Best Artificial Intelligence Software for Python - Page 14
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Compare the Top Artificial Intelligence Software that integrates with Python as of June 2025 - Page 14
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This a list of Artificial Intelligence software that integrates with Python. Use the filters on the left to add additional filters for products that have integrations with Python. View the products that work with Python in the table below.
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1
SuperDuperDB
SuperDuperDB
Build and manage AI applications easily without needing to move your data to complex pipelines and specialized vector databases. Integrate AI and vector search directly with your database including real-time inference and model training. A single scalable deployment of all your AI models and APIs which is automatically kept up-to-date as new data is processed immediately. No need to introduce an additional database and duplicate your data to use vector search and build on top of it. SuperDuperDB enables vector search in your existing database. Integrate and combine models from Sklearn, PyTorch, and HuggingFace with AI APIs such as OpenAI to build even the most complex AI applications and workflows. Deploy all your AI models to automatically compute outputs (inference) in your datastore in a single environment with simple Python commands. -
2
WhyLabs
WhyLabs
Enable observability to detect data and ML issues faster, deliver continuous improvements, and avoid costly incidents. Start with reliable data. Continuously monitor any data-in-motion for data quality issues. Pinpoint data and model drift. Identify training-serving skew and proactively retrain. Detect model accuracy degradation by continuously monitoring key performance metrics. Identify risky behavior in generative AI applications and prevent data leakage. Protect your generative AI applications are safe from malicious actions. Improve AI applications through user feedback, monitoring, and cross-team collaboration. Integrate in minutes with purpose-built agents that analyze raw data without moving or duplicating it, ensuring privacy and security. Onboard the WhyLabs SaaS Platform for any use cases using the proprietary privacy-preserving integration. Security approved for healthcare and banks. -
3
Martian
Martian
By using the best-performing model for each request, we can achieve higher performance than any single model. Martian outperforms GPT-4 across OpenAI's evals (open/evals). We turn opaque black boxes into interpretable representations. Our router is the first tool built on top of our model mapping method. We are developing many other applications of model mapping including turning transformers from indecipherable matrices into human-readable programs. If a company experiences an outage or high latency period, automatically reroute to other providers so your customers never experience any issues. Determine how much you could save by using the Martian Model Router with our interactive cost calculator. Input your number of users, tokens per session, and sessions per month, and specify your cost/quality tradeoff. -
4
Dryrun Security
DryRun Security
DryRun Security has been built from our experience training 10,000+ developers and security professionals in application security testing and building security products at GitHub and Signal Sciences. From our experience, one thing is missing from all tools on the market today: security context for developers. Developers make code changes all day, every day. They need a security tool that provides security context to help them move faster and safer. Security code reviews often slow down the development team and happen too late in the development pipeline. Developers need security context right when a pull request is opened, so they can know the impact of the code change that's getting merged. Until now, most security testing has taken a generic approach that frustrates developers with repetitive alerts or inaccurate results. -
5
Shakker
Shakker
With Shakker you can turn your imagination into images, in seconds. AI image generation doesn't have to be clunky when you use Shakker. Whether you want to create images, change styles, combine components, or paint any parts, Shakker makes it smoother than ever for you with prompt suggestions and precise designs. Shakker revolutionizes image creation, you can simply upload a reference photo, and it recommends styles from a library of vast images, making it easy to craft the perfect image. Beyond style transformation, Shakker offers advanced editing tools like segmentation, quick selection, and lasso for precise inpainting. Shakker.AI operates on sophisticated AI algorithms that analyze input and generate images accordingly. It interprets user commands or prompts to produce images that align with specified styles and themes. The underlying technology seamlessly blends AI's computational power with artistic creativity, delivering both unique and high-quality outputs. -
6
UbiOps
UbiOps
UbiOps is an AI infrastructure platform that helps teams to quickly run their AI & ML workloads as reliable and secure microservices, without upending their existing workflows. Integrate UbiOps seamlessly into your data science workbench within minutes, and avoid the time-consuming burden of setting up and managing expensive cloud infrastructure. Whether you are a start-up looking to launch an AI product, or a data science team at a large organization. UbiOps will be there for you as a reliable backbone for any AI or ML service. Scale your AI workloads dynamically with usage without paying for idle time. Accelerate model training and inference with instant on-demand access to powerful GPUs enhanced with serverless, multi-cloud workload distribution. -
7
Gemma
Google
Gemma is a family of lightweight, state-of-the-art open models built from the same research and technology used to create the Gemini models. Developed by Google DeepMind and other teams across Google, Gemma is inspired by Gemini, and the name reflects the Latin gemma, meaning “precious stone.” Accompanying our model weights, we’re also releasing tools to support developer innovation, foster collaboration, and guide the responsible use of Gemma models. Gemma models share technical and infrastructure components with Gemini, our largest and most capable AI model widely available today. This enables Gemma 2B and 7B to achieve best-in-class performance for their sizes compared to other open models. And Gemma models are capable of running directly on a developer laptop or desktop computer. Notably, Gemma surpasses significantly larger models on key benchmarks while adhering to our rigorous standards for safe and responsible outputs. -
8
Zama
Zama
Improve patient care while maintaining privacy by allowing secure, confidential data sharing between healthcare providers. Facilitate secure financial data analysis for risk management and fraud detection, keeping client information encrypted and safe. Create targeted advertising and campaign insights in a post-cookie era, ensuring user privacy through encrypted data analysis. Enable data collaboration between different agencies, while keeping it confidential from each other, enhancing efficiency and data security, without revealing secrets. Give the ability to create user authentication applications without having to reveal their identities. Enable governments to create digitized versions of their services without having to trust cloud providers. -
9
Superlinked
Superlinked
Combine semantic relevance and user feedback to reliably retrieve the optimal document chunks in your retrieval augmented generation system. Combine semantic relevance and document freshness in your search system, because more recent results tend to be more accurate. Build a real-time personalized ecommerce product feed with user vectors constructed from SKU embeddings the user interacted with. Discover behavioral clusters of your customers using a vector index in your data warehouse. Describe and load your data, use spaces to construct your indices and run queries - all in-memory within a Python notebook. -
10
Anon
Anon
Anon offers two powerful ways to integrate your applications with services that lack APIs, enabling you to build innovative solutions and automate workflows like never before. The API packages pre-built automation on popular services that don’t offer APIs and are the simplest way to use Anon. The toolkit to build user-permissions integrations for sites without APIs. Using Anon, developers can enable agents to authenticate and take actions on behalf of users across the most popular sites on the internet. Programmatically interact with the most popular messaging services. The runtime SDK is an authentication toolkit that lets AI agent developers build their own integrations on popular services that don’t offer APIs. Anon simplifies the work of building and maintaining user-permission integrations across platforms, languages, auth types, and services. We build the annoying infra so you can build amazing apps. -
11
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. -
12
GaiaNet
GaiaNet
The API approach allows any agent application in the OpenAI ecosystem, which is 100% of AI agents today, to use GaiaNet as an alternative to OpenAI. Furthermore, while the OpenAI API is backed by a handful of models to give generic responses, each GaiaNet node can be heavily customized with a finetuned model supplemented by domain knowledge. GaiaNet is a decentralized computing infrastructure that enables everyone to create, deploy, scale, and monetize their own AI agents that reflect their styles, values, knowledge, and expertise. It allows individuals and businesses to create AI agents. Each GaiaNet node provides. A distributed and decentralized network of GaiaNodes. Fine-tuned large language models with private data. Proprietary knowledge base that individuals or enterprises have to improve the performance of the model. Decentralized AI apps that utilize the API of the distributed GaiaNet infrastructure. Offers personal AI teaching assistants, ready to enlighten at any place & time. -
13
Gemma 2
Google
A family of state-of-the-art, light-open models created from the same research and technology that were used to create Gemini models. These models incorporate comprehensive security measures and help ensure responsible and reliable AI solutions through selected data sets and rigorous adjustments. Gemma models achieve exceptional comparative results in their 2B, 7B, 9B, and 27B sizes, even outperforming some larger open models. With Keras 3.0, enjoy seamless compatibility with JAX, TensorFlow, and PyTorch, allowing you to effortlessly choose and change frameworks based on task. Redesigned to deliver outstanding performance and unmatched efficiency, Gemma 2 is optimized for incredibly fast inference on various hardware. The Gemma family of models offers different models that are optimized for specific use cases and adapt to your needs. Gemma models are large text-to-text lightweight language models with a decoder, trained in a huge set of text data, code, and mathematical content. -
14
ModelOp
ModelOp
ModelOp is the leading AI governance software that helps enterprises safeguard all AI initiatives, including generative AI, Large Language Models (LLMs), in-house, third-party vendors, embedded systems, etc., without stifling innovation. Corporate boards and C‑suites are demanding the rapid adoption of generative AI but face financial, regulatory, security, privacy, ethical, and brand risks. Global, federal, state, and local-level governments are moving quickly to implement AI regulations and oversight, forcing enterprises to urgently prepare for and comply with rules designed to prevent AI from going wrong. Connect with AI Governance experts to stay informed about market trends, regulations, news, research, opinions, and insights to help you balance the risks and rewards of enterprise AI. ModelOp Center keeps organizations safe and gives peace of mind to all stakeholders. Streamline reporting, monitoring, and compliance adherence across the enterprise. -
15
KaneAI
LambdaTest
Advanced AI-powered platform built on modern Large Language Models (LLMs). A unique approach to create, debug, and evolve end to end tests using natural language. Test generation & evolution effortlessly using natural language inputs, simplifying the testing process with intelligent automation. Intelligent test planner automatically generates and automates test steps using high-level objectives. Multi-language code export converts your automated tests in all major languages and frameworks. Convert your actions into natural language instructions to generate bulletproof tests. Express sophisticated conditions and assertions in natural language. As easy as conversing and communicating with your team. Convey the same instructions to KaneAI and watch it automate your tests. Generate your tests with just high-level objectives. Develop tests across your stack on both web and mobile devices for extensive test coverage. -
16
StackGen
StackGen
Generate context-aware, secure IaC from application code without code changes. We love infrastructure as code, but that doesn’t mean there isn’t room for improvement. StackGen uses an application’s code to generate consistent, secure, and compliant IaC. Remove bottlenecks, liabilities, and error-prone manual processes between DevOps, developers, and security to get your application to market faster. Allow developers a better, more productive experience without becoming infrastructure experts. Consistency, security, and policy guardrails are incorporated by default when IaC is auto-generated. Context-aware IaC is auto-generated, with no code changes required, supported, and rightsized with least-privileged access controls. No need to rebuild your pipelines. StackGen works alongside your existing workflows to remove silos between teams. Enable developers to auto-generate IaC that complies with your provisioning checklist. -
17
Runyour AI
Runyour AI
From renting machines for AI research to specialized templates and servers, Runyour AI provides the optimal environment for artificial intelligence research. Runyour AI is an AI cloud service that provides easy access to GPU resources and research environments for artificial intelligence research. You can rent various high-performance GPU machines and environments at a reasonable price. Additionally, you can register your own GPUs to generate revenue. Transparent billing policy where you pay for charging points used through minute-by-minute real-time monitoring. From casual hobbyists to seasoned researchers, we provide specialized GPUs for AI projects, catering to a range of needs. An AI project environment that is easy and convenient for even first-time users. By utilizing Runyour AI's GPU machines, you can kickstart your AI research with minimal setup. Designed for quick access to GPUs, it provides a seamless research environment for machine learning and AI development. -
18
Outspeed
Outspeed
Outspeed provides networking and inference infrastructure to build fast, real-time voice and video AI apps. AI-powered speech recognition, natural language processing, and text-to-speech for intelligent voice assistants, automated transcription, and voice-controlled systems. Create interactive digital characters for virtual hosts, AI tutors, or customer service. Enable real-time animation and natural conversations for engaging digital interactions. Real-time visual AI for quality control, surveillance, touchless interactions, and medical imaging analysis. Process and analyze video streams and images with high speed and accuracy. AI-driven content generation for creating vast, detailed digital worlds efficiently. Ideal for game environments, architectural visualizations, and virtual reality experiences. Create custom multimodal AI solutions with Adapt's flexible SDK and infrastructure. Combine AI models, data sources, and interaction modes for innovative applications. -
19
poolside
poolside
poolside is building next-generation AI for software engineering. A model built specifically for the challenges of modern software engineering. Fine-tune our model on how your business writes software, using your practices, libraries, APIs, and knowledge bases. Your proprietary model continuously learns how your developers write code. You become an AI company. We're building foundation models, an API, and an assistant to bring the power of generative AI to your developers. The poolside stack can be deployed to your own infrastructure. No data or code ever leaves your security boundary. Ideal for highly regulated industries like financial services, defense, and technology as well as retail, tech, and systems integrators. Your model ingests your codebases, documentation & knowledge bases to create a model that is uniquely suited to your dev teams & business. poolside is deployed in your environment which allows you to securely and privately connect it to your data. -
20
Algoreus
Turium AI
All your data needs are delivered in one powerful platform. From data ingestion/integration, transformation, and storage to knowledge catalog, graph networks, data analytics, governance, monitoring, and, sharing. An AI/ML platform that lets enterprises, train, test, troubleshoot, deploy, and govern models at scale to boost productivity while maintaining model performance in production with confidence. A dedicated solution for training models with minimal effort through AutoML or training your case-specific models from scratch with CustomML. Giving you the power to connect essential logic from ML with data. An integrated exploration of possible actions. Integration with your protocols and authorization models. Propagation by default; extreme configurability at your service. Leverage internal lineage system, for alerting and impact analysis. Interwoven with the security paradigm; provides immutable tracking. -
21
Invert
Invert
Invert offers a complete suite for collecting, cleaning, and contextualizing data, ensuring every analysis and insight is based on reliable, organized data. Invert collects and standardizes all your bioprocess data, with powerful, built-in products for analysis, machine learning, and modeling. Clean, standardized data is just the beginning. Explore our suite of data management, analysis, and modeling tools. Replace manual workflows in spreadsheets or statistical software. Calculate anything using powerful statistical features. Automatically generate reports based on recent runs. Add interactive plots, calculations, and comments and share with internal or external collaborators. Streamline planning, coordination, and execution of experiments. Easily find the data you need, and deep dive into any analysis you'd like. From integration to analysis to modeling, find all the tools you need to manage and make sense of your data. -
22
Literal AI
Literal AI
Literal AI is a collaborative platform designed to assist engineering and product teams in developing production-grade Large Language Model (LLM) applications. It offers a suite of tools for observability, evaluation, and analytics, enabling efficient tracking, optimization, and integration of prompt versions. Key features include multimodal logging, encompassing vision, audio, and video, prompt management with versioning and AB testing capabilities, and a prompt playground for testing multiple LLM providers and configurations. Literal AI integrates seamlessly with various LLM providers and AI frameworks, such as OpenAI, LangChain, and LlamaIndex, and provides SDKs in Python and TypeScript for easy instrumentation of code. The platform also supports the creation of experiments against datasets, facilitating continuous improvement and preventing regressions in LLM applications. -
23
PromptQL
Hasura
PromptQL is a platform developed by Hasura that enables Large Language Models (LLMs) to access and interact with structured data sources through agentic query planning. This approach allows AI agents to retrieve and process data in a human-like manner, enhancing their ability to handle complex, real-world user queries. By providing LLMs with access to a Python runtime and a standardized SQL interface, PromptQL facilitates accurate data querying and manipulation. The platform supports integration with various data sources, including GitHub repositories and PostgreSQL databases, allowing users to build AI assistants tailored to their specific needs. PromptQL addresses the limitations of traditional search-based retrieval methods by enabling AI agents to perform tasks such as gathering relevant emails and classifying follow-ups with greater accuracy. Users can get started by connecting their data, adding their LLM API key, and building with AI. -
24
MLBox
Axel ARONIO DE ROMBLAY
MLBox is a powerful Automated Machine Learning python library. It provides the following features fast reading and distributed data preprocessing/cleaning/formatting, highly robust feature selection and leak detection, accurate hyper-parameter optimization in high-dimensional space, state-of-the art predictive models for classification and regression (Deep Learning, Stacking, LightGBM), and prediction with models interpretation. MLBox main package contains 3 sub-packages: preprocessing, optimization and prediction. Each one of them are respectively aimed at reading and preprocessing data, testing or optimizing a wide range of learners and predicting the target on a test dataset. -
25
Ludwig
Uber AI
Ludwig is a low-code framework for building custom AI models like LLMs and other deep neural networks. Build custom models with ease: a declarative YAML configuration file is all you need to train a state-of-the-art LLM on your data. Support for multi-task and multi-modality learning. Comprehensive config validation detects invalid parameter combinations and prevents runtime failures. Optimized for scale and efficiency: automatic batch size selection, distributed training (DDP, DeepSpeed), parameter efficient fine-tuning (PEFT), 4-bit quantization (QLoRA), and larger-than-memory datasets. Expert level control: retain full control of your models down to the activation functions. Support for hyperparameter optimization, explainability, and rich metric visualizations. Modular and extensible: experiment with different model architectures, tasks, features, and modalities with just a few parameter changes in the config. Think building blocks for deep learning. -
26
AutoKeras
AutoKeras
An AutoML system based on Keras. It is developed by DATA Lab at Texas A&M University. The goal of AutoKeras is to make machine learning accessible to everyone. AutoKeras supports several tasks with an extremely simple interface. -
27
HACARUS Check
HACARUS Inc.
HACARUS' AI Core is uniquely differentiated in that it is able to create highly accurate defect detection models from small data sets, trained with only good data, and can be adapted to changing production conditions with simple adjustments. HACARUS Check AI software is a complete inspection software suite, built with the human operator in mind. The intuitive application features a full graphical interface and allows for the creation of powerful AI models with ease. Training a new inspection model requires only a small, good only, data set, and predictions are near instantaneous. Defects detected are highlighted with helpful features such as bounding boxes and heat maps. -
28
Prompt flow
Microsoft
Prompt Flow is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, and evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality. With Prompt Flow, you can create flows that link LLMs, prompts, Python code, and other tools together in an executable workflow. It allows for debugging and iteration of flows, especially tracing interactions with LLMs with ease. You can evaluate your flows, calculate quality and performance metrics with larger datasets, and integrate the testing and evaluation into your CI/CD system to ensure quality. Deployment of flows to the serving platform of your choice or integration into your app’s code base is made easy. Additionally, collaboration with your team is facilitated by leveraging the cloud version of Prompt Flow in Azure AI. -
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Tumeryk
Tumeryk
Tumeryk Inc. specializes in advanced generative AI security solutions, offering tools like the AI trust score for real-time monitoring, risk management, and compliance. Our platform empowers organizations to secure AI systems, ensuring reliable, trustworthy, and policy-aligned deployments. The AI Trust Score quantifies the risk of using generative AI systems, enabling compliance with regulations like the EU AI Act, ISO 42001, and NIST RMF 600.1. This score evaluates and scores the trustworthiness of generated prompt responses, accounting for risks including bias, jailbreak propensity, off-topic responses, toxicity, Personally Identifiable Information (PII) data leakage, and hallucinations. It can be integrated into business processes to help determine whether content should be accepted, flagged, or blocked, thus allowing organizations to mitigate risks associated with AI-generated content. -
30
Smolagents
Smolagents
Smolagents is an AI agent framework developed to simplify the creation and deployment of intelligent agents with minimal code. It supports code-first agents where agents execute Python code snippets to perform tasks, offering enhanced efficiency compared to traditional JSON-based approaches. Smolagents integrates with large language models like those from Hugging Face, OpenAI, and others, enabling developers to create agents that can control workflows, call functions, and interact with external systems. The framework is designed to be user-friendly, requiring only a few lines of code to define and execute agents. It features secure execution environments, such as sandboxed spaces, for safe code running. Smolagents also promotes collaboration by integrating deeply with the Hugging Face Hub, allowing users to share and import tools. It supports a variety of use cases, from simple tasks to multi-agent workflows, offering flexibility and performance improvements.