Alternatives to Goodfire AI
Compare Goodfire AI alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Goodfire AI in 2026. Compare features, ratings, user reviews, pricing, and more from Goodfire AI competitors and alternatives in order to make an informed decision for your business.
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1
TensorFlow
TensorFlow
An end-to-end open source machine learning platform. TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. Build and train ML models easily using intuitive high-level APIs like Keras with eager execution, which makes for immediate model iteration and easy debugging. Easily train and deploy models in the cloud, on-prem, in the browser, or on-device no matter what language you use. A simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication faster. Build, deploy, and experiment easily with TensorFlow.Starting Price: Free -
2
Encord
Encord
Achieve peak model performance with the best data. Create & manage training data for any visual modality, debug models and boost performance, and make foundation models your own. Expert review, QA and QC workflows help you deliver higher quality datasets to your artificial intelligence teams, helping improve model performance. Connect your data and models with Encord's Python SDK and API access to create automated pipelines for continuously training ML models. Improve model accuracy by identifying errors and biases in your data, labels and models. -
3
Cerbrec Graphbook
Cerbrec
Construct your model directly as a live, interactive graph. Preview data flowing through your visualized model architecture. View and edit your visualized model architecture down to the atomic level. Graphbook provides X-ray transparency with no black boxes. Graphbook live checks data type and shape with understandable error messages, making your model debugging quick and easy. Abstracting out software dependencies and environment configuration, Graphbook allows you to focus on model architecture and data flow with the handy computing resources needed. Cerbrec Graphbook is a visual IDE for AI modeling, transforming cumbersome model development into a user-friendly experience. With a growing community of machine learning engineers and data scientists, Graphbook helps developers work with their text and tabular data to fine-tune language models such as BERT and GPT. Everything is fully managed out of the box so you can preview your model exactly as it will behave. -
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BioSymetrics
BioSymetrics
We integrate clinical and experimental data using machine learning to navigate human disease biology and advance precision medicines. Our patent-pending Contingent AI™ understands relationships within the data to provide sophisticated insights. We address data bias by iterating on machine learning models based upon decisions made in the pre-processing and feature engineering stages. We leverage zebrafish, cellular and other phenotypic animal models to validate in silico predictions in vivo experiments and genetically modify them in vitro and in vivo, to improve translation. Using active learning and computer vision on validated models for cardiac, central nervous system and rare disorders, we rapidly incorporate new data into our machine learning models. -
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Entry Point AI
Entry Point AI
Entry Point AI is the modern AI optimization platform for proprietary and open source language models. Manage prompts, fine-tunes, and evals all in one place. When you reach the limits of prompt engineering, it’s time to fine-tune a model, and we make it easy. Fine-tuning is showing a model how to behave, not telling. It works together with prompt engineering and retrieval-augmented generation (RAG) to leverage the full potential of AI models. Fine-tuning can help you to get better quality from your prompts. Think of it like an upgrade to few-shot learning that bakes the examples into the model itself. For simpler tasks, you can train a lighter model to perform at or above the level of a higher-quality model, greatly reducing latency and cost. Train your model not to respond in certain ways to users, for safety, to protect your brand, and to get the formatting right. Cover edge cases and steer model behavior by adding examples to your dataset.Starting Price: $49 per month -
6
Xero.AI
Xero.AI
Building an AI-powered machine learning engineer that can handle all your data science and ML needs. Xero's artificial analyst is the future of data science and ML. Just ask Xara what you want to do with your data and she will do it for you. Explore your data and create custom visuals using natural language to help you better understand your data and generate insights. Clean and transform your data and extract new features in the most seamless way possible. Create, train, and test unlimited customizable machine learning models by simply asking XARA.Starting Price: $30 per month -
7
Daria
XBrain
Daria’s advanced automated features allow users to quickly and easily build predictive models, significantly cutting back on days and weeks of iterative work associated with the traditional machine learning process. Remove financial and technological barriers to build AI systems from scratch for enterprises. Streamline and expedite workflows by lifting weeks of iterative work through automated machine learning for data experts. Get hands-on experience in machine learning with an intuitive GUI for data science beginners. Daria provides various data transformation functions to conveniently construct multiple feature sets. Daria automatically explores through millions of possible combinations of algorithms, modeling techniques and hyperparameters to select the best predictive model. Predictive models built with Daria can be deployed straight to production with a single line of code via Daria’s RESTful API. -
8
Langtail
Langtail
Langtail is a cloud-based application development tool designed to help companies debug, test, deploy, and monitor LLM-powered apps with ease. The platform offers a no-code playground for debugging prompts, fine-tuning model parameters, and running LLM tests to prevent issues when models or prompts change. Langtail specializes in LLM testing, including chatbot testing and ensuring robust AI LLM test prompts. With its comprehensive features, Langtail enables teams to: • Test LLM models thoroughly to catch potential issues before they affect production environments. • Deploy prompts as API endpoints for seamless integration. • Monitor model performance in production to ensure consistent outcomes. • Use advanced AI firewall capabilities to safeguard and control AI interactions. Langtail is the ideal solution for teams looking to ensure the quality, stability, and security of their LLM and AI-powered applications.Starting Price: $99/month/unlimited users -
9
Guide Labs
Guide Labs
Guide Labs is developing a new class of interpretable AI systems and foundation models that humans can reliably debug, trust, and understand. Our models are engineered to produce human-understandable factors for any output, provide reliable context citations, and specify which training data influences the generated output. This approach addresses issues in current AI systems, which often produce explanations unrelated to their outputs, are difficult to debug, and are challenging to control and align. The Guide Labs team comprises experts with over 20 years of experience in interpretable machine learning. We have developed the first interpretable generative diffusion model and large language model. We are rethinking the model architecture, loss function, and entire pipeline to constrain the model training process such that the models we get are more easily understandable, their errors easier to identify and fix, and easy to align. -
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Options for every business to train deep learning and machine learning models cost-effectively. AI accelerators for every use case, from low-cost inference to high-performance training. Simple to get started with a range of services for development and deployment. Tensor Processing Units (TPUs) are custom-built ASIC to train and execute deep neural networks. Train and run more powerful and accurate models cost-effectively with faster speed and scale. A range of NVIDIA GPUs to help with cost-effective inference or scale-up or scale-out training. Leverage RAPID and Spark with GPUs to execute deep learning. Run GPU workloads on Google Cloud where you have access to industry-leading storage, networking, and data analytics technologies. Access CPU platforms when you start a VM instance on Compute Engine. Compute Engine offers a range of both Intel and AMD processors for your VMs.
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LatticeFlow
LatticeFlow
Empower your ML teams to deliver robust and performant AI models by auto-diagnosing and improving your data and models. The only platform that can auto-diagnose data and models, empowering ML teams to deliver robust and performant AI models faster. Covering camera noise, sign stickers, shadows, and others. Confirmed with real-world images on which the model systematically fails. While improving model accuracy by 0.2%. Our mission is to change the way the next generation of AI systems is built. If we are to use AI in our businesses, at doctor’s offices, on our roads, or in our homes, we need to build AI systems that companies and users can trust. We are leading AI professors and researchers from ETH Zurich with broad expertise in formal methods, symbolic reasoning, and machine learning. We started LatticeFlow with the goal of building the world’s first platform that enables companies to deliver robust AI models that work reliably in the wild. -
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Intel Gaudi Software
Intel
Intel’s Gaudi software gives developers access to a comprehensive set of tools, libraries, containers, model references, and documentation that support creation, migration, optimization, and deployment of AI models on Intel® Gaudi® accelerators. It helps streamline every stage of AI development including training, fine-tuning, debugging, profiling, and performance optimization for generative AI (GenAI) and large language models (LLMs) on Gaudi hardware, whether in data centers or cloud environments. It includes up-to-date documentation with code samples, best practices, API references, and guides for efficient use of Gaudi solutions such as Gaudi 2 and Gaudi 3, and it integrates with popular frameworks and tools to support model portability and scalability. Users can access performance data to review training and inference benchmarks, utilize community and support resources, and take advantage of containers and libraries tailored to high-performance AI workloads. -
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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. -
14
Citrusˣ
Citrusˣ
Citrusˣ’s end-to-end platform for AI transparency and explainability allows organizations to maintain confidence in their models. Data scientists can use the Summary and Validation pages on the web UI and SDK to validate the performance of their models, investigate results, and address issues. Data science managers and Chief data officers can track the work of their teams, compare models, and ensure KPIs are being met. Risk officers and MRMs can use the web UI and reports to verify the soundness of the model, assess the risks, and ensure AI is being used responsibly and fairly according to regulatory requirements. Executives and regulators can use summarized custom reports to verify the model's strength and accuracy, understand the model's decisions, identify risks, and ensure compliance to protect the organization from potential lawsuits and maintain its reputation. -
15
ESMC
Biohub
ESMC is the latest in the ESM family of protein language models, establishing a new frontier in representation learning for protein biology. Trained on billions of evolutionary sequences, it learns representations that reflect a mechanistic reduction of protein structure and function. The model is built on a transformer architecture, supports sequences as its core modality, and is trained on up to 6 billion proteins. ESMC is designed for protein science research, including structure prediction, function annotation, protein design, and understanding evolutionary relationships between proteins. It can generate novel proteins from partial sequence, structure, or functional constraints, helping researchers explore new possibilities in protein design and biological discovery. The Biohub Platform provides access to ESMC through the API and the ESM Python package, with quickstart resources for installing the package, creating an API key, connecting to the platform.Starting Price: Free -
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Evidently AI
Evidently AI
The open-source ML observability platform. Evaluate, test, and monitor ML models from validation to production. From tabular data to NLP and LLM. Built for data scientists and ML engineers. All you need to reliably run ML systems in production. Start with simple ad hoc checks. Scale to the complete monitoring platform. All within one tool, with consistent API and metrics. Useful, beautiful, and shareable. Get a comprehensive view of data and ML model quality to explore and debug. Takes a minute to start. Test before you ship, validate in production and run checks at every model update. Skip the manual setup by generating test conditions from a reference dataset. Monitor every aspect of your data, models, and test results. Proactively catch and resolve production model issues, ensure optimal performance, and continuously improve it.Starting Price: $500 per month -
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Metatext
Metatext
Build, evaluate, deploy, and refine custom natural language processing models. Empower your team to automate workflows without hiring an AI expert team and costly infra. Metatext simplifies the process of creating customized AI/NLP models, even without expertise in ML, data science, or MLOps. With just a few steps, automate complex workflows, and rely on intuitive UI and APIs to handle the heavy work. Enable AI into your team using a simple but intuitive UI, add your domain expertise, and let our APIs do all the heavy work. Get your custom AI trained and deployed automatically. Get the best from a set of deep learning algorithms. Test it using a Playground. Integrate our APIs with your existing systems, Google Spreadsheets, and other tools. Select the AI engine that best suits your use case. Each one offers a set of tools to assist creating datasets and fine-tuning models. Upload text data in various file formats and annotate labels using our built-in AI-assisted data labeling tool.Starting Price: $35 per month -
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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. -
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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 -
20
Tencent Cloud TI Platform
Tencent
Tencent Cloud TI Platform is a one-stop machine learning service platform designed for AI engineers. It empowers AI development throughout the entire process from data preprocessing to model building, model training, model evaluation, and model service. Preconfigured with diverse algorithm components, it supports multiple algorithm frameworks to adapt to different AI use cases. Tencent Cloud TI Platform delivers a one-stop machine learning experience that covers a complete and closed-loop workflow from data preprocessing to model building, model training, and model evaluation. With Tencent Cloud TI Platform, even AI beginners can have their models constructed automatically, making it much easier to complete the entire training process. Tencent Cloud TI Platform's auto-tuning tool can also further enhance the efficiency of parameter tuning. Tencent Cloud TI Platform allows CPU/GPU resources to elastically respond to different computing power needs with flexible billing modes. -
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Amazon Bedrock Guardrails
Amazon
Amazon Bedrock Guardrails is a configurable safeguard system designed to enhance the safety and compliance of generative AI applications built on Amazon Bedrock. It enables developers to implement customized safety, privacy, and truthfulness controls across various foundation models, including those hosted within Amazon Bedrock, fine-tuned models, and self-hosted models. Guardrails provide a consistent approach to enforcing responsible AI policies by evaluating both user inputs and model responses based on defined policies. These policies include content filters for harmful text and image content, denial of specific topics, word filters for undesirable terms, sensitive information filters to redact personally identifiable information, and contextual grounding checks to detect and filter hallucinations in model responses. -
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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. -
23
Openlayer
Openlayer
Onboard your data and models to Openlayer and collaborate with the whole team to align expectations surrounding quality and performance. Breeze through the whys behind failed goals to solve them efficiently. The information to diagnose the root cause of issues is at your fingertips. Generate more data that looks like the subpopulation and retrain the model. Test new commits against your goals to ensure systematic progress without regressions. Compare versions side-by-side to make informed decisions and ship with confidence. Save engineering time by rapidly figuring out exactly what’s driving model performance. Find the most direct paths to improving your model. Know the exact data needed to boost model performance and focus on cultivating high-quality and representative datasets. -
24
Taylor AI
Taylor AI
Training open source language models requires time and specialized knowledge. Taylor AI empowers your engineering team to focus on generating real business value, rather than deciphering complex libraries and setting up training infrastructure. Working with third-party LLM providers requires exposing your company's sensitive data. Most providers reserve the right to re-train models with your data. With Taylor AI, you own and control your models. Break away from the pay-per-token pricing structure. With Taylor AI, you only pay to train the model. You have the freedom to deploy and interact with your AI models as much as you like. New open source models emerge every month. Taylor AI stays current on the best open source language models, so you don't have to. Stay ahead, and train with the latest open source models. You own your model, so you can deploy it on your terms according to your unique compliance and security standards. -
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Lunary
Lunary
Lunary is an AI developer platform designed to help AI teams manage, improve, and protect Large Language Model (LLM) chatbots. It offers features such as conversation and feedback tracking, analytics on costs and performance, debugging tools, and a prompt directory for versioning and team collaboration. Lunary supports integration with various LLMs and frameworks, including OpenAI and LangChain, and provides SDKs for Python and JavaScript. Guardrails to deflect malicious prompts and sensitive data leaks. Deploy in your VPC with Kubernetes or Docker. Allow your team to judge responses from your LLMs. Understand what languages your users are speaking. Experiment with prompts and LLM models. Search and filter anything in milliseconds. Receive notifications when agents are not performing as expected. Lunary's core platform is 100% open-source. Self-host or in the cloud, get started in minutes.Starting Price: $20 per month -
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Lightning AI
Lightning AI
Use our platform to build AI products, train, fine tune and deploy models on the cloud without worrying about infrastructure, cost management, scaling, and other technical headaches. Train, fine tune and deploy models with prebuilt, fully customizable, modular components. Focus on the science and not the engineering. A Lightning component organizes code to run on the cloud, manage its own infrastructure, cloud costs, and more. 50+ optimizations to lower cloud costs and deliver AI in weeks not months. Get enterprise-grade control with consumer-level simplicity to optimize performance, reduce cost, and lower risk. Go beyond a demo. Launch the next GPT startup, diffusion startup, or cloud SaaS ML service in days not months.Starting Price: $10 per credit -
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3LC
3LC
Light up the black box and pip install 3LC to gain the clarity you need to make meaningful changes to your models in moments. Remove the guesswork from your model training and iterate fast. Collect per-sample metrics and visualize them in your browser. Analyze your training and eliminate issues in your dataset. Model-guided, interactive data debugging and enhancements. Find important or inefficient samples. Understand what samples work and where your model struggles. Improve your model in different ways by weighting your data. Make sparse, non-destructive edits to individual samples or in a batch. Maintain a lineage of all changes and restore any previous revisions. Dive deeper than standard experiment trackers with per-sample per epoch metrics and data tracking. Aggregate metrics by sample features, rather than just epoch, to spot hidden trends. Tie each training run to a specific dataset revision for full reproducibility. -
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Biohub
Biohub
Biohub is an open platform for building on the world model of protein biology. It provides access to the ESM family of models, including ESMC, ESMFold2, and ESM3, along with interactive tools and developer resources for protein science research. ESMC is a state-of-the-art protein language model trained on billions of evolutionary sequences, building representations that capture fundamental mechanisms of protein structure and function. It powers functional analysis, structure prediction, protein design, and the exploration of evolutionary relationships between proteins. ESMFold2 predicts high-resolution, all-atom 3D structures of biomolecular complexes directly from sequence, with optional multiple sequence alignment input for enhanced accuracy on challenging targets. ESM3 jointly models sequence, structure, and function, enabling controllable generation of novel proteins by conditioning on any combination of these modalities. -
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NVIDIA PhysicsNeMo
NVIDIA
NVIDIA PhysicsNeMo is an open source Python deep-learning framework for building, training, fine-tuning, and inferring physics-AI models that combine physics knowledge with data to accelerate simulations, create high-fidelity surrogate models, and enable near-real-time predictions across domains such as computational fluid dynamics, structural mechanics, electromagnetics, weather and climate, and digital twin applications. It provides scalable, GPU-accelerated tools and Python APIs built on PyTorch and released under the Apache 2.0 license, offering curated model architectures including physics-informed neural networks, neural operators, graph neural networks, and generative AI–based approaches so developers can harness physics-driven causality alongside observed data for engineering-grade modeling. PhysicsNeMo includes end-to-end training pipelines from geometry ingestion to differential equations, reference application recipes to jump-start workflows.Starting Price: Free -
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Alibaba Cloud Machine Learning Platform for AI
Alibaba Cloud
An end-to-end platform that provides various machine learning algorithms to meet your data mining and analysis requirements. Machine Learning Platform for AI provides end-to-end machine learning services, including data processing, feature engineering, model training, model prediction, and model evaluation. Machine learning platform for AI combines all of these services to make AI more accessible than ever. Machine Learning Platform for AI provides a visualized web interface allowing you to create experiments by dragging and dropping different components to the canvas. Machine learning modeling is a simple, step-by-step procedure, improving efficiencies and reducing costs when creating an experiment. Machine Learning Platform for AI provides more than one hundred algorithm components, covering such scenarios as regression, classification, clustering, text analysis, finance, and time series.Starting Price: $1.872 per hour -
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Striveworks Chariot
Striveworks
Make AI a trusted part of your business. Build better, deploy faster, and audit easily with the flexibility of a cloud-native platform and the power to deploy anywhere. Easily import models and search cataloged models from across your organization. Save time by annotating data rapidly with model-in-the-loop hinting. Understand the full provenance of your data, models, workflows, and inferences. Deploy models where you need them, including for edge and IoT use cases. Getting valuable insights from your data is not just for data scientists. With Chariot’s low-code interface, meaningful collaboration can take place across teams. Train models rapidly using your organization's production data. Deploy models with one click and monitor models in production at scale. -
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Build, run and manage AI models, and optimize decisions at scale across any cloud. IBM Watson Studio empowers you to operationalize AI anywhere as part of IBM Cloud Pak® for Data, the IBM data and AI platform. Unite teams, simplify AI lifecycle management and accelerate time to value with an open, flexible multicloud architecture. Automate AI lifecycles with ModelOps pipelines. Speed data science development with AutoAI. Prepare and build models visually and programmatically. Deploy and run models through one-click integration. Promote AI governance with fair, explainable AI. Drive better business outcomes by optimizing decisions. Use open source frameworks like PyTorch, TensorFlow and scikit-learn. Bring together the development tools including popular IDEs, Jupyter notebooks, JupterLab and CLIs — or languages such as Python, R and Scala. IBM Watson Studio helps you build and scale AI with trust and transparency by automating AI lifecycle management.
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NVIDIA Modulus
NVIDIA
NVIDIA Modulus is a neural network framework that blends the power of physics in the form of governing partial differential equations (PDEs) with data to build high-fidelity, parameterized surrogate models with near-real-time latency. Whether you’re looking to get started with AI-driven physics problems or designing digital twin models for complex non-linear, multi-physics systems, NVIDIA Modulus can support your work. Offers building blocks for developing physics machine learning surrogate models that combine both physics and data. The framework is generalizable to different domains and use cases—from engineering simulations to life sciences and from forward simulations to inverse/data assimilation problems. Provides parameterized system representation that solves for multiple scenarios in near real time, letting you train once offline to infer in real time repeatedly. -
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Actable AI
Actable AI
Powered by open-source state-of-the-art AutoML to train quality models without hassle. Leverages Deep Learning and pre-trained models for extra intelligence whenever applicable. Utilizes Causal AI with AutoML for fairness, causal inference and counterfactual predictions. All trained models are deployed instantly to be used interactively online or with an API. Full feature importances and model explanations with Shapley values. Our AI engine is entirely open-source. It means our algorithms can be fully audited and used everywhere. Clusters customers or products to similar cohorts with a rich set of features. Forecasts future by capturing temporal patterns from historical data. Trains predictive models with labelled data to predict unlabelled data.Starting Price: $80 per user per month -
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InSilicoTrials
InSilicoTrials
InSilicoTrials.com is a web-based platform, which provides a user-friendly computational modeling and simulation environment where many integrated easy-to-use in silico tools are readily available. The platform targets primarily users from the medical devices and pharmaceutical sectors. The in silico tools available for medical devices enable computational testing in different biomedical areas like radiology, orthopedics and cardiovascular during product design, development and validation processes. For the pharmaceutical sector, the platform provides access to in silico tools developed at all stages of the drug discovery and development processes and for many different therapeutic areas. We have built the only cloud-platform based on the crowdscience concept that makes it easy to use validated models and cut your R&D costs now. A growing catalogue of models ready to be used, on a pay per use basis. -
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Neuri
Neuri
We conduct and implement cutting-edge research on artificial intelligence to create real advantage in financial investment. Illuminating the financial market with ground-breaking neuro-prediction. We combine novel deep reinforcement learning algorithms and graph-based learning with artificial neural networks for modeling and predicting time series. Neuri strives to generate synthetic data emulating the global financial markets, testing it with complex simulations of trading behavior. We bet on the future of quantum optimization in enabling our simulations to surpass the limits of classical supercomputing. Financial markets are highly fluid, with dynamics evolving over time. As such we build AI algorithms that adapt and learn continuously, in order to uncover the connections between different financial assets, classes and markets. The application of neuroscience-inspired models, quantum algorithms and machine learning to systematic trading at this point is underexplored. -
37
Predibase
Predibase
Declarative machine learning systems provide the best of flexibility and simplicity to enable the fastest-way to operationalize state-of-the-art models. Users focus on specifying the “what”, and the system figures out the “how”. Start with smart defaults, but iterate on parameters as much as you’d like down to the level of code. Our team pioneered declarative machine learning systems in industry, with Ludwig at Uber and Overton at Apple. Choose from our menu of prebuilt data connectors that support your databases, data warehouses, lakehouses, and object storage. Train state-of-the-art deep learning models without the pain of managing infrastructure. Automated Machine Learning that strikes the balance of flexibility and control, all in a declarative fashion. With a declarative approach, finally train and deploy models as quickly as you want. -
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Hive AutoML
Hive
Build and deploy deep learning models for custom use cases. Our automated machine learning process allows customers to create powerful AI solutions built on our best-in-class models and tailored to the specific challenges they face. Digital platforms can quickly create models specifically made to fit their guidelines and needs. Build large language models for specialized use cases such as customer and technical support bots. Create image classification models to better understand image libraries for search, organization, and more. -
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BIOVIA Discovery Studio
Dassault Systèmes
Today’s biopharmaceutical industry is marked by complexity: growing market demands for improved specificity and safety, novel treatment classes and more intricate mechanisms of disease. Keeping up with this complexity requires a deeper understanding of therapeutic behavior. Modeling and simulation methods provide a unique means to explore biological and physicochemical processes down to the atomic level. This can guide physical experimentation, accelerating the discovery and development process. BIOVIA Discovery Studio brings together over 30 years of peer-reviewed research and world-class in silico techniques such as molecular mechanics, free energy calculations, biotherapeutics developability and more into a common environment. It provides researchers with a complete toolset to explore the nuances of protein chemistry and catalyze discovery of small and large molecule therapeutics from Target ID to Lead Optimization. -
40
Amazon SageMaker HyperPod
Amazon
Amazon SageMaker HyperPod is a purpose-built, resilient compute infrastructure that simplifies and accelerates the development of large AI and machine-learning models by handling distributed training, fine-tuning, and inference across clusters with hundreds or thousands of accelerators, including GPUs and AWS Trainium chips. It removes the heavy lifting involved in building and managing ML infrastructure by providing persistent clusters that automatically detect and repair hardware failures, automatically resume workloads, and optimize checkpointing to minimize interruption risk, enabling months-long training jobs without disruption. HyperPod offers centralized resource governance; administrators can set priorities, quotas, and task-preemption rules so compute resources are allocated efficiently among tasks and teams, maximizing utilization and reducing idle time. It also supports “recipes” and pre-configured settings to quickly fine-tune or customize foundation models. -
41
Simplismart
Simplismart
Fine-tune and deploy AI models with Simplismart's fastest inference engine. Integrate with AWS/Azure/GCP and many more cloud providers for simple, scalable, cost-effective deployment. Import open source models from popular online repositories or deploy your own custom model. Leverage your own cloud resources or let Simplismart host your model. With Simplismart, you can go far beyond AI model deployment. You can train, deploy, and observe any ML model and realize increased inference speeds at lower costs. Import any dataset and fine-tune open-source or custom models rapidly. Run multiple training experiments in parallel efficiently to speed up your workflow. Deploy any model on our endpoints or your own VPC/premise and see greater performance at lower costs. Streamlined and intuitive deployment is now a reality. Monitor GPU utilization and all your node clusters in one dashboard. Detect any resource constraints and model inefficiencies on the go. -
42
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 -
43
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 -
44
Amazon Nova Forge
Amazon
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. -
45
Flip AI
Flip AI
Our large language model (LLM) can understand and reason through any and all observability data, including unstructured data, so that you can rapidly restore software and systems to health. Our LLM has been trained to understand and mitigate thousands of critical incidents, across every type of architecture imaginable – giving enterprise developers access to the world’s best debugging expert. Our LLM was built to solve the hardest part of the software engineering process – debugging production incidents. Our model requires no training and works on any observability data system. It can learn based on feedback and finetune based on past incidents and patterns in your environment while keeping your data in your boundaries. This means you are resolving critical incidents using Flip in seconds. -
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AIxBlock
AIxBlock
AIxBlock: The first unified and decentralized platform for end-to-end AI development and workflow automation - built natively on MCP. AIxBlock is a MCP-based, decentralized end-to-end AI development and workflow automation platform purpose-built for AI engineer teams. It empowers users to build, train, deploy AI models and build AI automation workflows using those models through a unified environment that integrates decentralized compute, models, datasets, and labeling resources - all at a fraction of the traditional cost. AIxBlock is the modular AI ecosystem - purpose-built for custom model creation, workflow automation, and open interoperability across MCP client tools like Cursor, Claude, WindSurf, etc.Starting Price: $19 per month -
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Geminus
Geminus
Geminus unleashes the power of predictive intelligence by intersecting AI and physics with multi-fidelity modeling. Our novel, first-principles AI translates the constraints of the physical world inside resilient predictive models. The Geminus platform leverages sparse data to quickly analyze the behavior of complex industrial systems, and precisely predict the impact of decisions that drive your business forward. The Geminus multi-fidelity approach fuses models with data, which enables you to create highly accurate surrogates over 1,000x faster than simulation. Only Geminus accurately quantifies model uncertainty, so you can be confident in your predictions and the decisions they inspire. Geminus compresses model creation time from months to hours requiring far fewer data and computes resources than traditional AI, or simulation methods. Models built on Geminus are infused with an understanding of the known behavior of real-world systems. -
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Pryon
Pryon
Natural Language Processing is Artificial Intelligence that enables computers to analyze and understand human language. Pryon’s AI is trained to perform read, organize and search in ways that previously required humans. This powerful capability is used in every interaction, both to understand a request and to retrieve the accurate response. The success of any NLP project is directly correlated to the sophistication of the underlying natural language technologies used. To make your content ready for use in chatbots, search, automations, etc. – it must be broken into specific pieces so a user can get the exact answer, result or snippet needed. This can be done manually as when a specialist breaks information into intents and entities. Pryon creates a dynamic model of your content for automatically identifying and attaching rich metadata to each piece of information. When you need to add, change or remove content this model is regenerated with a click. -
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Artec Studio
Artec 3D
Transform your 3D scanner with industry-acclaimed software for professional 3D scanning and data processing, easy 3D scanning, and high-precision results. Whether you choose Autopilot for ease of use, or manual mode for full control and flexibility, Artec Studio never compromises on precision. Fast measurements and mesh-to-CAD analysis right in Artec Studio. Fully integrated with Geomagic Control X for advanced inspection within the Artec Studio interface. Accelerate your engineering by fitting primitives to your 3D model and precisely positioning it. Export STEP files directly to SOLIDWORKS, or complex meshes to Design X or Geomagic for SOLIDWORKS. Use Artec Studio’s host of CGI tools including full-color 3D scan data, texturizing via photogrammetry, and auto glare removal to create replica 3D models with perfect geometry and color representation. Artec Studio’s AI neural network delivers astonishing, high-resolution scans via HD Mode for users scanning with Eva or Leo. -
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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.Starting Price: Free
