Best AI Development Platforms of 2024

ShipGPT is an AI repository that contains a ready-made boilerplate for a variety of AI use cases. It allows you to create your own AI applications or integrate AI into existing tech, without the need to hire full stack developers or AI dev wrappers. ShipGPT allows you to transform your apps into AI apps and create products such as ChatBase, ChatPDF or Jenni AI. The service includes live support as well as continuous updates. The service is designed for developers to create AI apps quickly. It is regularly updated. It does not rely on licensed or third-party libraries or APIs. Instead, it relies on open-source libraries that are easy to maintain.

Set up your workspace easily by integrating GPT language models with your provider key for unparalleled performance. LLM Spark's GPT templates can be used to create AI applications quickly. Or, you can start from scratch and create unique projects. Test and compare multiple models at the same time to ensure optimal performance in multiple scenarios. Save versions and history with ease while streamlining development. Invite others to your workspace so they can collaborate on projects. Semantic search is a powerful search tool that allows you to find documents by meaning and not just keywords. AI applications can be made accessible across platforms by deploying trained prompts.

PostgresML is an entire platform that comes as a PostgreSQL Extension. Build simpler, faster and more scalable model right inside your database. Explore the SDK, and test open-source models in our hosted databases. Automate the entire workflow, from embedding creation to indexing and Querying for the easiest (and fastest) knowledge based chatbot implementation. Use multiple types of machine learning and natural language processing models, such as vector search or personalization with embeddings, to improve search results. Time series forecasting can help you gain key business insights. SQL and dozens regression algorithms allow you to build statistical and predictive models. ML at database layer can detect fraud and return results faster. PostgresML abstracts data management overheads from the ML/AI cycle by allowing users to run ML/LLM on a Postgres Database.

The open-source ML observability Platform. From validation to production, evaluate, test, and track ML models. From tabular data up to NLP and LLM. Built for data scientists and ML Engineers. All you need to run ML systems reliably in production. Start with simple ad-hoc checks. Scale up to the full monitoring platform. All in one tool with consistent APIs and metrics. Useful, beautiful and shareable. Explore and debug a comprehensive view on data and ML models. Start in a matter of seconds. Test before shipping, validate in production, and run checks with every model update. By generating test conditions based on a reference dataset, you can skip the manual setup. Monitor all aspects of your data, models and test results. Proactively identify and resolve production model problems, ensure optimal performance and continually improve it.

Experience JinaChat - a LLM service designed for professionals. JinaChat is a multimodal chat service that goes beyond text and includes images. Enjoy our free short interactions below 100 tokens. Our API allows developers to build complex applications by leveraging long conversation histories. JinaChat is the future of LLM, with multimodal conversations that are long-memory and affordable. Modern LLM applications are often based on long prompts or large memory, which can lead to high costs if the same prompts are sent repeatedly to the server. JinaChat API solves this issue by allowing you to carry forward previous conversations, without having to resend the entire prompt. This is a great way to save both time and money when developing complex applications such as AutoGPT.

Vishwa.ai, an AutoOps Platform for AI and ML Use Cases. It offers expert delivery, fine-tuning and monitoring of Large Language Models. Features: Expert Prompt Delivery : Tailored prompts tailored to various applications. Create LLM Apps without Coding: Create LLM workflows with our drag-and-drop UI. Advanced Fine-Tuning : Customization AI models. LLM Monitoring: Comprehensive monitoring of model performance. Integration and Security Cloud Integration: Supports Google Cloud (AWS, Azure), Azure, and Google Cloud. Secure LLM Integration - Safe connection with LLM providers Automated Observability for efficient LLM Management Managed Self Hosting: Dedicated hosting solutions. Access Control and Audits - Ensure secure and compliant operations.

LLMFuzzer will be the perfect tool for anyone who is a security enthusiast, a researcher in cybersecurity, or a pentester. They love to find and exploit AI system vulnerabilities. It's designed to make your testing process efficient and streamlined. We are currently working on a full documentation. It will include detailed information about the architecture, various fuzzing techniques, examples, as well as how to extend the tool.

Choose the appropriate app type. Choose the output and input required for the app. Add data from existing sources such as files, URLs, sitemaps and YouTube links. You can also add data from Notion exports, Google Drive and other sources. Attach these data sources in the app builder. Save and publish your application. You can embed the code in your website or access it directly from the dedicated app page. Our APIs allow you to run the app directly from your application. Promptly offers embeddable widgets you can easily integrate in your website. These widgets can be used to create conversational AI applications, or to add a bot to your website. Customize the look and feel of the chatbot to match your website. Include a logo.

Lilac is a free open-source tool that allows data and AI practitioners improve their products through better data. Understanding your data is easy with powerful filtering and search. Work together with your team to create a single dataset. Use best practices for data curation to reduce the size of your dataset and training costs and time. Our diff viewer allows you to see how your pipeline affects your data. Clustering is an automatic technique that assigns categories to documents by analyzing their text content. Similar documents are then placed in the same category. This reveals your dataset's overall structure. Lilac uses LLMs and state-of-the art algorithms to cluster the data and assign descriptive, informative titles. We can use keyword search before we do advanced searches, such as concept or semantic searching.

Even the most complex requests from users can be fulfilled by our advanced planning engine. Out-of-the-box automation, inside your product. Your users can ask the system to do amazing things by using simple text, such as "Please show me sales from last month and give me some suggestions". OpenCopilot can be integrated into your product by using our chat bubble. No coding knowledge is required. You can also use our SDKs for your copilot to blend in. You can also feed all kinds of data to your copilot and it will be capable of understanding it and offering help to your users. OpenCopilot can be hosted on your own website with a simple make install command. All paid plans come with personal support from our team. Your users can ask complicated questions that require multiple actions to be executed in one go. The single platform for building, managing, and deploying your next AI-powered features. You will be the first to get new features, which is great since we ship so much.

OpenPipe provides fine-tuning for developers. Keep all your models, datasets, and evaluations in one place. New models can be trained with a click of a mouse. Automatically record LLM responses and requests. Create datasets using your captured data. Train multiple base models using the same dataset. We can scale your model to millions of requests on our managed endpoints. Write evaluations and compare outputs of models side by side. You only need to change a few lines of code. OpenPipe API Key can be added to your Python or Javascript OpenAI SDK. Custom tags make your data searchable. Small, specialized models are much cheaper to run than large, multipurpose LLMs. Replace prompts in minutes instead of weeks. Mistral and Llama 2 models that are fine-tuned consistently outperform GPT-4-1106 Turbo, at a fraction the cost. Many of the base models that we use are open-source. You can download your own weights at any time when you fine-tune Mistral or Llama 2.

Query and compare multiple proprietary and open-source models simultaneously. Replace expensive APIs with custom AI models. Customize foundational AI models using your private data and adapt them to fit your specific use case. Small, fine-tuned models perform at the same level as GPT-4 while being up to 90% less expensive. Airtrain's LLM-assisted scoring simplifies model grading using your task descriptions. Airtrain's API allows you to serve your custom models in the cloud, or on your own secure infrastructure. Evaluate and compare proprietary and open-source models across your entire dataset using custom properties. Airtrain's powerful AI evaluation tools let you score models based on arbitrary properties to create a fully customized assessment. Find out which model produces outputs that are compliant with the JSON Schema required by your agents or applications. Your dataset is scored by models using metrics such as length and compression.

PlugBear provides a low-code/no-code solution to connect communication channels with LLM applications (Large Language Model). It allows, for example, the creation of a Slack Bot from an LLM application in just a few simple clicks. PlugBear is notified when a trigger event occurs on the integrated channels. It then transforms messages into LLM applications, and initiates generation. PlugBear then transforms the generated results so that they are compatible with each channel. This allows users to interact with LLM applications seamlessly across different channels.

Fireworks works with the leading generative AI researchers in the world to provide the best models at the fastest speed. Independently benchmarked for the fastest inference providers. Use models curated by Fireworks, or our multi-modal and functionality-calling models that we have trained in-house. Fireworks is also the 2nd most popular open-source model provider, and generates more than 1M images/day. Fireworks' OpenAI-compatible interface makes it simple to get started. Dedicated deployments of your models will ensure uptime and performance. Fireworks is HIPAA-compliant and SOC2-compliant and offers secure VPC connectivity and VPN connectivity. Own your data and models. Fireworks hosts serverless models, so there's no need for hardware configuration or deployment. Fireworks.ai provides a lightning fast inference platform to help you serve generative AI model.

Multi-model AI can help you build a better AI. AI models are an entirely new type of building block. Sieve makes it easy to use these building block to understand audio, create video, and more. The latest models are available in just a few line of code and there is a set of production-ready applications for many different use cases. Import your favorite models like Python packages. Visualize results using auto-generated interfaces created for your entire team. Easily deploy custom code. Define your environment computation in code and deploy it with a single command. Fast, scalable infrastructure with no hassle. Sieve is designed to scale automatically as your traffic grows with no extra configuration. Package models using a simple Python decorator, and deploy them instantly. A fully-featured observability layer that allows you to see what's going on under the hood. Pay only for the seconds you use. Take full control of your costs.

Use AI algorithms to receive intelligent suggestions about your API design. Automated recommendations tailored to your project will accelerate development. AI can automatically generate your database. Streamline deployment and increase productivity. Create and implement automated workflows to ensure a smooth, efficient development cycle. Customize automation processes to meet your project requirements. Workflows that are adaptable will allow you to create a personalized experience. Enjoy the flexibility to manage diverse data sources in a single, organized workspace. Workspaces can be designed to reflect the structure of projects. Create dedicated workspaces that can house multiple data sources seamlessly. Streamlining tasks by automating processes, increasing efficiency, and reducing the amount of manual effort. Each user has their own instance(s). Custom logic can be used to handle complex data operations.

LangWatch is a vital part of AI maintenance. It protects you and your company from exposing sensitive information, prevents prompt injection, and keeps your AI on track, preventing unforeseen damage to the brand. Businesses with integrated AI can find it difficult to understand the behaviour of AI and users. Maintaining quality by monitoring will ensure accurate and appropriate responses. LangWatch's safety check and guardrails help prevent common AI problems, such as jailbreaking, exposing sensitive information, and off-topic discussions. Real-time metrics allow you to track conversion rates, output, user feedback, and knowledge base gaps. Gain constant insights for continuous improvements. Data evaluation tools allow you to test new models and prompts and run simulations.

Emly Labs, an AI framework, is designed to make AI accessible to users of all technical levels via a user-friendly interface. It offers AI project-management with tools that automate workflows for faster execution. The platform promotes team collaboration, innovation, and data preparation without code. It also integrates external data to create robust AI models. Emly AutoML automates model evaluation and data processing, reducing the need for human input. It prioritizes transparency with AI features that are easily explained and robust auditing to ensure compliance. Data isolation, role-based accessibility, and secure integrations are all security measures. Emly's cost effective infrastructure allows for on-demand resource provisioning, policy management and risk reduction.

Learn how to choose the right LLM based on your needs, and how you can optimize quality, speed and cost-efficiency. With a single API and standard API, you can access all LLMs from all providers. Set your own constraints for output speed, latency and cost. Define your own quality metric. Personalize your router for your requirements. Send your queries to the fastest providers based on the latest benchmark data for the region you are in, updated every 10 minutes. Unify's dedicated walkthrough will help you get started. Discover the features that you already have and our upcoming roadmap. Create a Unify Account to access all models supported by all providers using a single API Key. Our router balances output speed, quality, and cost according to user preferences. The quality of the output is predicted using a neural scoring system, which predicts each model's ability to respond to a given prompt.

Frontly is an advanced SaaS/web app builder that does not require any coding. It offers powerful features and a rapid development process. Frontly is different from other app builders in that it allows users to create apps and components using AI, to build and maintain a library of reusable components and to duplicate entire apps and web pages. Users can also share and sell their designs on Frontly's marketplace for revenue. OpenAI allows you to automate repetitive tasks, edit and generate content using structured output. Transform your spreadsheets with charts, tables, forms and more into stunning visual interfaces. Access controls allow you to control which data users see down to the individual row level. Add your logo, brand colours, and custom domain to create a professional client-facing portal. Create apps in minutes that display data in a customized visual interface, filter the data based on permissions, generate with AI and revise your data, and connect your external systems via workflow automation integrations.

Caffe is a deep-learning framework that focuses on expression, speed and modularity. It was developed by Berkeley AI Research (BAIR), and community contributors. The project was created by Yangqing Jia during his PhD at UC Berkeley. Caffe is available under the BSD 2-Clause License. Check out our web image classification demo! Expressive architecture encourages innovation and application. Configuration is all that is required to define models and optimize them. You can switch between CPU and GPU by setting one flag to train on a GPU, then deploy to commodity clusters of mobile devices. Extensible code fosters active development. Caffe was forked by more than 1,000 developers in its first year. Many significant changes were also made back. These contributors helped to track the state of the art in code and models. Caffe's speed makes it ideal for industry deployment and research experiments. Caffe can process more than 60M images per hour using a single NVIDIA GPU K40.

Accelerate the entire machine learning lifecycle. Developers and data scientists can have more productive experiences building, training, and deploying machine-learning models faster by empowering them. Accelerate time-to-market and foster collaboration with industry-leading MLOps -DevOps machine learning. Innovate on a trusted platform that is secure and trustworthy, which is designed for responsible ML. Productivity for all levels, code-first and drag and drop designer, and automated machine-learning. Robust MLOps capabilities integrate with existing DevOps processes to help manage the entire ML lifecycle. Responsible ML capabilities – understand models with interpretability, fairness, and protect data with differential privacy, confidential computing, as well as control the ML cycle with datasheets and audit trials. Open-source languages and frameworks supported by the best in class, including MLflow and Kubeflow, ONNX and PyTorch. TensorFlow and Python are also supported.

Amazon Augmented AI (Amazon A2I), makes it easy to create the workflows needed for human review of ML prediction. Amazon A2I provides human review for all developers. This removes the undifferentiated work involved in building systems that require human review or managing large numbers. Machine learning applications often require humans to review low confidence predictions in order to verify that the results are accurate. In some cases, such as extracting information from scanned mortgage applications forms, human review may be required due to poor scan quality or handwriting. However, building human review systems can be costly and time-consuming because it involves complex processes or "workflows", creating custom software to manage review tasks, results, and managing large numbers of reviewers.

You can build, run, and manage AI models and optimize decisions across any cloud. IBM Watson Studio allows you to deploy AI anywhere with IBM Cloud Pak®, the IBM data and AI platform. Open, flexible, multicloud architecture allows you to unite teams, simplify the AI lifecycle management, and accelerate time-to-value. ModelOps pipelines automate the AI lifecycle. AutoAI accelerates data science development. AutoAI allows you to create and programmatically build models. One-click integration allows you to deploy and run models. Promoting AI governance through fair and explicable AI. Optimizing decisions can improve business results. Open source frameworks such as PyTorch and TensorFlow can be used, as well as scikit-learn. You can combine the development tools, including popular IDEs and Jupyter notebooks. JupterLab and CLIs. This includes languages like Python, R, and Scala. IBM Watson Studio automates the management of the AI lifecycle to help you build and scale AI with trust.

An end-to-end solution gives you all the tools your data science team needs to scale your machine learning development, from research to production. cnvrg.io, the world's leading data science platform for MLOps (model management) is a leader in creating cutting-edge machine-learning development solutions that allow you to build high-impact models in half the time. In a collaborative and clear machine learning management environment, bridge science and engineering teams. Use interactive workspaces, dashboards and model repositories to communicate and reproduce results. You should be less concerned about technical complexity and more focused on creating high-impact ML models. The Cnvrg.io container based infrastructure simplifies engineering heavy tasks such as tracking, monitoring and configuration, compute resource management, server infrastructure, feature extraction, model deployment, and serving infrastructure.