Find and compare the best Artificial Intelligence software for Databricks Data Intelligence Platform in 2025
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Use the comparison tool below to compare the top Artificial Intelligence software for Databricks Data Intelligence Platform on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.
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Supaboard
Supaboard
$82 per monthSupaboard is an innovative business intelligence solution that leverages artificial intelligence to empower users to analyze their data and craft real-time dashboards simply by posing questions in everyday language. It allows for seamless one-click integration with more than 60 different data sources such as MySQL, PostgreSQL, Google Analytics, Shopify, Salesforce, and Notion, enabling users to harmonize their data effortlessly without complicated configurations. With pre-trained AI analysts tailored to specific industries, the platform automatically generates SQL and NoSQL queries, delivering quick insights through visual formats like charts, tables, and summaries. Users can easily create and customize dashboards by pinning their inquiries and adjusting the information presented according to various audience needs through filtered views. Supaboard prioritizes data security by only connecting with read-only permissions, retaining only schema metadata, and utilizing detailed access controls to safeguard information. Built with user-friendliness in mind, it significantly reduces operational complexity, allowing businesses to make informed decisions up to ten times faster, all without the necessity for coding skills or advanced data knowledge. Furthermore, this platform empowers teams to become more agile in their data-driven strategies, ultimately enhancing overall business performance. -
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FeatureByte
FeatureByte
FeatureByte acts as your AI data scientist, revolutionizing the entire data lifecycle so that processes that previously required months can now be accomplished in mere hours. It is seamlessly integrated with platforms like Databricks, Snowflake, BigQuery, or Spark, automating tasks such as feature engineering, ideation, cataloging, creating custom UDFs (including transformer support), evaluation, selection, historical backfill, deployment, and serving—whether online or in batch—all within a single, cohesive platform. The GenAI-inspired agents from FeatureByte collaborate with data, domain, MLOps, and data science experts to actively guide teams through essential processes like data acquisition, ensuring quality, generating features, creating models, orchestrating deployments, and ongoing monitoring. Additionally, FeatureByte offers an SDK and an intuitive user interface that facilitate both automated and semi-automated feature ideation, customizable pipelines, cataloging, lineage tracking, approval workflows, role-based access control, alerts, and version management, which collectively empower teams to rapidly and reliably construct, refine, document, and serve features. This comprehensive solution not only enhances efficiency but also ensures that teams can adapt to changing data requirements and maintain high standards in their data operations. -
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Claude for Financial Services
Anthropic
Claude for Financial Services revolutionizes the realm of institutional finance by integrating cutting-edge AI with robust financial data infrastructure and top-tier security into a cohesive intelligence framework. This Financial Analysis Solution seamlessly links key data sources, including S&P Global, Daloopa, and various internal systems within a single interface, ensuring that each data point is directly traceable to its original source for immediate verification and complete transparency. Designed with privacy at its core, it guarantees that your inputs and outputs will not contribute to the training of the underlying models by default. The platform also features guided onboarding and MCP-based connectors, facilitating smooth deployment in diverse environments such as banking, insurance, asset management, and fintech. With its native workflows and intelligent search functions, teams can efficiently handle intricate financial tasks on a large scale, quickly verifying information across numerous sources to minimize errors and conduct detailed analyses in mere minutes instead of hours. This innovative approach not only enhances productivity but also empowers financial professionals to make more informed decisions rapidly. -
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Airia
Airia
$49 per monthAiria’s enterprise AI orchestration platform effortlessly connects with current systems and data sources while providing a no-code agent builder that allows for quick prototyping. It features pre-built connectors for cohesive data integration, along with intelligent AI operations that enhance both performance and cost efficiency through smart routing and centralized lifecycle management. The platform also emphasizes enterprise-grade security and governance, including comprehensive audit capabilities and responsible AI guardrails. Being model-agnostic and vendor-neutral, it allows for adaptable deployment options across shared or dedicated cloud, private cloud, and on-premises setups. This flexibility empowers both technical and non-technical users to construct, implement, and oversee secure AI agents on a large scale without the need for intricate installations or migrations. With its user-friendly interface and cohesive platform, Airia revolutionizes workflows across various departments, including engineering, IT, finance, legal, marketing, sales, and support, enabling organizations to confidently and compliantly advance their AI initiatives. Ultimately, this comprehensive solution positions businesses to harness the full potential of AI while streamlining operations and ensuring security. -
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TiMi
TIMi
TIMi allows companies to use their corporate data to generate new ideas and make crucial business decisions more quickly and easily than ever before. The heart of TIMi’s Integrated Platform. TIMi's ultimate real time AUTO-ML engine. 3D VR segmentation, visualization. Unlimited self service business Intelligence. TIMi is a faster solution than any other to perform the 2 most critical analytical tasks: data cleaning, feature engineering, creation KPIs, and predictive modeling. TIMi is an ethical solution. There is no lock-in, just excellence. We guarantee you work in complete serenity, without unexpected costs. TIMi's unique software infrastructure allows for maximum flexibility during the exploration phase, and high reliability during the production phase. TIMi allows your analysts to test even the most crazy ideas. -
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Privacera
Privacera
Multi-cloud data security with a single pane of glass Industry's first SaaS access governance solution. Cloud is fragmented and data is scattered across different systems. Sensitive data is difficult to access and control due to limited visibility. Complex data onboarding hinders data scientist productivity. Data governance across services can be manual and fragmented. It can be time-consuming to securely move data to the cloud. Maximize visibility and assess the risk of sensitive data distributed across multiple cloud service providers. One system that enables you to manage multiple cloud services' data policies in a single place. Support RTBF, GDPR and other compliance requests across multiple cloud service providers. Securely move data to the cloud and enable Apache Ranger compliance policies. It is easier and quicker to transform sensitive data across multiple cloud databases and analytical platforms using one integrated system. -
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MLflow
MLflow
MLflow is an open-source suite designed to oversee the machine learning lifecycle, encompassing aspects such as experimentation, reproducibility, deployment, and a centralized model registry. The platform features four main components that facilitate various tasks: tracking and querying experiments encompassing code, data, configurations, and outcomes; packaging data science code to ensure reproducibility across multiple platforms; deploying machine learning models across various serving environments; and storing, annotating, discovering, and managing models in a unified repository. Among these, the MLflow Tracking component provides both an API and a user interface for logging essential aspects like parameters, code versions, metrics, and output files generated during the execution of machine learning tasks, enabling later visualization of results. It allows for logging and querying experiments through several interfaces, including Python, REST, R API, and Java API. Furthermore, an MLflow Project is a structured format for organizing data science code, ensuring it can be reused and reproduced easily, with a focus on established conventions. Additionally, the Projects component comes equipped with an API and command-line tools specifically designed for executing these projects effectively. Overall, MLflow streamlines the management of machine learning workflows, making it easier for teams to collaborate and iterate on their models. -
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Tonic
Tonic
Tonic provides an automated solution for generating mock data that retains essential features of sensitive datasets, enabling developers, data scientists, and sales teams to operate efficiently while ensuring confidentiality. By simulating your production data, Tonic produces de-identified, realistic, and secure datasets suitable for testing environments. The data is crafted to reflect your actual production data, allowing you to convey the same narrative in your testing scenarios. With Tonic, you receive safe and practical data designed to emulate your real-world data at scale. This tool generates data that not only resembles your production data but also behaves like it, facilitating safe sharing among teams, organizations, and across borders. It includes features for identifying, obfuscating, and transforming personally identifiable information (PII) and protected health information (PHI). Tonic also ensures the proactive safeguarding of sensitive data through automatic scanning, real-time alerts, de-identification processes, and mathematical assurances of data privacy. Moreover, it offers advanced subsetting capabilities across various database types. In addition to this, Tonic streamlines collaboration, compliance, and data workflows, delivering a fully automated experience to enhance productivity. With such robust features, Tonic stands out as a comprehensive solution for data security and usability, making it indispensable for organizations dealing with sensitive information. -
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NVIDIA RAPIDS
NVIDIA
The RAPIDS software library suite, designed on CUDA-X AI, empowers users to run comprehensive data science and analytics workflows entirely on GPUs. It utilizes NVIDIA® CUDA® primitives for optimizing low-level computations while providing user-friendly Python interfaces that leverage GPU parallelism and high-speed memory access. Additionally, RAPIDS emphasizes essential data preparation processes tailored for analytics and data science, featuring a familiar DataFrame API that seamlessly integrates with various machine learning algorithms to enhance pipeline efficiency without incurring the usual serialization overhead. Moreover, it supports multi-node and multi-GPU setups, enabling significantly faster processing and training on considerably larger datasets. By incorporating RAPIDS, you can enhance your Python data science workflows with minimal code modifications and without the need to learn any new tools. This approach not only streamlines the model iteration process but also facilitates more frequent deployments, ultimately leading to improved machine learning model accuracy. As a result, RAPIDS significantly transforms the landscape of data science, making it more efficient and accessible. -
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Secuvy AI
Secuvy
Secuvy, a next-generation cloud platform, automates data security, privacy compliance, and governance via AI-driven workflows. Unstructured data is treated with the best data intelligence. Secuvy, a next-generation cloud platform that automates data security, privacy compliance, and governance via AI-driven workflows is called Secuvy. Unstructured data is treated with the best data intelligence. Automated data discovery, customizable subjects access requests, user validations and data maps & workflows to comply with privacy regulations such as the ccpa or gdpr. Data intelligence is used to locate sensitive and private information in multiple data stores, both in motion and at rest. Our mission is to assist organizations in protecting their brand, automating processes, and improving customer trust in a world that is rapidly changing. We want to reduce human effort, costs and errors in handling sensitive data. -
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OPAQUE
OPAQUE Systems
OPAQUE Systems delivers a cutting-edge confidential AI platform designed to unlock the full potential of AI on sensitive enterprise data while maintaining strict security and compliance. By combining confidential computing with hardware root of trust and cryptographic attestation, OPAQUE ensures AI workflows on encrypted data are secure, auditable, and policy-compliant. The platform supports popular AI frameworks such as Python and Spark, enabling seamless integration into existing environments with no disruption or retraining required. Its turnkey retrieval-augmented generation (RAG) workflows allow teams to accelerate time-to-value by 4-5x and reduce costs by over 60%. OPAQUE’s confidential agents enable secure, scalable AI and machine learning on encrypted datasets, allowing businesses to leverage data that was previously off-limits due to privacy restrictions. Extensive audit logs and attestation provide verifiable trust and governance throughout AI lifecycle management. Leading financial firms like Ant Financial have enhanced their models using OPAQUE’s confidential computing capabilities. This platform transforms AI adoption by balancing innovation with rigorous data protection. -
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DataSentics
DataSentics
Our mission is to ensure that data science and machine learning truly transform organizations. As an AI product studio, we consist of a talented team of 100 seasoned data scientists and engineers, who bring a wealth of experience from both dynamic digital startups and large multinational firms. Our focus extends beyond creating appealing presentations and dashboards; we prioritize delivering automated data solutions that are seamlessly integrated into real-world processes. We emphasize the value of our skilled data scientists and engineers, rather than merely counting clicks. Our commitment lies in the effective deployment of data science solutions in the cloud, adhering to rigorous standards of continuous integration and automation. We strive to cultivate the brightest and most innovative data professionals by providing an inspiring and rewarding work environment in Central Europe. By empowering our team to leverage our collective expertise, we continuously seek and refine the most promising data-driven opportunities for both our clients and our own innovative products, ensuring we remain at the forefront of the industry. This approach not only enhances our clients’ capabilities but also fosters a culture of creativity and collaboration within our studio. -
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Wallaroo.AI
Wallaroo.AI
Wallaroo streamlines the final phase of your machine learning process, ensuring that ML is integrated into your production systems efficiently and rapidly to enhance financial performance. Built specifically for simplicity in deploying and managing machine learning applications, Wallaroo stands out from alternatives like Apache Spark and bulky containers. Users can achieve machine learning operations at costs reduced by up to 80% and can effortlessly scale to accommodate larger datasets, additional models, and more intricate algorithms. The platform is crafted to allow data scientists to swiftly implement their machine learning models with live data, whether in testing, staging, or production environments. Wallaroo is compatible with a wide array of machine learning training frameworks, providing flexibility in development. By utilizing Wallaroo, you can concentrate on refining and evolving your models while the platform efficiently handles deployment and inference, ensuring rapid performance and scalability. This way, your team can innovate without the burden of complex infrastructure management. -
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Feast
Tecton
Enable your offline data to support real-time predictions seamlessly without the need for custom pipelines. Maintain data consistency between offline training and online inference to avoid discrepancies in results. Streamline data engineering processes within a unified framework for better efficiency. Teams can leverage Feast as the cornerstone of their internal machine learning platforms. Feast eliminates the necessity for dedicated infrastructure management, instead opting to utilize existing resources while provisioning new ones when necessary. If you prefer not to use a managed solution, you are prepared to handle your own Feast implementation and maintenance. Your engineering team is equipped to support both the deployment and management of Feast effectively. You aim to create pipelines that convert raw data into features within a different system and seek to integrate with that system. With specific needs in mind, you want to expand functionalities based on an open-source foundation. Additionally, this approach not only enhances your data processing capabilities but also allows for greater flexibility and customization tailored to your unique business requirements. -
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Amazon SageMaker Feature Store serves as a comprehensive, fully managed repository specifically designed for the storage, sharing, and management of features utilized in machine learning (ML) models. Features represent the data inputs that are essential during both the training phase and inference process of ML models. For instance, in a music recommendation application, relevant features might encompass song ratings, listening times, and audience demographics. The importance of feature quality cannot be overstated, as it plays a vital role in achieving a model with high accuracy, and various teams often rely on these features repeatedly. Moreover, synchronizing features between offline batch training and real-time inference poses significant challenges. SageMaker Feature Store effectively addresses this issue by offering a secure and cohesive environment that supports feature utilization throughout the entire ML lifecycle. This platform enables users to store, share, and manage features for both training and inference, thereby facilitating their reuse across different ML applications. Additionally, it allows for the ingestion of features from a multitude of data sources, including both streaming and batch inputs such as application logs, service logs, clickstream data, and sensor readings, ensuring versatility and efficiency in feature management. Ultimately, SageMaker Feature Store enhances collaboration and improves model performance across various machine learning projects.
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Amazon SageMaker Data Wrangler significantly shortens the data aggregation and preparation timeline for machine learning tasks from several weeks to just minutes. This tool streamlines data preparation and feature engineering, allowing you to execute every phase of the data preparation process—such as data selection, cleansing, exploration, visualization, and large-scale processing—through a unified visual interface. You can effortlessly select data from diverse sources using SQL, enabling rapid imports. Following this, the Data Quality and Insights report serves to automatically assess data integrity and identify issues like duplicate entries and target leakage. With over 300 pre-built data transformations available, SageMaker Data Wrangler allows for quick data modification without the need for coding. After finalizing your data preparation, you can scale the workflow to encompass your complete datasets, facilitating model training, tuning, and deployment in a seamless manner. This comprehensive approach not only enhances efficiency but also empowers users to focus on deriving insights from their data rather than getting bogged down in the preparation phase.
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Sana
Sana Labs
Experience a centralized hub for all your educational and informational needs. Sana is an innovative learning platform powered by AI that equips teams with the ability to discover, disseminate, and leverage the knowledge necessary for fulfilling their objectives. Enhance the learning journey for everyone by merging live collaborative interactions with tailored self-paced courses, all available in a single location. Simplify the sharing of knowledge through the capabilities of Sana Assistant, which can create questions, explanations, images, and even entire courses autonomously. Encourage active participation and excitement through a variety of interactive elements such as quizzes, Q&A sessions, polls, sticky notes, reflection cards, recordings, and much more. Seamlessly integrate Sana with your team's favorite applications, ensuring that your organization's collective knowledge remains accessible and searchable in less than 100 milliseconds. From Github to Google Workspace, Notion, Slack, and Salesforce, whatever you need, Sana is ready to provide insights from it. All of this comes together to foster a vibrant learning culture within your organization. -
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Robust Intelligence
Robust Intelligence
The Robust Intelligence Platform is designed to integrate effortlessly into your machine learning lifecycle, thereby mitigating the risk of model failures. It identifies vulnerabilities within your model, blocks erroneous data from infiltrating your AI system, and uncovers statistical issues such as data drift. Central to our testing methodology is a singular test that assesses the resilience of your model against specific types of production failures. Stress Testing performs hundreds of these evaluations to gauge the readiness of the model for production deployment. The insights gained from these tests enable the automatic configuration of a tailored AI Firewall, which safeguards the model from particular failure risks that it may face. Additionally, Continuous Testing operates during production to execute these tests, offering automated root cause analysis that is driven by the underlying factors of any test failure. By utilizing all three components of the Robust Intelligence Platform in tandem, you can maintain the integrity of your machine learning processes, ensuring optimal performance and reliability. This holistic approach not only enhances model robustness but also fosters a proactive stance in managing potential issues before they escalate. -
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TextQL
TextQL
The platform organizes BI tools and semantic layers, documents data utilizing dbt, and incorporates OpenAI and language models to facilitate self-service advanced analytics. Through TextQL, users without a technical background can effortlessly interact with data by posing queries within their familiar work environments (such as Slack, Teams, or email) and receive prompt and secure automated responses. Additionally, the platform employs NLP and semantic layers, including the dbt Labs semantic layer, to deliver sensible solutions. TextQL enhances the question-to-answer workflow by seamlessly transitioning to human analysts when necessary, significantly streamlining the entire process with AI assistance. At TextQL, we are dedicated to enabling business teams to find the data they need in under a minute. To achieve this goal, we assist data teams in uncovering and creating documentation for their datasets, ensuring that business teams can rely on the accuracy and timeliness of their reports. Ultimately, our commitment to user-friendly data access transforms the way organizations utilize their information resources. -
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Qualytics
Qualytics
Assisting businesses in actively overseeing their comprehensive data quality lifecycle is achieved through the implementation of contextual data quality assessments, anomaly detection, and corrective measures. By revealing anomalies and relevant metadata, teams are empowered to implement necessary corrective actions effectively. Automated remediation workflows can be initiated to swiftly and efficiently address any errors that arise. This proactive approach helps ensure superior data quality, safeguarding against inaccuracies that could undermine business decision-making. Additionally, the SLA chart offers a detailed overview of service level agreements, showcasing the total number of monitoring activities conducted and any violations encountered. Such insights can significantly aid in pinpointing specific areas of your data that may necessitate further scrutiny or enhancement. Ultimately, maintaining robust data quality is essential for driving informed business strategies and fostering growth. -
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LlamaIndex
LlamaIndex
LlamaIndex serves as a versatile "data framework" designed to assist in the development of applications powered by large language models (LLMs). It enables the integration of semi-structured data from various APIs, including Slack, Salesforce, and Notion. This straightforward yet adaptable framework facilitates the connection of custom data sources to LLMs, enhancing the capabilities of your applications with essential data tools. By linking your existing data formats—such as APIs, PDFs, documents, and SQL databases—you can effectively utilize them within your LLM applications. Furthermore, you can store and index your data for various applications, ensuring seamless integration with downstream vector storage and database services. LlamaIndex also offers a query interface that allows users to input any prompt related to their data, yielding responses that are enriched with knowledge. It allows for the connection of unstructured data sources, including documents, raw text files, PDFs, videos, and images, while also making it simple to incorporate structured data from sources like Excel or SQL. Additionally, LlamaIndex provides methods for organizing your data through indices and graphs, making it more accessible for use with LLMs, thereby enhancing the overall user experience and expanding the potential applications. -
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Wherobots
Wherobots
Wherobots provides a seamless way for users to create, test, and implement geospatial data analytics and AI pipelines directly within their current data ecosystem, with the option for cloud deployment. This solution alleviates concerns regarding resource management, scalability of workloads, and the complexities of geospatial processing and optimization. By linking your Wherobots account to the cloud database housing your data via our user-friendly SaaS web interface, you can efficiently build your geospatial data science, machine learning, or analytics applications using the Sedona Developer Tool. You can also automate the deployment of your geospatial pipeline to the cloud data platform while monitoring its performance through Wherobots. The results of your geospatial analytics tasks can be accessed in various ways, such as through a single geospatial map visualization or via API calls, ensuring flexibility in how insights are utilized. This comprehensive approach makes geospatial analytics more accessible and manageable for users at all levels of expertise. -
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Acryl Data
Acryl Data
Bid farewell to abandoned data catalogs. Acryl Cloud accelerates time-to-value by implementing Shift Left methodologies for data producers and providing an easy-to-navigate interface for data consumers. It enables the continuous monitoring of data quality incidents in real-time, automating anomaly detection to avert disruptions and facilitating swift resolutions when issues arise. With support for both push-based and pull-based metadata ingestion, Acryl Cloud simplifies maintenance, ensuring that information remains reliable, current, and authoritative. Data should be actionable and operational. Move past mere visibility and leverage automated Metadata Tests to consistently reveal data insights and identify new opportunities for enhancement. Additionally, enhance clarity and speed up resolutions with defined asset ownership, automatic detection, streamlined notifications, and temporal lineage for tracing the origins of issues while fostering a culture of proactive data management. -
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Modelbit
Modelbit
Maintain your usual routine while working within Jupyter Notebooks or any Python setting. Just invoke modelbi.deploy to launch your model, allowing Modelbit to manage it — along with all associated dependencies — in a production environment. Machine learning models deployed via Modelbit can be accessed directly from your data warehouse with the same simplicity as invoking a SQL function. Additionally, they can be accessed as a REST endpoint directly from your application. Modelbit is integrated with your git repository, whether it's GitHub, GitLab, or a custom solution. It supports code review processes, CI/CD pipelines, pull requests, and merge requests, enabling you to incorporate your entire git workflow into your Python machine learning models. This platform offers seamless integration with tools like Hex, DeepNote, Noteable, and others, allowing you to transition your model directly from your preferred cloud notebook into a production setting. If you find managing VPC configurations and IAM roles cumbersome, you can effortlessly redeploy your SageMaker models to Modelbit. Experience immediate advantages from Modelbit's platform utilizing the models you have already developed, and streamline your machine learning deployment process like never before. -
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Virtualitics
Virtualitics
With the integration of embedded AI and immersive 3D visualizations, analysts are equipped to formulate groundbreaking business strategies and ensure that no vital insights from their data are overlooked. Virtualitics’ Intelligent Exploration enhances this process by offering AI-assisted exploration that proactively uncovers insights essential for driving impactful decisions. The AI-guided exploration simplifies complex data interpretations into straightforward language, ensuring that every detail is captured. Analysts can delve into a wide array of data types and complexities, swiftly uncovering significant relationships within seconds. Engaging and informative 3D visualizations enhance understanding by vividly portraying data narratives. By utilizing 3D and VR data visualizations, analysts can approach data from fresh perspectives, facilitating the comprehension of intricate findings. Moreover, the ability to share well-annotated insights and clear explanations ensures that all stakeholders are well-informed and aligned with strategic objectives. This holistic approach not only enriches the analysis process but also fosters collaboration among teams.