyTorch, and TensorFlow. Key components include low-code APIs for common AI tasks through MediaPipe, enabling quick integration of generative AI, vision, text, and audio functionalities. Visualize the transformation of your model through conversion and quantification. Overlays the results of the comparisons to debug the hotspots. Explore, debug, and compare your models visually. Overlays comparisons and numerical performance data to identify problematic hotspots.Starting Price: Free -
33
fastText
fastText
fastText is an open source, free, and lightweight library developed by Facebook's AI Research (FAIR) lab for efficient learning of word representations and text classification. It supports both unsupervised learning of word vectors and supervised learning for text classification tasks. A key feature of fastText is its ability to capture subword information by representing words as bags of character n-grams, which enhances the handling of morphologically rich languages and out-of-vocabulary words. The library is optimized for performance and capable of training on large datasets quickly, and the resulting models can be reduced in size for deployment on mobile devices. Pre-trained word vectors are available for 157 languages, trained on Common Crawl and Wikipedia data, and can be downloaded for immediate use. fastText also offers aligned word vectors for 44 languages, facilitating cross-lingual natural language processing tasks.Starting Price: Free -
34
Ensemble Dark Matter
Ensemble
Train accurate ML models on limited, sparse, and high-dimensional data without extensive feature engineering by creating statistically optimized representations of your data. By learning how to extract and represent complex relationships in your existing data, Dark Matter improves model performance and speeds up training without extensive feature engineering or resource-intensive deep learning, enabling data scientists to spend less time on data and more time-solving hard problems. Dark Matter significantly improved model precision and f1 scores in predicting customer conversion in the online retail space. Model performance metrics improved across the board when trained on an optimized embedding learned from a sparse, high-dimensional data set. Training XGBoost on a better representation of the data improved predictions of customer churn in the banking industry. Enhance your pipeline, no matter your model or domain. -
35
SiMa
SiMa
SiMa offers a software-centric, embedded edge machine learning system-on-chip (MLSoC) platform that delivers high-performance, low-power AI solutions for various applications. The MLSoC integrates multiple modalities, including text, image, audio, video, and haptic inputs, performing complex ML inference and presenting outputs in any modality. It supports a wide range of frameworks (e.g., TensorFlow, PyTorch, ONNX) and can compile over 250 models, providing customers with an effortless experience and world-class performance-per-watt results. Complementing the hardware, SiMa.ai is designed for complete ML stack application development. It supports any ML workflow customers plan to deploy on the edge without compromising performance and ease of use. Palette's integrated ML compiler accepts any model from any neural network framework. -
36
Embeddinghub
Featureform
Operationalize your embeddings with one simple tool. Experience a comprehensive database designed to provide embedding functionality that, until now, required multiple platforms. Elevate your machine learning quickly and painlessly through Embeddinghub. Embeddings are dense, numerical representations of real-world objects and relationships, expressed as vectors. They are often created by first defining a supervised machine learning problem, known as a "surrogate problem." Embeddings intend to capture the semantics of the inputs they were derived from, subsequently getting shared and reused for improved learning across machine learning models. Embeddinghub lets you achieve this in a streamlined, intuitive way.Starting Price: Free -
37
TFLearn
TFLearn
TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed up experimentations while remaining fully transparent and compatible with it. Easy-to-use and understand high-level API for implementing deep neural networks, with tutorial and examples. Fast prototyping through highly modular built-in neural network layers, regularizers, optimizers, metrics. Full transparency over Tensorflow. All functions are built over tensors and can be used independently of TFLearn. Powerful helper functions to train any TensorFlow graph, with support of multiple inputs, outputs, and optimizers. Easy and beautiful graph visualization, with details about weights, gradients, activations and more. The high-level API currently supports most of the recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, Generative networks. -
38
OpenAI
OpenAI
OpenAI’s mission is to ensure that artificial general intelligence (AGI)—by which we mean highly autonomous systems that outperform humans at most economically valuable work—benefits all of humanity. We will attempt to directly build safe and beneficial AGI, but will also consider our mission fulfilled if our work aids others to achieve this outcome. Apply our API to any language task — semantic search, summarization, sentiment analysis, content generation, translation, and more — with only a few examples or by specifying your task in English. One simple integration gives you access to our constantly-improving AI technology. Explore how you integrate with the API with these sample completions. -
39
spaCy
spaCy
spaCy is designed to help you do real work, build real products, or gather real insights. The library respects your time and tries to avoid wasting it. It's easy to install, and its API is simple and productive. spaCy excels at large-scale information extraction tasks. It's written from the ground up in carefully memory-managed Cython. If your application needs to process entire web dumps, spaCy is the library you want to be using. Since its release in 2015, spaCy has become an industry standard with a huge ecosystem. Choose from a variety of plugins, integrate with your machine learning stack, and build custom components and workflows. Components for named entity recognition, part-of-speech tagging, dependency parsing, sentence segmentation, text classification, lemmatization, morphological analysis, entity linking, and more. Easily extensible with custom components and attributes. Easy model packaging, deployment, and workflow management.Starting Price: Free -
40
NVIDIA DIGITS
NVIDIA DIGITS
The NVIDIA Deep Learning GPU Training System (DIGITS) puts the power of deep learning into the hands of engineers and data scientists. DIGITS can be used to rapidly train the highly accurate deep neural network (DNNs) for image classification, segmentation and object detection tasks. DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real-time with advanced visualizations, and selecting the best performing model from the results browser for deployment. DIGITS is completely interactive so that data scientists can focus on designing and training networks rather than programming and debugging. Interactively train models using TensorFlow and visualize model architecture using TensorBoard. Integrate custom plug-ins for importing special data formats such as DICOM used in medical imaging. -
41
Llama 3.2
Meta
The open-source AI model you can fine-tune, distill and deploy anywhere is now available in more versions. Choose from 1B, 3B, 11B or 90B, or continue building with Llama 3.1. Llama 3.2 is a collection of large language models (LLMs) pretrained and fine-tuned in 1B and 3B sizes that are multilingual text only, and 11B and 90B sizes that take both text and image inputs and output text. Develop highly performative and efficient applications from our latest release. Use our 1B or 3B models for on device applications such as summarizing a discussion from your phone or calling on-device tools like calendar. Use our 11B or 90B models for image use cases such as transforming an existing image into something new or getting more information from an image of your surroundings.Starting Price: Free -
42
ColBERT
Future Data Systems
ColBERT is a fast and accurate retrieval model, enabling scalable BERT-based search over large text collections in tens of milliseconds. It relies on fine-grained contextual late interaction: it encodes each passage into a matrix of token-level embeddings. At search time, it embeds every query into another matrix and efficiently finds passages that contextually match the query using scalable vector-similarity (MaxSim) operators. These rich interactions allow ColBERT to surpass the quality of single-vector representation models while scaling efficiently to large corpora. The toolkit includes components for retrieval, reranking, evaluation, and response analysis, facilitating end-to-end workflows. ColBERT integrates with Pyserini for retrieval and provides integrated evaluation for multi-stage pipelines. It also includes a module for detailed analysis of input prompts and LLM responses, addressing reliability concerns with LLM APIs and non-deterministic behavior in Mixture-of-Experts.Starting Price: Free -
43
BERT
Google
BERT is a large language model and a method of pre-training language representations. Pre-training refers to how BERT is first trained on a large source of text, such as Wikipedia. You can then apply the training results to other Natural Language Processing (NLP) tasks, such as question answering and sentiment analysis. With BERT and AI Platform Training, you can train a variety of NLP models in about 30 minutes.Starting Price: Free -
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Llama
Meta
Llama (Large Language Model Meta AI) is a state-of-the-art foundational large language model designed to help researchers advance their work in this subfield of AI. Smaller, more performant models such as Llama enable others in the research community who don’t have access to large amounts of infrastructure to study these models, further democratizing access in this important, fast-changing field. Training smaller foundation models like Llama is desirable in the large language model space because it requires far less computing power and resources to test new approaches, validate others’ work, and explore new use cases. Foundation models train on a large set of unlabeled data, which makes them ideal for fine-tuning for a variety of tasks. We are making Llama available at several sizes (7B, 13B, 33B, and 65B parameters) and also sharing a Llama model card that details how we built the model in keeping with our approach to Responsible AI practices. -
45
Arches AI
Arches AI
Arches AI provides tools to craft chatbots, train custom models, and generate AI-based media, all tailored to your unique needs. Deploy LLMs, stable diffusion models, and more with ease. An large language model (LLM) agent is a type of artificial intelligence that uses deep learning techniques and large data sets to understand, summarize, generate and predict new content. Arches AI works by turning your documents into what are called 'word embeddings'. These embeddings allow you to search by semantic meaning instead of by the exact language. This is incredibly useful when trying to understand unstructed text information, such as textbooks, documentation, and others. With strict security rules in place, your information is safe from hackers and other bad actors. All documents can be deleted through on the 'Files' page.Starting Price: $12.99 per month -
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Cloudflare Vectorize
Cloudflare
Begin building for free in minutes. Vectorize enables fast & cost-effective vector storage to power your search & AI Retrieval Augmented Generation (RAG) applications. Avoid tool sprawl & reduce total cost of ownership, Vectorize seamlessly integrates with Cloudflare’s AI developer platform and AI gateway for centralized development, monitoring & control of AI applications on a global scale. Vectorize is a globally distributed vector database that enables you to build full-stack, AI-powered applications with Cloudflare Workers AI. Vectorize makes querying embeddings, representations of values or objects like text, images, and audio that are designed to be consumed by machine learning models and semantic search algorithms, faster, easier, and more affordable. Search, similarity, recommendation, classification & anomaly detection based on your own data. Improved results & faster search. String, number & boolean types are supported. -
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Keras
Keras
Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages. It also has extensive documentation and developer guides. Keras is the most used deep learning framework among top-5 winning teams on Kaggle. Because Keras makes it easier to run new experiments, it empowers you to try more ideas than your competition, faster. And this is how you win. Built on top of TensorFlow 2.0, Keras is an industry-strength framework that can scale to large clusters of GPUs or an entire TPU pod. It's not only possible; it's easy. Take advantage of the full deployment capabilities of the TensorFlow platform. You can export Keras models to JavaScript to run directly in the browser, to TF Lite to run on iOS, Android, and embedded devices. It's also easy to serve Keras models as via a web API. -
48
Brokk
Brokk
Brokk is an AI-native code assistant built to handle large, complex codebases by giving language models compiler-grade understanding of code structure, semantics, and dependencies. It enables context management by selectively loading summaries, diffs, or full files into a workspace so that the AI sees just the relevant portions of a million-line codebase rather than everything. Brokk supports actions such as Quick Context, which suggests files to include based on embeddings and structural relevance; Deep Scan, which uses more powerful models to recommend which files to edit or summarize further; and Agentic Search, allowing multi-step exploration of symbols, call graphs, or usages across the project. The architecture is grounded in static analysis via Joern (offering type inference beyond simple ASTs) and uses JLama for fast embedding inference to guide context changes. Brokk is offered as a standalone Java application (not an IDE plugin) to let users supervise AI workflows clearly.Starting Price: $20 per month -
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Haystack
deepset
Apply the latest NLP technology to your own data with the use of Haystack's pipeline architecture. Implement production-ready semantic search, question answering, summarization and document ranking for a wide range of NLP applications. Evaluate components and fine-tune models. Ask questions in natural language and find granular answers in your documents using the latest QA models with the help of Haystack pipelines. Perform semantic search and retrieve ranked documents according to meaning, not just keywords! Make use of and compare the latest pre-trained transformer-based languages models like OpenAI’s GPT-3, BERT, RoBERTa, DPR, and more. Build semantic search and question-answering applications that can scale to millions of documents. Building blocks for the entire product development cycle such as file converters, indexing functions, models, labeling tools, domain adaptation modules, and REST API. -
50
Deep Lake
activeloop
Generative AI may be new, but we've been building for this day for the past 5 years. Deep Lake thus combines the power of both data lakes and vector databases to build and fine-tune enterprise-grade, LLM-based solutions, and iteratively improve them over time. Vector search does not resolve retrieval. To solve it, you need a serverless query for multi-modal data, including embeddings or metadata. Filter, search, & more from the cloud or your laptop. Visualize and understand your data, as well as the embeddings. Track & compare versions over time to improve your data & your model. Competitive businesses are not built on OpenAI APIs. Fine-tune your LLMs on your data. Efficiently stream data from remote storage to the GPUs as models are trained. Deep Lake datasets are visualized right in your browser or Jupyter Notebook. Instantly retrieve different versions of your data, materialize new datasets via queries on the fly, and stream them to PyTorch or TensorFlow.Starting Price: $995 per month
