Alternatives to Dataset Finder

Compare Dataset Finder alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Dataset Finder in 2026. Compare features, ratings, user reviews, pricing, and more from Dataset Finder competitors and alternatives in order to make an informed decision for your business.

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    OORT DataHub

    OORT DataHub

    OORT DataHub

    Data Collection and Labeling for AI Innovation. Transform your AI development with our decentralized platform that connects you to worldwide data contributors. We combine global crowdsourcing with blockchain verification to deliver diverse, traceable datasets. Global Network: Ensure AI models are trained on data that reflects diverse perspectives, reducing bias, and enhancing inclusivity. Distributed and Transparent: Every piece of data is timestamped for provenance stored securely stored in the OORT cloud , and verified for integrity, creating a trustless ecosystem. Ethical and Responsible AI Development: Ensure contributors retain autonomy with data ownership while making their data available for AI innovation in a transparent, fair, and secure environment Quality Assured: Human verification ensures data meets rigorous standards Access diverse data at scale. Verify data integrity. Get human-validated datasets for AI. Reduce costs while maintaining quality. Scale globally.
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    Bitext

    Bitext

    Bitext

    Bitext provides multilingual, hybrid synthetic training datasets specifically designed for intent detection and LLM fine‑tuning. These datasets blend large-scale synthetic text generation with expert curation and linguistic annotation, covering lexical, syntactic, semantic, register, and stylistic variation, to enhance conversational models’ understanding, accuracy, and domain adaptation. For example, their open source customer‑support dataset features ~27,000 question–answer pairs (≈3.57 million tokens), 27 intents across 10 categories, 30 entity types, and 12 language‑generation tags, all anonymized to comply with privacy, bias, and anti‑hallucination standards. Bitext also offers vertical-specific datasets (e.g., travel, banking) and supports over 20 industries in multiple languages with more than 95% accuracy. Their hybrid approach ensures scalable, multilingual training data, privacy-compliant, bias-mitigated, and ready for seamless LLM improvement and deployment.
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    Dataocean AI

    Dataocean AI

    Dataocean AI

    DataOcean AI is a leading provider of high-quality, labeled training data and comprehensive AI data solutions, offering over 1,600 off‑the‑shelf datasets and thousands of customized datasets for machine learning and AI applications. Dataocean's offerings cover diverse modalities (speech, text, image, audio, video, multimodal) and support tasks such as ASR, TTS, NLP, OCR, computer vision, content moderation, machine translation, lexicon development, autonomous driving, and LLM fine‑tuning. It combines AI-driven techniques with human-in-the-loop (HITL) processes via their DOTS platform, which includes over 200 data-processing algorithms and hundreds of labeling tools for automation, assisted labeling, collection, cleaning, annotation, training, and model evaluation. With almost 20 years of experience and presence in more than 70 countries, DataOcean AI ensures strong quality, security, and compliance, serving over 1,000 enterprises and academic institutions globally.
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    Kled

    Kled

    Kled AI

    Kled is a secure, crypto-powered AI data marketplace that connects content rights holders with AI developers by providing high‑quality, ethically sourced datasets, spanning video, audio, music, text, transcripts, and behavioral data, for training generative AI models. It handles end-to-end licensing: it curates, labels, and rates datasets for accuracy and bias, manages contracts and payments securely, and offers custom dataset creation and discovery via a marketplace. Rights holders can upload original content, choose licensing terms, and earn KLED tokens, while developers gain access to premium data for responsible AI model training. Kled also supplies monitoring and recognition tools to ensure authorized usage and to detect misuse. Built for transparency and compliance, the system bridges IP owners and AI builders through a powerful yet user-friendly interface.
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    Twine AI

    Twine AI

    Twine.net

    Twine AI offers tailored speech, image, and video data collection and annotation services, including off‑the‑shelf and custom datasets, for training and fine‑tuning AI/ML models. It offers audio (voice recordings, transcription across 163+ languages and dialects), image and video (biometrics, object/scene detection, drone/satellite feeds), text, and synthetic data. Leveraging a vetted global crowd of 400,000–500,000 contributors, Twine ensures ethical, consent‑based collection and bias reduction with ISO 27001-level security and GDPR compliance. Projects are managed end‑to‑end through technical scoping, proofs of concept, and full delivery supported by dedicated project managers, version control, QA workflows, and secure payments across 190+ countries. Its service includes humans‑in‑the‑loop annotation, RLHF techniques, dataset versioning, audit trails, and full dataset management, enabling scalable, context‑rich training data for advanced computer vision.
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    Luel

    Luel

    Luel AI

    Luel is a two-sided AI training data marketplace that connects enterprises and AI teams with a global network of contributors to source, license, and generate high-quality multimodal datasets for machine learning models. It provides curated, rights-cleared datasets that are verified, structured, and ready for training, including video, audio, and image data tailored for use cases such as speech recognition, computer vision, and multimodal AI systems. It enables companies to either browse a catalog of existing datasets or request custom data collection campaigns by specifying detailed requirements such as format, labels, quality standards, and scenarios, which are then fulfilled through a vetted contributor network. Submissions undergo multi-stage validation and quality checks to ensure compliance, accuracy, and usability, delivering enterprise-ready datasets with full licensing and documentation.
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    AfterQuery

    AfterQuery

    AfterQuery

    AfterQuery is an applied research platform designed to create high-quality training data for frontier artificial intelligence models by capturing how real experts think, reason, and solve problems in professional contexts. It focuses on transforming real-world work into structured datasets that go beyond simple outputs, encoding decision-making processes, tradeoffs, and contextual reasoning that traditional internet-sourced data cannot provide. It works directly with domain experts to generate supervised fine-tuning data, including prompt–response pairs and detailed reasoning traces, as well as reinforcement learning datasets with expert-designed prompts and grading frameworks that convert subjective judgment into scalable reward signals. It also builds custom agent environments across APIs and tools, enabling models to be trained and evaluated in realistic workflows, and captures computer-use trajectories that demonstrate how humans interact with software step by step.
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    Synetic

    Synetic

    Synetic

    Synetic AI is a platform that accelerates the creation and deployment of real-world computer vision models by automatically generating photorealistic synthetic training datasets with pixel-perfect annotations and no manual labeling required, using advanced physics-based rendering and simulation to eliminate the traditional gap between synthetic and real-world data and achieve superior model performance. Its synthetic data has been independently validated to outperform real-world datasets by an average of 34% in generalization and recall, covering unlimited variations like lighting, weather, camera angles, and edge cases with comprehensive metadata, annotations, and multi-modal sensor support, enabling teams to iterate instantly and train models faster and cheaper than traditional approaches; Synetic AI supports common architectures and export formats, handles edge deployment and monitoring, and can deliver full datasets in about a week and custom trained models in a few weeks.
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    Pixta AI

    Pixta AI

    Pixta AI

    Pixta AI is a cutting‑edge, fully managed data‑annotation and dataset marketplace designed to connect data providers with companies and researchers needing high‑quality training data for AI, ML, and computer vision projects. It offers extensive coverage across modalities, visual, audio, OCR, and conversation, and provides tailored datasets in categories like face recognition, vehicle detection, human emotion, landscape, healthcare, and more. Leveraging a massive 100 million+ compliant visual data library from Pixta Stock and a team of experienced annotators, Pixta AI delivers scalable, ground‑truth annotation services (bounding boxes, landmarks, segmentation, attribute classification, OCR, etc.) that are 3–4× faster thanks to semi‑automated tools. It's a secure, compliant marketplace that facilitates on‑demand sourcing, ordering of custom datasets, and global delivery via S3, email, or API in formats like JSON, XML, CSV, and TXT, covering over 249 countries.
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    Gramosynth

    Gramosynth

    Rightsify

    Gramosynth is a powerful AI-driven platform for generating high-quality synthetic music datasets tailored for training next-gen AI models. Leveraging Rightsify’s vast corpus, the system operates on a perpetual data flywheel that continuously ingests freshly released music to generate realistic, copyright-safe audio at professional 48 kHz stereo quality. Datasets include rich, ground-truth metadata such as instrument, genre, tempo, key, and more, structured specifically for advanced model training. It accelerates data collection timelines by up to 99.9%, eliminates licensing bottlenecks, and supports virtually limitless scaling. Integration is seamless via a simple API that allows users to define parameters like genre, mood, instruments, duration, and stems, producing fully annotated datasets with unprocessed stems, FLAC audio, alongside outputs in JSON or CSV formats.
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    DataGen

    DataGen

    DataGen

    DataGen is a leading AI platform specializing in synthetic data generation and custom generative AI models for machine learning projects. Their flagship product, SynthEngyne, supports multi-format data generation including text, images, tabular, and time-series data, ensuring privacy-compliant, high-quality training datasets. The platform offers scalable, real-time processing and advanced quality controls like deduplication to maintain dataset fidelity. DataGen also provides professional AI development services such as model deployment, fine-tuning, synthetic data consulting, and intelligent automation systems. With flexible pricing plans ranging from free tiers for individuals to custom enterprise solutions, DataGen caters to a wide range of users. Their solutions serve diverse industries including healthcare, finance, automotive, and retail.
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    DataSeeds.AI

    DataSeeds.AI

    DataSeeds.AI

    DataSeeds.ai provides large‑scale, ethically sourced, high‑quality image (and video) datasets tailored for AI training, combining both off‑the‑shelf collections and on‑demand custom builds. Their ready‑to‑use photo sets include millions of images fully annotated with EXIF metadata, content labels, bounding boxes, expert aesthetic scores, scene context, pixel‑level masks, and more. It supports object and scene detection tasks, global coverage, and human‑peer‑ranking for label accuracy. Custom datasets can be launched rapidly via a global contributor network in 160+ countries, collecting images that align with specific technical or thematic requirements. Accompanying annotations include descriptive titles, detailed scene context, camera settings (type, model, lens, exposure, ISO), environmental attributes, and optional geo/contextual tags.
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    Shaip

    Shaip

    Shaip

    Shaip offers end-to-end generative AI services, specializing in high-quality data collection and annotation across multiple data types including text, audio, images, and video. The platform sources and curates diverse datasets from over 60 countries, supporting AI and machine learning projects globally. Shaip provides precise data labeling services with domain experts ensuring accuracy in tasks like image segmentation and object detection. It also focuses on healthcare data, delivering vast repositories of physician audio, electronic health records, and medical images for AI training. With multilingual audio datasets covering 60+ languages and dialects, Shaip enhances conversational AI development. The company ensures data privacy through de-identification services, protecting sensitive information while maintaining data utility.
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    DataHive AI

    DataHive AI

    DataHive AI

    DataHive provides high-quality, fully rights-owned datasets across text, image, video, and audio to power modern AI development. The platform sources, creates, and labels data through a global contributor network, ensuring accuracy, diversity, and commercial readiness. DataHive offers specialized datasets including e-commerce listings, customer reviews, multilingual speech, transcribed audio, global video collections, and original photo libraries. Each dataset is enriched with metadata such as pricing, sentiment, tags, engagement metrics, and contextual information. These resources support a wide range of use cases, from computer vision and ASR training to retail analytics, sentiment modeling, and entertainment AI research. Trusted by startups and Fortune 500 companies, DataHive is built to accelerate high-performance machine learning with reliable, scalable data.
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    Keymakr

    Keymakr

    Keymakr

    Keymakr provides image and video data annotation, along with data creation, collection, and validation services for AI and machine learning computer vision projects of any scale. The company’s core expertise lies in delivering high-quality training data for multimodal and embodied AI systems, and supporting human-verified annotation and LLM ground-truth validation of model outputs. Keymakr's motto, "Human teaching for machine learning," reflects its commitment to the human-in-the-loop approach. This is why the company maintains an in-house team of over 600 highly skilled annotators. Keymakr's goal is to deliver custom datasets that enhance the accuracy and efficiency of ML systems. To create precise datasets, Keymakr developed Keylabs.ai, a powerful enterprise-grade annotation platform that supports all annotation types. Keymakr also follows strict data security and compliance standards, holds ISO 9001 and ISO 27001 certifications, and maintains GDPR and HIPAA compliance.
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    TagX

    TagX

    TagX

    TagX delivers comprehensive data and AI solutions, offering services like AI model development, generative AI, and a full data lifecycle including collection, curation, web scraping, and annotation across modalities (image, video, text, audio, 3D/LiDAR), as well as synthetic data generation and intelligent document processing. TagX's division specializes in building, fine‑tuning, deploying, and managing multimodal models (GANs, VAEs, transformers) for image, video, audio, and language tasks. It supports robust APIs for real‑time financial and employment intelligence. With GDPR, HIPAA compliance, and ISO 27001 certification, TagX serves industries from agriculture and autonomous driving to finance, logistics, healthcare, and security, delivering privacy‑aware, scalable, customizable AI datasets and models. Its end‑to‑end approach, from annotation guidelines and foundational model selection to deployment and monitoring, helps enterprises automate documentation.
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    Scale Data Engine
    Scale Data Engine helps ML teams build better datasets. Bring together your data, ground truth, and model predictions to effortlessly fix model failures and data quality issues. Optimize your labeling spend by identifying class imbalance, errors, and edge cases in your data with Scale Data Engine. Significantly improve model performance by uncovering and fixing model failures. Find and label high-value data by curating unlabeled data with active learning and edge case mining. Curate the best datasets by collaborating with ML engineers, labelers, and data ops on the same platform. Easily visualize and explore your data to quickly find edge cases that need labeling. Check how well your models are performing and always ship the best one. Easily view your data, metadata, and aggregate statistics with rich overlays, using our powerful UI. Scale Data Engine supports visualization of images, videos, and lidar scenes, overlaid with all associated labels, predictions, and metadata.
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    GCX

    GCX

    Rightsify

    GCX (Global Copyright Exchange) is a dataset licensing service for AI‑driven music, offering ethically sourced and copyright‑cleared premium datasets ideal for tasks like music generation, source separation, music recommendation, and MIR. Launched by Rightsify in 2023, it provides over 4.4 million hours of audio and 32 billion metadata-text pairs, totaling more than 3 petabytes, comprising MIDI, stems, and WAV files with rich descriptive metadata (key, tempo, instrumentation, chord progressions, etc.). Datasets can be licensed “as is” or customized by genre, culture, instruments, and more, with full commercial indemnification. GCX bridges creators, rights holders, and AI developers by streamlining licensing and ensuring legal compliance. It supports perpetual use, unlimited editing, and is recognized for excellence by Datarade. Use cases include generative AI, research, and multimedia production.
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    OpenPipe

    OpenPipe

    OpenPipe

    OpenPipe provides fine-tuning for developers. Keep your datasets, models, and evaluations all in one place. Train new models with the click of a button. Automatically record LLM requests and responses. Create datasets from your captured data. Train multiple base models on the same dataset. We serve your model on our managed endpoints that scale to millions of requests. Write evaluations and compare model outputs side by side. Change a couple of lines of code, and you're good to go. Simply replace your Python or Javascript OpenAI SDK and add an OpenPipe API key. Make your data searchable with custom tags. Small specialized models cost much less to run than large multipurpose LLMs. Replace prompts with models in minutes, not weeks. Fine-tuned Mistral and Llama 2 models consistently outperform GPT-4-1106-Turbo, at a fraction of the cost. We're open-source, and so are many of the base models we use. Own your own weights when you fine-tune Mistral and Llama 2, and download them at any time.
    Starting Price: $1.20 per 1M tokens
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    Maxim

    Maxim

    Maxim

    Maxim is an agent simulation, evaluation, and observability platform that empowers modern AI teams to deploy agents with quality, reliability, and speed. Maxim's end-to-end evaluation and data management stack covers every stage of the AI lifecycle, from prompt engineering to pre & post release testing and observability, data-set creation & management, and fine-tuning. Use Maxim to simulate and test your multi-turn workflows on a wide variety of scenarios and across different user personas before taking your application to production. Features: Agent Simulation Agent Evaluation Prompt Playground Logging/Tracing Workflows Custom Evaluators- AI, Programmatic and Statistical Dataset Curation Human-in-the-loop Use Case: Simulate and test AI agents Evals for agentic workflows: pre and post-release Tracing and debugging multi-agent workflows Real-time alerts on performance and quality Creating robust datasets for evals and fine-tuning Human-in-the-loop workflows
    Starting Price: $29/seat/month
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    thinkdeeply

    thinkdeeply

    Think Deeply

    Discover from a variety of assets to jump-start your AI project. The AI hub provides a rich collection of artifacts that your project may need - industry AI starter kits, datasets, notebooks, pre-trained models, deployment-ready solutions & pipelines. Get access to the best resources from external parties, or created by your organization. Prepare and manage your data for model training. Collect, organize, tag, or select features, and prepare datasets for training with simple drag and drop UI. Collaborate with multiple team members to tag large datasets. Implement a quality control process to ensure dataset quality. Build models with simple clicks using the model wizards. No data science knowledge required. The system selects the best models for the problem and optimizes their training parameters. Advanced users, however, can fine-tune the models and their hyper-parameters. One-click deployment to production inference enviornments.
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    FinetuneDB

    FinetuneDB

    FinetuneDB

    Capture production data, evaluate outputs collaboratively, and fine-tune your LLM's performance. Know exactly what goes on in production with an in-depth log overview. Collaborate with product managers, domain experts and engineers to build reliable model outputs. Track AI metrics such as speed, quality scores, and token usage. Copilot automates evaluations and model improvements for your use case. Create, manage, and optimize prompts to achieve precise and relevant interactions between users and AI models. Compare foundation models, and fine-tuned versions to improve prompt performance and save tokens. Collaborate with your team to build a proprietary fine-tuning dataset for your AI models. Build custom fine-tuning datasets to optimize model performance for specific use cases.
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    Oxen.ai

    Oxen.ai

    Oxen.ai

    Oxen.ai is a collaborative data platform built to help teams manage, version, and operationalize machine learning datasets from initial curation through model deployment. At its core, the system provides a high-performance data version control engine optimized for large and complex datasets, allowing teams to version, branch, and share datasets, model weights, and experiments efficiently. It enables stakeholders across machine learning engineering, data science, product, and legal teams to review, edit, and collaborate on data within a unified workflow. Users can query, modify, and manage datasets through an intuitive web interface, command line tools, or a Python library, making it flexible for different technical workflows. Oxen.ai supports the full AI lifecycle by allowing teams to curate datasets, fine-tune models, and deploy them at scale while maintaining full ownership and traceability.
    Starting Price: $30 per month
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    Cleanlab

    Cleanlab

    Cleanlab

    Cleanlab Studio handles the entire data quality and data-centric AI pipeline in a single framework for analytics and machine learning tasks. Automated pipeline does all ML for you: data preprocessing, foundation model fine-tuning, hyperparameter tuning, and model selection. ML models are used to diagnose data issues, and then can be re-trained on your corrected dataset with one click. Explore the entire heatmap of suggested corrections for all classes in your dataset. Cleanlab Studio provides all of this information and more for free as soon as you upload your dataset. Cleanlab Studio comes pre-loaded with several demo datasets and projects, so you can check those out in your account after signing in.
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    StableVicuna

    StableVicuna

    Stability AI

    StableVicuna is the first large-scale open source chatbot trained via reinforced learning from human feedback (RHLF). StableVicuna is a further instruction fine tuned and RLHF trained version of Vicuna v0 13b, which is an instruction fine tuned LLaMA 13b model. In order to achieve StableVicuna’s strong performance, we utilize Vicuna as the base model and follow the typical three-stage RLHF pipeline outlined by Steinnon et al. and Ouyang et al. Concretely, we further train the base Vicuna model with supervised finetuning (SFT) using a mixture of three datasets: OpenAssistant Conversations Dataset (OASST1), a human-generated, human-annotated assistant-style conversation corpus comprising 161,443 messages distributed across 66,497 conversation trees, in 35 different languages; GPT4All Prompt Generations, a dataset of 437,605 prompts and responses generated by GPT-3.5 Turbo; And Alpaca, a dataset of 52,000 instructions and demonstrations generated by OpenAI's text-davinci-003.
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    Lightning Rod

    Lightning Rod

    Lightning Rod

    Lightning Rod is an AI platform designed to transform messy, unstructured real-world data into verified, production-ready training datasets and domain-specific AI models without requiring manual labeling. It enables users to generate high-quality, citable question–answer pairs from sources such as news articles, financial filings, and internal documents, turning raw historical data into structured datasets that can be used for supervised fine-tuning or reinforcement learning. It operates through an agent-driven workflow where users describe their goal, and the system automatically gathers sources, generates questions, resolves outcomes based on real-world events, and adds contextual grounding before training a model. A key innovation is its “future-as-label” methodology, which uses actual outcomes as training signals, allowing AI systems to learn directly from real-world results at scale instead of relying on synthetic or manually annotated data.
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    Innovatiana

    Innovatiana

    Innovatiana

    Innovatiana is a data labeling and AI dataset preparation platform designed to transform raw data into high-quality, structured training datasets for machine learning and generative AI systems. It provides an end-to-end solution for collecting, annotating, structuring, and enriching data within a unified environment, enabling organizations to centralize all data preparation needs for AI projects in one place. It supports a wide range of data types, including images, videos, text, audio, and multimodal data, and delivers annotated datasets in multiple formats ready for use in machine learning, deep learning, and large language model training. Its approach combines human expertise with structured methodologies and automated or semi-automated quality controls to ensure accuracy, consistency, and reliability across large-scale datasets.
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    Syphoon

    Syphoon

    Syphoon

    Syphoon is a web scraping API platform that turns hard-to-access web data into structured, reliable feeds for social listening, competitive intelligence, market research, and AI/LLM training pipelines. Our social media scraper APIs power social listening platforms, lead generation agencies, and job search tools. Our e-commerce scraper APIs support competitive intelligence, product launch research, price monitoring, and AI training datasets. We serve quick commerce platforms such as Zepto, Blinkit, Tata 1mg, and Instamart for competitive benchmarking, launch planning, and sales inference. Syphoon also powers scraper APIs for travel, MRO, electronics, and other verticals. Beyond these, we support teams building AI, LLM, and voice model training datasets, giving AI infra and ML teams structured, large-scale data pipelines for training and fine-tuning.
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    Defined.ai

    Defined.ai

    Defined.ai

    Defined.ai provides high-quality training data, tools, and models to AI professionals to power their AI projects. With resources in speech, NLP, translation, and computer vision, AI professionals can look to Defined.ai as a resource to get complex AI and machine learning projects to market quickly and efficiently. We host the leading AI marketplace, where data scientists, machine learning engineers, academics, and others can buy and sell off-the-shelf datasets, tools, and models. We also provide customizable workflows with tailor-made solutions to improve any AI project. Quality is at the core of everything we do, and we are in compliance with industry privacy standards and best practices. We also have a passion and mission to ensure that our data is ethically collected, transparently presented, and representative – since AI often reflects of our own human biases, it’s necessary to make efforts to prevent as much bias as possible, and our practices reflect that.
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    Amazon Nova Forge
    Amazon Nova Forge is a groundbreaking service that enables organizations to build their own frontier models by leveraging early Nova checkpoints and proprietary data. It provides complete flexibility across the full training lifecycle, including pre-training, mid-training, supervised fine-tuning, and reinforcement learning. With access to Nova-curated datasets and responsible AI tooling, customers can create powerful and safer custom models tailored to their domain. Nova Forge allows teams to mix their own datasets at the peak learning stage to maximize accuracy while preventing catastrophic forgetting. Companies across industries—from Reddit to Sony—use Nova Forge to consolidate ML workflows, accelerate innovation, and outperform specialized models. Hosted securely on AWS, it offers the most cost-effective, streamlined path to building next-generation AI systems.
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    prompteasy.ai

    prompteasy.ai

    prompteasy.ai

    You can now fine-tune GPT with absolutely zero technical skills. Enhance AI models by tailoring them to your specific needs. Prompteasy.ai helps you fine-tune AI models in a matter of seconds. We make AI tailored to your needs by helping you fine-tune it. The best part is, that you don't even have to know AI fine-tuning. Our AI models will take care of everything. We will be offering prompteasy for free as part of our initial launch. We'll be rolling out pricing plans later this year. Our vision is to make AI smart and easily accessible to anyone. We believe that the true power of AI lies in how we train and orchestrate the foundational models, as opposed to just using them off the shelf. Forget generating massive datasets, just upload relevant materials and interact with our AI through natural language. We take care of building the dataset ready for fine-tuning. You just chat with the AI, download the dataset, and fine-tune GPT.
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    Azure Open Datasets
    Improve the accuracy of your machine learning models with publicly available datasets. Save time on data discovery and preparation by using curated datasets that are ready to use in machine learning workflows and easy to access from Azure services. Account for real-world factors that can impact business outcomes. By incorporating features from curated datasets into your machine learning models, improve the accuracy of predictions and reduce data preparation time. Share datasets with a growing community of data scientists and developers. Deliver insights at hyperscale using Azure Open Datasets with Azure’s machine learning and data analytics solutions. There's no additional charge for using most Open Datasets. Pay only for Azure services consumed while using Open Datasets, such as virtual machine instances, storage, networking resources, and machine learning. Curated open data made easily accessible on Azure.
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    OCI Data Labeling
    OCI Data Labeling is a service that enables developers and data scientists to build accurately labelled datasets for training AI and machine-learning models. It supports documents (PDF, TIFF), images (JPEG, PNG), and text, allowing users to upload raw data, apply annotations (such as classification labels, object-detection bounding boxes, or key-value pairs), and export the results in line-delimited JSON for seamless integration into model-training workflows. The service offers custom templates for different annotation formats, user interfaces, and public APIs for dataset creation and management, and smooth interoperability with other data and AI services, so annotated data can feed directly into custom vision or language models, as well as Oracle’s AI services. OCI Data Labeling lets users create a dataset, generate records, annotate them, and then use the export snapshot for model development.
    Starting Price: $0.0002 per 1,000 transactions
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    Vaalti

    Vaalti

    Vaalti

    Vaalti is a B2B sales intelligence, lead generation and contact data enrichment platform for sales, recruiting and GTM teams. It centralizes contact data in a reusable vault where users can import datasets, remove duplicates, search with 110+ contact and company filters, enrich missing information and export clean prospect lists. Built-in tools include Email Finder, Email Verifier, LinkedIn Phone Finder, Google Maps Scraper, Website Finder and Website Extractor, plus bulk CSV/Excel processing, tagging and saved lists. Vaalti offers a free plan and 6- or 12-month paid plans with unlimited platform usage, helping teams reduce dependence on fragmented credit-based data tools.
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    AI Verse

    AI Verse

    AI Verse

    When real-life data capture is challenging, we generate diverse, fully labeled image datasets. Our procedural technology ensures the highest quality, unbiased, labeled synthetic datasets that will improve your computer vision model’s accuracy. AI Verse empowers users with full control over scene parameters, ensuring you can fine-tune the environments for unlimited image generation, giving you an edge in the competitive landscape of computer vision development.
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    Bakery

    Bakery

    Bakery

    Easily fine-tune & monetize your AI models with one click. For AI startups, ML engineers, and researchers. Bakery is a platform that enables AI startups, machine learning engineers, and researchers to fine-tune and monetize AI models with ease. Users can create or upload datasets, adjust model settings, and publish their models on the marketplace. The platform supports various model types and provides access to community-driven datasets for project development. Bakery's fine-tuning process is streamlined, allowing users to build, test, and deploy models efficiently. The platform integrates with tools like Hugging Face and supports decentralized storage solutions, ensuring flexibility and scalability for diverse AI projects. The bakery empowers contributors to collaboratively build AI models without exposing model parameters or data to one another. It ensures proper attribution and fair revenue distribution to all contributors.
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    Anyverse

    Anyverse

    Anyverse

    A flexible and accurate synthetic data generation platform. Craft the data you need for your perception system in minutes. Design scenarios for your use case with endless variations. Generate your datasets in the cloud. Anyverse offers a scalable synthetic data software platform to design, train, validate, or fine-tune your perception system. It provides unparalleled computing power in the cloud to generate all the data you need in a fraction of the time and cost compared with other real-world data workflows. Anyverse provides a modular platform that enables efficient scene definition and dataset production. Anyverse™ Studio is a standalone graphical interface application that manages all Anyverse functions, including scenario definition, variability settings, asset behaviors, dataset settings, and inspection. Data is stored in the cloud, and the Anyverse cloud engine is responsible for final scene generation, simulation, and rendering.
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    SuperAnnotate

    SuperAnnotate

    SuperAnnotate

    SuperAnnotate is the world's leading platform for building the highest quality training datasets for computer vision and NLP. With advanced tooling and QA, ML and automation features, data curation, robust SDK, offline access, and integrated annotation services, we enable machine learning teams to build incredibly accurate datasets and successful ML pipelines 3-5x faster. By bringing our annotation tool and professional annotators together we've built a unified annotation environment, optimized to provide integrated software and services experience that leads to higher quality data and more efficient data pipelines.
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    OpenEuroLLM

    OpenEuroLLM

    OpenEuroLLM

    OpenEuroLLM is a collaborative initiative among Europe's leading AI companies and research institutions to develop a series of open-source foundation models for transparent AI in Europe. The project emphasizes transparency by openly sharing data, documentation, training, testing code, and evaluation metrics, fostering community involvement. It ensures compliance with EU regulations, aiming to provide performant large language models that align with European standards. A key focus is on linguistic and cultural diversity, extending multilingual capabilities to encompass all EU official languages and beyond. The initiative seeks to enhance access to foundational models ready for fine-tuning across various applications, expand evaluation results in multiple languages, and increase the availability of training datasets and benchmarks. Transparency is maintained throughout the training processes by sharing tools, methodologies, and intermediate results.
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    Lilac

    Lilac

    Lilac

    Lilac is an open source tool that enables data and AI practitioners to improve their products by improving their data. Understand your data with powerful search and filtering. Collaborate with your team on a single, centralized dataset. Apply best practices for data curation, like removing duplicates and PII to reduce dataset size and lower training cost and time. See how your pipeline impacts your data using our diff viewer. Clustering is a technique that automatically assigns categories to each document by analyzing the text content and putting similar documents in the same category. This reveals the overarching structure of your dataset. Lilac uses state-of-the-art algorithms and LLMs to cluster the dataset and assign informative, descriptive titles. Before we do advanced searching, like concept or semantic search, we can immediately use keyword search by typing a keyword in the search box.
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    Axolotl

    Axolotl

    Axolotl

    ​Axolotl is an open source tool designed to streamline the fine-tuning of various AI models, offering support for multiple configurations and architectures. It enables users to train models, supporting methods like full fine-tuning, LoRA, QLoRA, ReLoRA, and GPTQ. Users can customize configurations using simple YAML files or command-line interface overrides, and load different dataset formats, including custom or pre-tokenized datasets. Axolotl integrates with technologies like xFormers, Flash Attention, Liger kernel, RoPE scaling, and multipacking, and works with single or multiple GPUs via Fully Sharded Data Parallel (FSDP) or DeepSpeed. It can be run locally or on the cloud using Docker and supports logging results and checkpoints to several platforms. It is designed to make fine-tuning AI models friendly, fast, and fun, without sacrificing functionality or scale.
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    Superb AI

    Superb AI

    Superb AI

    Superb AI provides a new generation machine learning data platform to AI teams so that they can build better AI in less time. The Superb AI Suite is an enterprise SaaS platform built to help ML engineers, product teams, researchers and data annotators create efficient training data workflows, saving time and money. Majority of ML teams spend more than 50% of their time managing training datasets Superb AI can help. On average, our customers have reduced the time it takes to start training models by 80%. Fully managed workforce, powerful labeling tools, training data quality control, pre-trained model predictions, advanced auto-labeling, filter and search your datasets, data source integration, robust developer tools, ML workflow integrations, and much more. Training data management just got easier with Superb AI. Superb AI offers enterprise-level features for every layer in an ML organization.
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    Sapien

    Sapien

    Sapien

    High-quality training data is essential for all large language models, whether you build the data yourself or use pre-existing models. A human-in-the-loop labeling process delivers real-time feedback for fine-tuning datasets to build the most performant and differentiated AI models. We provide precise data labeling with faster human input to enhance the robustness and input diversity to improve the adaptability of LLMs for your enterprise applications. Our labeler management allows us to segment teams— you only pay for the level of experience and skill sets your data labelling project requires. Sapien can quickly scale labelling operations up and down for annotation projects large and small. Human intelligence at scale. We can customize labeling models to handle your specific data types, formats, and annotation requirements.
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    BasicAI

    BasicAI

    BasicAI

    BasicAI is a smart data annotation platform and managed labeling service provider that helps organizations create high-quality training data for artificial intelligence and machine learning models. The platform offers annotation services for 3D LiDAR, image data, audio and video tagging, NLP datasets, and RLHF and SFT dataset creation to support a wide range of AI applications. BasicAI combines AI-powered annotation tools, enterprise project management features, and specialized global annotation teams to deliver precise and scalable data labeling workflows. The company provides both managed labeling services and private deployment annotation platforms designed for organizations that require greater control over data security and processing environments. BasicAI supports industries such as automotive, robotics, logistics, manufacturing, agriculture, construction, smart cities, and healthcare with customized annotation solutions.
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    T-Rex Label

    T-Rex Label

    T-Rex Label

    T-Rex Label is an intelligent tool designed for complex scenario annotation, applicable across various industries. It is the go-to option for those aiming to streamline their workflows and effortlessly create high-quality datasets. Leveraging the power of visual prompts, T-Rex allows for the quick prediction of numerous bounding boxes in a single step, making it ideal for annotating complex and dense scenes. Leveraging its exceptional zero-shot detection capability, T-Rex empowers complex scene annotation across industries without fine-tuning, supporting diverse applications ranging from agriculture to logistics and beyond. T-Rex assists a growing number of algorithm engineers and researchers in speeding up their annotation workflows, enabling the creation of high-quality datasets. T-Rex2 represents a significant step towards more generic and flexible object detection, leveraging the complementary strengths of language and vision.
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    Ximilar

    Ximilar

    Ximilar

    Ximilar is the first MLaaS platform for training and fine-tuning vision-language models without coding, enabling multimodal AI without in-house research teams. Build and train custom models on your own image and text data, then deploy via a single API click. Chain multiple models into automated workflows using Flows. Key capabilities: — Vision-language model fine-tuning on custom datasets — Image classification, annotation, and object detection — Visual search handling thousands of queries per second — Text-to-image search using natural language queries — Automated tagging and product description generation — OCR and text extraction from images — Fashion AI for apparel tagging and visual search — Defect detection for manufacturing and quality control — Classification, grading, and pricing of collectible items Built on Intel Xeon® with TensorFlow and OpenVINO. Deploy via API or offline. GDPR-compliant, EU servers. 15B+ images processed. Clients in 40+ countries.
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    Oumi

    Oumi

    Oumi

    Oumi is a fully open source platform that streamlines the entire lifecycle of foundation models, from data preparation and training to evaluation and deployment. It supports training and fine-tuning models ranging from 10 million to 405 billion parameters using state-of-the-art techniques such as SFT, LoRA, QLoRA, and DPO. The platform accommodates both text and multimodal models, including architectures like Llama, DeepSeek, Qwen, and Phi. Oumi offers tools for data synthesis and curation, enabling users to generate and manage training datasets effectively. For deployment, it integrates with popular inference engines like vLLM and SGLang, ensuring efficient model serving. The platform also provides comprehensive evaluation capabilities across standard benchmarks to assess model performance. Designed for flexibility, Oumi can run on various environments, from local laptops to cloud infrastructures such as AWS, Azure, GCP, and Lambda.
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    Llama 2
    The next generation of our open source large language model. This release includes model weights and starting code for pretrained and fine-tuned Llama language models — ranging from 7B to 70B parameters. Llama 2 pretrained models are trained on 2 trillion tokens, and have double the context length than Llama 1. Its fine-tuned models have been trained on over 1 million human annotations. Llama 2 outperforms other open source language models on many external benchmarks, including reasoning, coding, proficiency, and knowledge tests. Llama 2 was pretrained on publicly available online data sources. The fine-tuned model, Llama-2-chat, leverages publicly available instruction datasets and over 1 million human annotations. We have a broad range of supporters around the world who believe in our open approach to today’s AI — companies that have given early feedback and are excited to build with Llama 2.
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    Tuning Engines

    Tuning Engines

    CerebrixOS

    Tuning Engines is a unified AI control and governance layer for teams building production intelligence across models, agents, tools, and fine-tuned systems. It brings together the full AI lifecycle in one governed platform: inference, model routing, fallback policies, fine-tuning jobs, datasets, evaluations, model imports and exports, custom models, agents, MCP servers, reusable skills, guardrails, AGT YAML policies, data capture, runtime traces, usage analytics, API keys, billing, team roles, and integrations. Developers get OpenAI-compatible APIs, Anthropic-compatible routes, CLI workflows, MCP access, coding-agent integrations, and resource catalogs for models, agents, tools, and skills. Teams can connect Claude Code, OpenCode, Aider, Cline, Roo, Continue.dev, Cursor, VS Code, Windsurf, and other AI workflows through a single governed platform.
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    Utelly

    Utelly

    Synamedia Utelly

    Metadata aggregation, AI/ML enrichments, search & recommendation APIs, CMS, and promotion engine: Utelly brings the best content discovery toolkit for TV & OTT clients. We ingest core metadata catalogs to provide a universal view of the content available, along with ingesting individual feeds which are matched with the core metadata to provide an enriched unified dataset ready for powering content discovery. Our AI enrichment modules allow sparse data sets to be enhanced and then used to achieve improved content discovery experiences. Our search can be indexed on individual catalogs or a universal dataset, to provide an entertainment-focused search capability which is a future-proof approach to providing your customers with a great search experience. Our powerful recommendation engine leverages the latest ML/AI techniques to generate personalized recommendations based on key indicators identified throughout a user life cycle along with ingesting datasets.