Alternatives to Inferable

Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case. Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection. Vertex AI Agent Builder empowers developers to design and deploy advanced generative AI applications for enterprise use. It supports both no-code and code-driven development, enabling users to create AI agents through natural language prompts or by integrating with frameworks like LangChain and LlamaIndex.

Google AI Studio is an all-in-one environment designed for building AI-first applications with Google’s latest models. It supports Gemini, Imagen, Veo, and Gemma, allowing developers to experiment across multiple modalities in one place. The platform emphasizes vibe coding, enabling users to describe what they want and let AI handle the technical heavy lifting. Developers can generate complete, production-ready apps using natural language instructions. One-click deployment makes it easy to move from prototype to live application. Google AI Studio includes a centralized dashboard for API keys, billing, and usage tracking. Detailed logs and rate-limit insights help teams operate efficiently. SDK support for Python, Node.js, and REST APIs ensures flexibility. Quickstart guides reduce onboarding time to minutes. Overall, Google AI Studio blends experimentation, vibe coding, and scalable production into a single workflow.

LM-Kit.NET is an enterprise-grade toolkit designed for seamlessly integrating generative AI into your .NET applications, fully supporting Windows, Linux, and macOS. Empower your C# and VB.NET projects with a flexible platform that simplifies the creation and orchestration of dynamic AI agents. Leverage efficient Small Language Models for on‑device inference, reducing computational load, minimizing latency, and enhancing security by processing data locally. Experience the power of Retrieval‑Augmented Generation (RAG) to boost accuracy and relevance, while advanced AI agents simplify complex workflows and accelerate development. Native SDKs ensure smooth integration and high performance across diverse platforms. With robust support for custom AI agent development and multi‑agent orchestration, LM‑Kit.NET streamlines prototyping, deployment, and scalability—enabling you to build smarter, faster, and more secure solutions trusted by professionals worldwide.

Potpie is a collaborative open source platform designed for developers to craft AI agents specifically suited for their codebases, streamlining processes such as debugging, testing, system architecture, onboarding, code evaluations, and documentation. By converting your codebase into an extensive knowledge graph, Potpie equips its agents with a profound contextual understanding that enables them to execute engineering tasks with remarkable accuracy. The platform includes more than five pre-built agents, with some focusing on stack trace analysis and the generation of integration tests. Additionally, developers have the option to create personalized agents through straightforward prompts, ensuring easy incorporation into their established workflows. Potpie also features an intuitive chat interface and offers a VS Code extension for direct integration into development setups. With capabilities like multi-LLM support, developers can incorporate various AI models to enhance performance and adaptability, making Potpie an invaluable tool for modern software engineering. This versatility allows teams to optimize their overall productivity while benefiting from advanced automation techniques.

Mistral AI stands out as an innovative startup in the realm of artificial intelligence, focusing on open-source generative solutions. The company provides a diverse array of customizable, enterprise-level AI offerings that can be implemented on various platforms, such as on-premises, cloud, edge, and devices. Among its key products are "Le Chat," a multilingual AI assistant aimed at boosting productivity in both personal and professional settings, and "La Plateforme," a platform for developers that facilitates the creation and deployment of AI-driven applications. With a strong commitment to transparency and cutting-edge innovation, Mistral AI has established itself as a prominent independent AI laboratory, actively contributing to the advancement of open-source AI and influencing policy discussions. Their dedication to fostering an open AI ecosystem underscores their role as a thought leader in the industry.

An open-source programming framework designed for agent-based AI is available in the form of AutoGen. This framework presents a multi-agent conversational system that serves as a user-friendly abstraction layer, enabling the efficient creation of workflows involving large language models. AutoGen encompasses a diverse array of functional systems that cater to numerous applications across different fields and levels of complexity. Furthermore, it enhances the performance of inference APIs for large language models, offering opportunities to optimize efficiency and minimize expenses. By leveraging this framework, developers can streamline their projects while exploring innovative solutions in AI.

Nurix AI, located in Bengaluru, focuses on creating customized AI agents that aim to streamline and improve enterprise workflows across a range of industries, such as sales and customer support. Their platform is designed to integrate effortlessly with current enterprise systems, allowing AI agents to perform sophisticated tasks independently, deliver immediate responses, and make smart decisions without ongoing human intervention. One of the most remarkable aspects of their offering is a unique voice-to-voice model, which facilitates fast and natural conversations in various languages, thus enhancing customer engagement. Furthermore, Nurix AI provides specialized AI services for startups, delivering comprehensive solutions to develop and expand AI products while minimizing the need for large internal teams. Their wide-ranging expertise includes large language models, cloud integration, inference, and model training, guaranteeing that clients receive dependable and enterprise-ready AI solutions tailored to their specific needs. By committing to innovation and quality, Nurix AI positions itself as a key player in the AI landscape, supporting businesses in leveraging technology for greater efficiency and success.

Tensormesh serves as an innovative caching layer designed for inference tasks involving large language models, allowing organizations to capitalize on intermediate computations, significantly minimize GPU consumption, and enhance both time-to-first-token and overall latency. By capturing and repurposing essential key-value cache states that would typically be discarded after each inference, it eliminates unnecessary computational efforts and achieves “up to 10x faster inference,” all while substantially reducing the strain on GPUs. The platform is versatile, accommodating both public cloud and on-premises deployments, and offers comprehensive observability, enterprise-level control, as well as SDKs/APIs and dashboards for seamless integration into existing inference frameworks, boasting compatibility with inference engines like vLLM right out of the box. Tensormesh prioritizes high performance at scale, enabling sub-millisecond repeated queries, and fine-tunes every aspect of inference from caching to computation, ensuring that organizations can maximize efficiency and responsiveness in their applications. In an increasingly competitive landscape, such enhancements provide a critical edge for companies aiming to leverage advanced language models effectively.

Fal represents a serverless Python environment enabling effortless cloud scaling of your code without the need for infrastructure management. It allows developers to create real-time AI applications with incredibly fast inference times, typically around 120 milliseconds. Explore a variety of pre-built models that offer straightforward API endpoints, making it easy to launch your own AI-driven applications. You can also deploy custom model endpoints, allowing for precise control over factors such as idle timeout, maximum concurrency, and automatic scaling. Utilize widely-used models like Stable Diffusion and Background Removal through accessible APIs, all kept warm at no cost to you—meaning you won’t have to worry about the expense of cold starts. Engage in conversations about our product and contribute to the evolution of AI technology. The platform can automatically expand to utilize hundreds of GPUs and retract back to zero when not in use, ensuring you only pay for compute resources when your code is actively running. To get started with fal, simply import it into any Python project and wrap your existing functions with its convenient decorator, streamlining the development process for AI applications. This flexibility makes fal an excellent choice for both novice and experienced developers looking to harness the power of AI.

Amazon SageMaker simplifies the process of deploying machine learning models for making predictions, also referred to as inference, ensuring optimal price-performance for a variety of applications. The service offers an extensive range of infrastructure and deployment options tailored to fulfill all your machine learning inference requirements. As a fully managed solution, it seamlessly integrates with MLOps tools, allowing you to efficiently scale your model deployments, minimize inference costs, manage models more effectively in a production environment, and alleviate operational challenges. Whether you require low latency (just a few milliseconds) and high throughput (capable of handling hundreds of thousands of requests per second) or longer-running inference for applications like natural language processing and computer vision, Amazon SageMaker caters to all your inference needs, making it a versatile choice for data-driven organizations. This comprehensive approach ensures that businesses can leverage machine learning without encountering significant technical hurdles.

Semantic Kernel is an open-source development toolkit that facilitates the creation of AI agents and the integration of cutting-edge AI models into applications written in C#, Python, or Java. This efficient middleware accelerates the deployment of robust enterprise solutions. Companies like Microsoft and other Fortune 500 firms are taking advantage of Semantic Kernel's flexibility, modularity, and observability. With built-in security features such as telemetry support, hooks, and filters, developers can confidently provide responsible AI solutions at scale. The support for versions 1.0 and above across C#, Python, and Java ensures reliability and a commitment to maintaining non-breaking changes. Existing chat-based APIs can be effortlessly enhanced to include additional modalities such as voice and video, making the toolkit highly adaptable. Semantic Kernel is crafted to be future-proof, ensuring seamless integration with the latest AI models as technology evolves, thus maintaining its relevance in the rapidly changing landscape of artificial intelligence. This forward-thinking design empowers developers to innovate without fear of obsolescence.

IBM watsonx Orchestrate serves as an advanced generative AI and automation platform designed to help enterprises automate various tasks and simplify intricate processes. It offers a diverse catalog of prebuilt applications and skills, complemented by an interactive chat interface that allows users to create scalable AI assistants and agents for managing repetitive tasks and optimizing operations. A standout feature is its next-generation low-code builder studio, which supports the creation and deployment of language model-powered assistants, all guided by an intuitive natural language interface that streamlines the development process. Additionally, the Skills Studio empowers teams to craft automation solutions by leveraging data, decisions, and workflows, effectively integrating their existing technological investments with AI capabilities. With thousands of prebuilt skills readily available, organizations can swiftly connect with their current systems and applications. Furthermore, the platform's LLM-based routing and orchestration functionalities enhance user experience, enabling rapid interaction with AI agents to efficiently execute tasks, thereby significantly reducing the time and effort required for operations. Ultimately, IBM watsonx Orchestrate is designed not just to improve productivity but also to foster innovation across various business processes.

Lamini empowers organizations to transform their proprietary data into advanced LLM capabilities, providing a platform that allows internal software teams to elevate their skills to match those of leading AI teams like OpenAI, all while maintaining the security of their existing systems. It ensures structured outputs accompanied by optimized JSON decoding, features a photographic memory enabled by retrieval-augmented fine-tuning, and enhances accuracy while significantly minimizing hallucinations. Additionally, it offers highly parallelized inference for processing large batches efficiently and supports parameter-efficient fine-tuning that scales to millions of production adapters. Uniquely, Lamini stands out as the sole provider that allows enterprises to safely and swiftly create and manage their own LLMs in any environment. The company harnesses cutting-edge technologies and research that contributed to the development of ChatGPT from GPT-3 and GitHub Copilot from Codex. Among these advancements are fine-tuning, reinforcement learning from human feedback (RLHF), retrieval-augmented training, data augmentation, and GPU optimization, which collectively enhance the capabilities of AI solutions. Consequently, Lamini positions itself as a crucial partner for businesses looking to innovate and gain a competitive edge in the AI landscape.

FriendliAI serves as an advanced generative AI infrastructure platform that delivers rapid, efficient, and dependable inference solutions tailored for production settings. The platform is equipped with an array of tools and services aimed at refining the deployment and operation of large language models (LLMs) alongside various generative AI tasks on a large scale. Among its key features is Friendli Endpoints, which empowers users to create and implement custom generative AI models, thereby reducing GPU expenses and hastening AI inference processes. Additionally, it facilitates smooth integration with well-known open-source models available on the Hugging Face Hub, ensuring exceptionally fast and high-performance inference capabilities. FriendliAI incorporates state-of-the-art technologies, including Iteration Batching, the Friendli DNN Library, Friendli TCache, and Native Quantization, all of which lead to impressive cost reductions (ranging from 50% to 90%), a significant decrease in GPU demands (up to 6 times fewer GPUs), enhanced throughput (up to 10.7 times), and a marked decrease in latency (up to 6.2 times). With its innovative approach, FriendliAI positions itself as a key player in the evolving landscape of generative AI solutions.

Transformers is a versatile library that includes pretrained models for natural language processing, computer vision, audio, and multimodal tasks, facilitating both inference and training. With the Transformers library, you can effectively train models tailored to your specific data, create inference applications, and utilize large language models for text generation. Visit the Hugging Face Hub now to discover a suitable model and leverage Transformers to kickstart your projects immediately. This library provides a streamlined and efficient inference class that caters to various machine learning tasks, including text generation, image segmentation, automatic speech recognition, and document question answering, among others. Additionally, it features a robust trainer that incorporates advanced capabilities like mixed precision, torch.compile, and FlashAttention, making it ideal for both training and distributed training of PyTorch models. The library ensures rapid text generation through large language models and vision-language models, and each model is constructed from three fundamental classes (configuration, model, and preprocessor), allowing for quick deployment in either inference or training scenarios. Overall, Transformers empowers users with the tools needed to create sophisticated machine learning solutions with ease and efficiency.

Deploy machine learning applications in just minutes instead of taking months. Streamline the conversion of raw data, create training datasets, and deliver features for scalable online inference effortlessly. By replacing custom data pipelines with reliable automated pipelines, you can save significant time and effort. Boost your team's productivity by enabling the sharing of features across the organization while standardizing all your machine learning data workflows within a single platform. With the ability to serve features at massive scale, you can trust that your systems will remain operational consistently. Tecton adheres to rigorous security and compliance standards. Importantly, Tecton is not a database or a processing engine; instead, it integrates seamlessly with your current storage and processing systems, enhancing their orchestration capabilities. This integration allows for greater flexibility and efficiency in managing your machine learning processes.

Dasha is a platform offering conversational AI as a service that enables the integration of lifelike voice and text interactions into various applications or products. By utilizing a straightforward integration process, developers can create intelligent conversational applications for multiple platforms, including web, desktop, mobile, IoT devices, and call centers. The platform features DashaScript, an event-driven declarative programming language designed to facilitate the creation of complex dialogues that can effectively pass a limited Turing test. This technology allows for the automation of call center interactions, the replication of the Google Duplex demo with fewer than 400 lines of code, or the development of user-friendly no-code graphical interfaces that translate into DashaScript. Any device with internet connectivity and access to a microphone or speaker is capable of running a Dasha application. Developers can leverage their existing infrastructure, such as databases and external services like Airtable, Zendesk, and TalkDesk, to enhance their voice and chat applications. Conversations can be executed across various platforms, and custom data can be incorporated into Dasha, allowing users to obtain results that deliver maximum value in their specific contexts. This flexibility ensures that Dasha remains a powerful tool for businesses looking to improve their conversational AI capabilities.

The NVIDIA Triton™ inference server provides efficient and scalable AI solutions for production environments. This open-source software simplifies the process of AI inference, allowing teams to deploy trained models from various frameworks, such as TensorFlow, NVIDIA TensorRT®, PyTorch, ONNX, XGBoost, Python, and more, across any infrastructure that relies on GPUs or CPUs, whether in the cloud, data center, or at the edge. By enabling concurrent model execution on GPUs, Triton enhances throughput and resource utilization, while also supporting inferencing on both x86 and ARM architectures. It comes equipped with advanced features such as dynamic batching, model analysis, ensemble modeling, and audio streaming capabilities. Additionally, Triton is designed to integrate seamlessly with Kubernetes, facilitating orchestration and scaling, while providing Prometheus metrics for effective monitoring and supporting live updates to models. This software is compatible with all major public cloud machine learning platforms and managed Kubernetes services, making it an essential tool for standardizing model deployment in production settings. Ultimately, Triton empowers developers to achieve high-performance inference while simplifying the overall deployment process.

NVIDIA DGX Cloud Serverless Inference provides a cutting-edge, serverless AI inference framework designed to expedite AI advancements through automatic scaling, efficient GPU resource management, multi-cloud adaptability, and effortless scalability. This solution enables users to reduce instances to zero during idle times, thereby optimizing resource use and lowering expenses. Importantly, there are no additional charges incurred for cold-boot startup durations, as the system is engineered to keep these times to a minimum. The service is driven by NVIDIA Cloud Functions (NVCF), which includes extensive observability capabilities, allowing users to integrate their choice of monitoring tools, such as Splunk, for detailed visibility into their AI operations. Furthermore, NVCF supports versatile deployment methods for NIM microservices, granting the ability to utilize custom containers, models, and Helm charts, thus catering to diverse deployment preferences and enhancing user flexibility. This combination of features positions NVIDIA DGX Cloud Serverless Inference as a powerful tool for organizations seeking to optimize their AI inference processes.

SiliconFlow is an advanced AI infrastructure platform tailored for developers, providing a comprehensive and scalable environment for executing, optimizing, and deploying both language and multimodal models. With its impressive speed, minimal latency, and high throughput, it ensures swift and dependable inference across various open-source and commercial models while offering versatile options such as serverless endpoints, dedicated computing resources, or private cloud solutions. The platform boasts a wide array of features, including integrated inference capabilities, fine-tuning pipelines, and guaranteed GPU access, all facilitated through an OpenAI-compatible API that comes equipped with built-in monitoring, observability, and intelligent scaling to optimize costs. For tasks that rely on diffusion, SiliconFlow includes the open-source OneDiff acceleration library, and its BizyAir runtime is designed to efficiently handle scalable multimodal workloads. Built with enterprise-level stability in mind, it incorporates essential features such as BYOC (Bring Your Own Cloud), strong security measures, and real-time performance metrics, making it an ideal choice for organizations looking to harness the power of AI effectively. Furthermore, SiliconFlow's user-friendly interface ensures that developers can easily navigate and leverage its capabilities to enhance their projects.

Accelerate the building, training, and deployment of models at scale through a fully managed infrastructure that provides essential tools and streamlined workflows. Launch personalized AI and LLMs on any infrastructure in mere seconds, effortlessly scaling inference as required. Tackle your most intensive tasks with batch job scheduling, ensuring you only pay for what you use on a per-second basis. Reduce costs effectively by utilizing GPU resources, spot instances, and a built-in automatic failover mechanism. Simplify complex infrastructure configurations by deploying with just a single command using YAML. Adjust to demand by automatically increasing worker capacity during peak traffic periods and reducing it to zero when not in use. Release advanced models via persistent endpoints within a serverless architecture, maximizing resource efficiency. Keep a close eye on system performance and inference metrics in real-time, tracking aspects like worker numbers, GPU usage, latency, and throughput. Additionally, carry out A/B testing with ease by distributing traffic across various models for thorough evaluation, ensuring your deployments are continually optimized for performance.

Baseten is a cloud-native platform focused on delivering robust and scalable AI inference solutions for businesses requiring high reliability. It enables deployment of custom, open-source, and fine-tuned AI models with optimized performance across any cloud or on-premises infrastructure. The platform boasts ultra-low latency, high throughput, and automatic autoscaling capabilities tailored to generative AI tasks like transcription, text-to-speech, and image generation. Baseten’s inference stack includes advanced caching, custom kernels, and decoding techniques to maximize efficiency. Developers benefit from a smooth experience with integrated tooling and seamless workflows, supported by hands-on engineering assistance from the Baseten team. The platform supports hybrid deployments, enabling overflow between private and Baseten clouds for maximum performance. Baseten also emphasizes security, compliance, and operational excellence with 99.99% uptime guarantees. This makes it ideal for enterprises aiming to deploy mission-critical AI products at scale.

Nscale is a specialized hyperscaler designed specifically for artificial intelligence, delivering high-performance computing that is fine-tuned for training, fine-tuning, and demanding workloads. Our vertically integrated approach in Europe spans from data centers to software solutions, ensuring unmatched performance, efficiency, and sustainability in all our offerings. Users can tap into thousands of customizable GPUs through our advanced AI cloud platform, enabling significant cost reductions and revenue growth while optimizing AI workload management. The platform is crafted to facilitate a smooth transition from development to production, whether employing Nscale's internal AI/ML tools or integrating your own. Users can also explore the Nscale Marketplace, which provides access to a wide array of AI/ML tools and resources that support effective and scalable model creation and deployment. Additionally, our serverless architecture allows for effortless and scalable AI inference, eliminating the hassle of infrastructure management. This system dynamically adjusts to demand, guaranteeing low latency and economical inference for leading generative AI models, ultimately enhancing user experience and operational efficiency. With Nscale, organizations can focus on innovation while we handle the complexities of AI infrastructure.

Calljmp provides a powerful edge-native platform for building AI agents that understand your product’s data and run directly within your environment. Its layered agentic architecture enables developers to choose the best tools at each stage while maintaining full control over context, memory, prompts, reasoning, and orchestration. Using TypeScript as the core development language, teams can build agents as code and deploy them instantly to Cloudflare Edge for low-latency execution. The platform includes persistent memory, vector search, hybrid search, and real-time observability to support complex AI logic with full transparency. Business teams gain instant visibility into workflows, logs, traces, and evaluations without relying on additional infrastructure. Human-in-the-loop controls allow manual review or approvals within any AI workflow, blending automation with oversight. Developers can launch AI portals to share agents with internal teams or clients in seconds, making collaboration effortless. With its focus on speed, security, and control, Calljmp significantly accelerates the development of AI-enabled products and backend automations.

We take the hard work out of AI processing on the edge. The Latent AI Efficient Inference Platform (LEIP) enables adaptive AI at edge by optimizing compute, energy, and memory without requiring modifications to existing AI/ML infrastructure or frameworks. LEIP is a fully-integrated modular workflow that can be used to build, quantify, and deploy edge AI neural network. Latent AI believes in a vibrant and sustainable future driven by the power of AI. Our mission is to enable the vast potential of AI that is efficient, practical and useful. We reduce the time to market with a Robust, Repeatable, and Reproducible workflow for edge AI. We help companies transform into an AI factory to make better products and services.

North is an all-in-one AI platform created by Cohere, which merges large language models, smart search functions, and automation into a secure and scalable workspace. This innovative platform is tailored to boost workforce productivity and enhance operational efficiency, allowing teams to direct their focus towards impactful tasks through the use of customized AI agents and sophisticated search features. With an intuitive interface that easily integrates into existing workflows, North empowers modern professionals to achieve greater results within a secure setting. By utilizing North’s advanced capabilities, businesses can automate mundane tasks, uncover valuable insights, and implement AI solutions that are not only robust but also flexible, all while upholding high standards of security and data protection. Organizations interested in discovering the transformative impact of North on their productivity and efficiency can either join the waitlist or request a demonstration directly from Cohere's official website. Additionally, this platform supports teams in adapting to changing demands and enhancing collaboration, making it a vital tool for companies looking to thrive in today's competitive landscape.

NetApp AIPod presents a holistic AI infrastructure solution aimed at simplifying the deployment and oversight of artificial intelligence workloads. By incorporating NVIDIA-validated turnkey solutions like the NVIDIA DGX BasePOD™ alongside NetApp's cloud-integrated all-flash storage, AIPod brings together analytics, training, and inference into one unified and scalable system. This integration allows organizations to efficiently execute AI workflows, encompassing everything from model training to fine-tuning and inference, while also prioritizing data management and security. With a preconfigured infrastructure tailored for AI operations, NetApp AIPod minimizes complexity, speeds up the path to insights, and ensures smooth integration in hybrid cloud settings. Furthermore, its design empowers businesses to leverage AI capabilities more effectively, ultimately enhancing their competitive edge in the market.

Effortlessly create and oversee AI applications without transferring your data through intricate pipelines or specialized vector databases. You can seamlessly connect AI and vector search directly with your existing database, allowing for real-time inference and model training. With a single, scalable deployment of all your AI models and APIs, you will benefit from automatic updates as new data flows in without the hassle of managing an additional database or duplicating your data for vector search. SuperDuperDB facilitates vector search within your current database infrastructure. You can easily integrate and merge models from Sklearn, PyTorch, and HuggingFace alongside AI APIs like OpenAI, enabling the development of sophisticated AI applications and workflows. Moreover, all your AI models can be deployed to compute outputs (inference) directly in your datastore using straightforward Python commands, streamlining the entire process. This approach not only enhances efficiency but also reduces the complexity usually involved in managing multiple data sources.

Choose a model, and we will take care of the deployment, hosting, version control, and optimization, ultimately providing you with an inference endpoint for your use. This way, you can focus on your core tasks while we manage the technical details.

Autonomy AI is an innovative platform that leverages artificial intelligence to enhance front-end development by seamlessly integrating into a business's existing codebase and workflows. By operating within the company's technology stack, it efficiently reuses and expands upon the design system and current code, effectively minimizing the accumulation of technical debt from the outset. Powered by the Agentic Context Engine (ACE), it possesses an exceptional ability to comprehend the complexities of the codebase and analyze the subtleties of Figma designs at an extraordinary level, retaining all relevant information as it progresses. Autonomy AI functions directly within the workflow, demonstrating a profound understanding of libraries, configurations, and corporate standards to produce organization-specific, production-ready code while optimizing every phase of the development process. Acting as a natural extension of the development team, it autonomously grasps tasks, iterates with independence, incorporates feedback fluidly, and accelerates the overall pace of work. This capability enables teams to focus on higher-level strategic initiatives, ultimately driving innovation and efficiency in software development.

UbiOps serves as a robust AI infrastructure platform designed to enable teams to efficiently execute their AI and ML workloads as dependable and secure microservices, all while maintaining their current workflows. In just a few minutes, you can integrate UbiOps effortlessly into your data science environment, thereby eliminating the tedious task of establishing and overseeing costly cloud infrastructure. Whether you're a start-up aiming to develop an AI product or part of a larger organization's data science unit, UbiOps provides a solid foundation for any AI or ML service you wish to implement. The platform allows you to scale your AI workloads in response to usage patterns, ensuring you only pay for what you use without incurring costs for time spent idle. Additionally, it accelerates both model training and inference by offering immediate access to powerful GPUs, complemented by serverless, multi-cloud workload distribution that enhances operational efficiency. By choosing UbiOps, teams can focus on innovation rather than infrastructure management, paving the way for groundbreaking AI solutions.

Vertesia serves as a comprehensive, low-code platform for generative AI that empowers enterprise teams to swiftly design, implement, and manage GenAI applications and agents on a large scale. Tailored for both business users and IT professionals, it facilitates a seamless development process, enabling a transition from initial prototype to final production without the need for lengthy timelines or cumbersome infrastructure. The platform accommodates a variety of generative AI models from top inference providers, granting users flexibility and reducing the risk of vendor lock-in. Additionally, Vertesia's agentic retrieval-augmented generation (RAG) pipeline boosts the precision and efficiency of generative AI by automating the content preparation process, which encompasses advanced document processing and semantic chunking techniques. With robust enterprise-level security measures, adherence to SOC2 compliance, and compatibility with major cloud services like AWS, GCP, and Azure, Vertesia guarantees safe and scalable deployment solutions. By simplifying the complexities of AI application development, Vertesia significantly accelerates the path to innovation for organizations looking to harness the power of generative AI.

Phidata serves as an open-source platform designed for the creation, deployment, and oversight of AI agents. By allowing users to craft specialized agents equipped with memory, knowledge, and the ability to utilize external tools, it significantly boosts the AI's effectiveness across various applications. The platform accommodates a diverse array of large language models and integrates effortlessly with numerous databases, vector storage solutions, and APIs. To facilitate rapid development and deployment, Phidata offers pre-built templates that empower users to seamlessly transition from agent creation to production readiness. Additionally, it features capabilities such as real-time monitoring, agent assessments, and tools for performance enhancement, which guarantee the dependability and scalability of AI implementations. Developers are also given the option to incorporate their own cloud infrastructure, providing customization flexibility for unique configurations. Moreover, Phidata emphasizes robust enterprise support, including security measures, agent guardrails, and automated DevOps processes, which contribute to a more efficient deployment experience. This comprehensive approach ensures that teams can harness the full potential of AI technology while maintaining control over their specific requirements.

Akira.ai offers organizations a suite of Agentic AI, which comprises tailored AI agents aimed at refining and automating intricate workflows across multiple sectors. These agents work alongside human teams to improve productivity, facilitate prompt decision-making, and handle monotonous tasks, including data analysis, HR operations, and incident management. The platform is designed to seamlessly integrate with current systems such as CRMs and ERPs, enabling a smooth shift to AI-driven processes without disruption. By implementing Akira’s AI agents, businesses can enhance their operational efficiency, accelerate decision-making, and foster innovation in industries such as finance, IT, and manufacturing. Ultimately, this collaboration between AI and human teams paves the way for significant advancements in productivity and operational excellence.

Agentra delivers an advanced AI workforce platform that transforms customer engagement and internal operations with intelligent, fully managed automation. Businesses can quickly deploy AI agents that handle support queries, qualify leads, manage appointments, and provide instant answers from knowledge bases. With integrations across WhatsApp, SMS, email, web chat, Slack, Teams, and CRM systems, Agentra connects seamlessly to existing workflows. Enterprise security is central to its design, featuring SOC 2, HIPAA, and SOX compliance along with end-to-end encryption and role-based access. Companies benefit from rapid 5-day deployment, no-code configuration, and a fully managed service that eliminates maintenance headaches. Its industry-specific solutions have delivered measurable results, from 300% boosts in e-commerce conversions to 90% efficiency gains in healthcare scheduling. With over 10,000 active businesses and millions of conversations automated, Agentra proves scalability and performance at enterprise level. For organizations seeking to cut costs, scale faster, and modernize customer experiences, Agentra is an AI partner that works.

The Qualcomm Cloud AI SDK serves as a robust software suite aimed at enhancing the performance of trained deep learning models for efficient inference on Qualcomm Cloud AI 100 accelerators. It accommodates a diverse array of AI frameworks like TensorFlow, PyTorch, and ONNX, which empowers developers to compile, optimize, and execute models with ease. Offering tools for onboarding, fine-tuning, and deploying models, the SDK streamlines the entire process from preparation to production rollout. In addition, it includes valuable resources such as model recipes, tutorials, and sample code to support developers in speeding up their AI projects. This ensures a seamless integration with existing infrastructures, promoting scalable and efficient AI inference solutions within cloud settings. By utilizing the Cloud AI SDK, developers are positioned to significantly boost the performance and effectiveness of their AI-driven applications, ultimately leading to more innovative solutions in the field.

Easily drag, drop, and implement tailor-made tools that suit your business requirements. Utilize the contributions of our community through ready-made workflows to enhance your productivity. Enrich your workspace with our expanding collection of nodes, opening up a myriad of opportunities. Seamlessly transfer data from point A to point Z using intuitive drag-and-click connections. Identify workflows that are tedious or repetitive as prime candidates for automation. Gumloop is designed to cater to a wide range of data processing tasks, encouraging you to unleash your creativity. Our advanced automation builder provides the capability for intricate automation, surpassing the capabilities of current solutions and integrating AI at its foundation. Our goal is not merely to optimize your workflow but to entirely transform it. You can invite colleagues to join a Workspace or simply share the URL to your automation with them. Streamline your intricate business processes effortlessly, without the need for any coding skills. For instance, just provide a link to a home listing, and the AI will automatically retrieve essential information such as pricing, address, and additional details, making your tasks even simpler. Additionally, the platform allows for real-time collaboration, enhancing team efficiency and fostering innovation.

Your software can see objects in video and images. A few dozen images can be used to train a computer vision model. This takes less than 24 hours. We support innovators just like you in applying computer vision. Upload files via API or manually, including images, annotations, videos, and audio. There are many annotation formats that we support and it is easy to add training data as you gather it. Roboflow Annotate was designed to make labeling quick and easy. Your team can quickly annotate hundreds upon images in a matter of minutes. You can assess the quality of your data and prepare them for training. Use transformation tools to create new training data. See what configurations result in better model performance. All your experiments can be managed from one central location. You can quickly annotate images right from your browser. Your model can be deployed to the cloud, the edge or the browser. Predict where you need them, in half the time.

Kluster.ai is an AI cloud platform tailored for developers, enabling quick deployment, scaling, and fine-tuning of large language models (LLMs) with remarkable efficiency. Crafted by developers with a focus on developer needs, it features Adaptive Inference, a versatile service that dynamically adjusts to varying workload demands, guaranteeing optimal processing performance and reliable turnaround times. This Adaptive Inference service includes three unique processing modes: real-time inference for tasks requiring minimal latency, asynchronous inference for budget-friendly management of tasks with flexible timing, and batch inference for the streamlined processing of large volumes of data. It accommodates an array of innovative multimodal models for various applications such as chat, vision, and coding, featuring models like Meta's Llama 4 Maverick and Scout, Qwen3-235B-A22B, DeepSeek-R1, and Gemma 3. Additionally, Kluster.ai provides an OpenAI-compatible API, simplifying the integration of these advanced models into developers' applications, and thereby enhancing their overall capabilities. This platform ultimately empowers developers to harness the full potential of AI technologies in their projects.

Replicat allows businesses to quickly create AI-driven customer support chatbots tailored to their unique needs. By training chatbots on company data such as FAQs and knowledge bases, businesses can offer real-time, 24/7 customer support without additional workload. The platform provides seamless integration with websites and other platforms, and allows easy customization of the chatbot’s appearance to match the brand’s identity. With advanced analytics and performance tracking, Replicat helps optimize chatbot responses and improve customer satisfaction, all backed by enterprise-grade security.

Nebius Token Factory is an advanced AI inference platform that enables the production of both open-source and proprietary AI models without the need for manual infrastructure oversight. It provides enterprise-level inference endpoints that ensure consistent performance, automatic scaling of throughput, and quick response times, even when faced with high request traffic. With a remarkable 99.9% uptime, it accommodates both unlimited and customized traffic patterns according to specific workload requirements, facilitating a seamless shift from testing to worldwide implementation. Supporting a diverse array of open-source models, including Llama, Qwen, DeepSeek, GPT-OSS, Flux, and many more, Nebius Token Factory allows teams to host and refine models via an intuitive API or dashboard interface. Users have the flexibility to upload LoRA adapters or fully fine-tuned versions directly, while still benefiting from the same enterprise-grade performance assurances for their custom models. This level of support ensures that organizations can confidently leverage AI technology to meet their evolving needs.

NeuReality enhances the potential of artificial intelligence by providing an innovative solution that simplifies complexity, reduces costs, and minimizes power usage. Although several companies are working on Deep Learning Accelerators (DLAs) for implementation, NeuReality stands out by integrating a software platform specifically designed to optimize the management of distinct hardware infrastructures. It uniquely connects the AI inference infrastructure with the MLOps ecosystem, creating a seamless interaction. The organization has introduced a novel architectural design that harnesses the capabilities of DLAs effectively. This new architecture facilitates inference via hardware utilizing AI-over-fabric, an AI hypervisor, and AI-pipeline offload, paving the way for more efficient AI processing. By doing so, NeuReality not only addresses current challenges in AI deployment but also sets a new standard for future advancements in the field.

Oraczen offers AI-powered solutions tailored to address complex challenges in modern enterprises. With its Zen platform, the company enables businesses to deploy agentic AI systems that automate processes and enhance decision-making in sectors like finance, healthcare, and supply chain. Oraczen’s platform ensures quick deployment (within two weeks) and robust security, enabling enterprises to integrate AI seamlessly into their operations. The platform provides a customizable approach, allowing organizations to meet evolving business needs efficiently.

Relevance AI stands out as a premier platform that enables organizations to develop and oversee autonomous AI agents and collaborative multi-agent teams, streamlining the automation of intricate tasks across diverse areas including sales, marketing, customer support, research, and operations. Its intuitive interface allows users to create AI agents without any programming skills, tailor them to adhere to unique organizational workflows, and easily integrate them with current technology systems. The platform features an assortment of ready-made agents, such as Bosh the Sales Agent, which is specifically crafted to engage prospects, arrange meetings at any hour, and deliver personalized communications, significantly boosting efficiency and scalability. With a strong focus on data privacy and security, Relevance AI is certified as SOC 2 Type II and complies with GDPR regulations, offering flexible data storage options across various regions. By utilizing Relevance AI, businesses can offload mundane tasks to AI agents, which enables their human workforce to prioritize more complex and valuable activities, ultimately fostering business expansion. This innovative approach not only enhances productivity but also positions companies to adapt swiftly to changing market dynamics.

OpenAI Frontier is an innovative platform designed for enterprises that facilitates the creation, deployment, management, and orchestration of numerous AI agents capable of executing practical tasks within established systems, workflows, and data environments. This unified framework enables organizations to seamlessly integrate AI agents, whether developed by OpenAI or external parties, with their internal tools such as CRM systems, data warehouses, and ticketing applications, ensuring that these agents operate with a shared context, permissions, memory, and oversight to effectively handle business-critical tasks. Frontier aims to transition AI agents from isolated experimental phases into fully operational production environments by offering features such as shared business context, governance controls, streamlined onboarding processes, observability, and secure access boundaries. In doing so, it empowers companies to centralize and expand their intelligent automation capabilities in a manner analogous to how human resources systems manage workforce operations, ultimately enhancing efficiency and productivity across the organization. By leveraging such a comprehensive approach, businesses can ensure that their AI agents are not only effective but also aligned with their strategic objectives.