Alternatives to Apache Ossie
Compare Apache Ossie alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Apache Ossie in 2026. Compare features, ratings, user reviews, pricing, and more from Apache Ossie competitors and alternatives in order to make an informed decision for your business.
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Denodo
Denodo Technologies
Denodo offers an AI Data Layer that helps organizations deliver live, unified, and governed data for trustworthy AI, analytics, and self-service initiatives. The platform uses logical data management to connect distributed data across hybrid, multi-cloud, on-premises, SaaS, and third-party environments without requiring data movement or duplication. Denodo helps businesses integrate data silos, enable self-service access, enforce governance, deliver real-time insights, and enrich data with business context. It is designed to support agentic AI by giving AI agents accurate, up-to-date, and governed enterprise data for better decisions and actions. The platform includes capabilities such as zero-copy data access, unified semantics, centralized compliance, natural language search, data marketplaces, and optimized query performance. -
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AnalyticsCreator
AnalyticsCreator
AnalyticsCreator is a metadata-driven design application for data warehouse automation and data product engineering across the Microsoft data stack. At its core is the AnalyticsCreator Governed Control Model, which keeps business meaning, data structures, transformation rules, dependencies and technical implementation connected in one controlled project model. Data teams design the required architecture in AnalyticsCreator, then generate native Microsoft assets from that design. Generated assets can include SQL Server objects, SSIS packages, Azure Data Factory pipelines, Microsoft Fabric components, deployment artefacts and Power BI semantic models. AnalyticsCreator supports dimensional, 3NF and hybrid modelling approaches together with ingestion, transformations, delta loading, historisation, Slowly Changing Dimensions, snapshots and repeatable data-processing patterns. Changes made in the model can be propagated across dependent project assets, while lineage, documentation and impact analysis remain connected to the underlying design. This helps teams reduce repetitive engineering work, standardise delivery and understand the effect of change before regenerating affected assets. AnalyticsCreator generates native Microsoft technology rather than requiring a proprietary runtime. Organisations retain ownership of the resulting implementation and can integrate generated assets into existing Git, Azure DevOps and CI/CD processes. Design Intelligence extends this governed project context into AI-assisted data engineering by giving authorised AI tools and agents structured access to metadata, lineage, dependencies and design rules. Typical use cases include enterprise data warehouse development, Microsoft Fabric adoption, SQL Server and SSIS modernisation, governed Power BI delivery, SAP-to-Microsoft analytics architectures and repeatable data product engineering. -
3
dbt
dbt Labs
dbt helps data teams transform raw data into trusted, analysis-ready datasets faster. With dbt, data analysts and data engineers can collaborate on version-controlled SQL models, enforce testing and documentation standards, lean on detailed metadata to troubleshoot and optimize pipelines, and deploy transformations reliably at scale. Built on modern software engineering best practices, dbt brings transparency and governance to every step of the data transformation workflow. Thousands of companies, from startups to Fortune 500 enterprises, rely on dbt to improve data quality and trust as well as drive efficiencies and reduce costs as they deliver AI-ready data across their organization. Whether you’re scaling data operations or just getting started, dbt empowers your team to move from raw data to actionable analytics with confidence.Starting Price: $100 per user/ month -
4
Dremio
Dremio
Dremio delivers lightning-fast queries and a self-service semantic layer directly on your data lake storage. No moving data to proprietary data warehouses, no cubes, no aggregation tables or extracts. Just flexibility and control for data architects, and self-service for data consumers. Dremio technologies like Data Reflections, Columnar Cloud Cache (C3) and Predictive Pipelining work alongside Apache Arrow to make queries on your data lake storage very, very fast. An abstraction layer enables IT to apply security and business meaning, while enabling analysts and data scientists to explore data and derive new virtual datasets. Dremio’s semantic layer is an integrated, searchable catalog that indexes all of your metadata, so business users can easily make sense of your data. Virtual datasets and spaces make up the semantic layer, and are all indexed and searchable. -
5
Dosi
Datus
Dosi is a downloadable AI data analyst engine for data and software teams building AI agents and applications that answer questions about organizational data. It provides a semantic layer for defining business entities, metrics, relationships, and dimensions, then uses those definitions to translate natural-language questions into executable SQL. Teams can expose Dosi to compatible agents through the Model Context Protocol (MCP), or use its command-line interface and local server in developer workflows. Dosi connects to supported analytical databases and data warehouses, allowing queries to run against configured data sources while keeping business definitions consistent. Its intended uses include governed conversational analytics, agent-powered data exploration, and adding structured business context to AI-driven data applications.Starting Price: $0 -
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Cube
Cube Dev
Cube is an AI-powered business intelligence and semantic layer platform that helps organizations access, analyze, and govern business data across applications and AI agents. The software connects to data warehouses, databases, and other data sources to create a centralized semantic model containing standardized metrics, dimensions, relationships, and business definitions. Its AI-powered analytics capabilities allow users to ask questions in natural language, generate reports, build dashboards, and explore data without writing complex SQL queries. Cube provides Model Context Protocol (MCP) integrations that enable AI assistants such as ChatGPT, Claude, and Cursor to retrieve governed business information and perform analytical tasks. Its query acceleration engine uses caching and pre-aggregations to improve analytics performance while maintaining consistent data access controls. -
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Databao
JetBrains
Databao is an AI-powered agentic analytics platform designed to help organizations connect databases, BI tools, documents, and spreadsheets into a governed semantic layer that enables reliable natural language querying and analytics. The platform allows technical and business users to ask questions in plain language and receive accurate, reproducible answers without relying on manual dashboard creation, SQL writing, or ad-hoc analytics requests. Databao includes open-source tools such as Context Engine, Data Agent, and an Analytics CLI that work together to generate semantic context from enterprise data sources, automate SQL generation, query multiple datasets, clean and visualize data, and orchestrate conversational analytics workflows. The platform supports local deployment within an organization’s environment and integrates with large language models to reduce SQL hallucinations, improve query accuracy, and streamline data workflows.Starting Price: Free -
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Strategy Mosaic
Strategy Software
Strategy Mosaic is an AI-powered universal semantic data layer and analytics foundation that sits on top of an organization’s existing data systems to unify, govern, and accelerate access to business data for analytics, AI, and reporting without costly restructuring. It creates a single source of truth with consistent business definitions, metrics, and security policies across tools and sources, harmonizing data from hundreds of systems so insights are reliable and comparable everywhere. Built with AI-assisted data modeling (Mosaic Studio), Mosaic automates data preparation, cleansing, enrichment, and modeling, reducing the time and effort needed to build robust data products and semantic models. Its universal connectors let users access governed data via SQL, REST, Python, or through popular BI and productivity tools like Power BI, Tableau, Excel, and Google Sheets, while an in-memory acceleration engine delivers fast query performance across diverse sources. -
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Codd AI
Codd AI
Codd AI solves one of the biggest problems in analytics: making data truly business-ready. Instead of teams spending weeks manually mapping schemas, building models, and defining metrics, Codd uses generative AI to automatically create a context-aware semantic layer that aligns technical data with your business language. That means business users can ask questions in plain English and get accurate, governed answers instantly—through BI tools, conversational AI, or any endpoint. With governance and auditability built in, Codd makes analytics faster, clearer, and more trustworthy. Codd AI ingests both technical metadata from your database, as well as business rules and logic to use AI to auto-generate the most comprehensive semantic layer. This semantic layer is embedded in an intelligent query agent to power natural language (NLP) conversational analytics or power traditional BI toolsStarting Price: $25k per year -
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Colrows
Colrows
Colrows is an autonomous semantic layer engineered to make enterprise AI reliable at production scale. Enterprise AI initiatives stall when models hallucinate and answers lack auditability. Colrows fixes the context, not the model. The platform continuously crawls databases, warehouses, catalogs, and documentation to build a dynamic business graph of entities, metrics, and logic. Sitting between enterprise data and AI surfaces, Colrows compiles natural language into governed, auditable SQL query plans. Every output traces directly to a verified single source of truth. Key capabilities include: AI Data Analyst: Conversational analytics with end-to-end SQL lineage. Semantic API: Standardized semantic infrastructure for internal copilots and agents. Auto-Crawl Engine: Continuous metadata synchronization without manual coding. Colrows delivers the mathematical accuracy and regulatory defensibility that probabilistic RAG cannot match. It is suited for regulated industries.Starting Price: $70/user/month -
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TextQL
TextQL
The platform indexes BI tools and semantic layers, documents data in dbt, and uses OpenAI and language models to provide self-serve power analytics. With TextQL, non-technical users can easily and quickly work with data by asking questions in their work context (Slack/Teams/email) and getting automated answers quickly and safely. The platform also leverages NLP and semantic layers, including the dbt Labs semantic layer, to ensure reasonable solutions. TextQL's elegant handoffs to human analysts, when required, dramatically simplify the whole question-to-answer process with AI. At TextQL, our mission is to empower business teams to access the data that they're looking for in less than a minute. To accomplish this, we help data teams surface and create documentation for their data so that business teams can trust that their reports are up to date. -
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GoodData.AI
GoodData.AI
GoodData.AI is an AI-ready analytics platform that helps organizations turn data into actionable insights through business intelligence, embedded analytics, and governed semantic modeling. It enables businesses to build dashboards, AI-powered applications, and intelligent workflows using a composable architecture designed for enterprise environments. The platform provides a centralized semantic layer that ensures consistent business logic across reports, analytics, AI agents, and embedded experiences. GoodData supports cloud and on-premises deployments while integrating with modern data ecosystems and enterprise applications. It also includes AI capabilities for conversational analytics, automation, decision support, and custom agent development. With enterprise-grade security, compliance certifications, and extensive developer tools, GoodData is designed to help organizations modernize their analytics infrastructure for the AI era. -
13
Arize Phoenix
Arize AI
Phoenix is an open-source observability library designed for experimentation, evaluation, and troubleshooting. It allows AI engineers and data scientists to quickly visualize their data, evaluate performance, track down issues, and export data to improve. Phoenix is built by Arize AI, the company behind the industry-leading AI observability platform, and a set of core contributors. Phoenix works with OpenTelemetry and OpenInference instrumentation. The main Phoenix package is arize-phoenix. We offer several helper packages for specific use cases. Our semantic layer is to add LLM telemetry to OpenTelemetry. Automatically instrumenting popular packages. Phoenix's open-source library supports tracing for AI applications, via manual instrumentation or through integrations with LlamaIndex, Langchain, OpenAI, and others. LLM tracing records the paths taken by requests as they propagate through multiple steps or components of an LLM application.Starting Price: Free -
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Timbr.ai
Timbr.ai
Timbr is the ontology-based semantic layer used by leading enterprises to make faster, better decisions with ontologies that transform structured data into AI-ready knowledge. By unifying enterprise data into a SQL-queryable knowledge graph, Timbr makes relationships, metrics, and context explicit, enabling both humans and AI to reason over data with accuracy and speed. Its open, modular architecture connects directly to existing data sources, virtualizing and governing them without replication. The result is a dynamic, easily accessible model that powers analytics, automation, and LLMs through SQL, APIs, SDKs, and natural language. Timbr lets organizations operationalize AI on their data - securely, transparently, and without dependence on proprietary stacks - maximizing data ROI and enabling teams to focus on solving problems instead of managing complexity.Starting Price: $599/month -
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CData Connect AI
CData
CData’s AI offering is centered on Connect AI and associated AI-driven connectivity capabilities, which provide live, governed access to enterprise data without moving it off source systems. Connect AI is built as a managed Model Context Protocol (MCP) platform that lets AI assistants, agents, copilots, and embedded AI applications directly query over 300 data sources, such as CRM, ERP, databases, APIs, with a full understanding of data semantics and relationships. It enforces source system authentication, respects existing role-based permissions, and ensures that AI actions (reads and writes) follow governance and audit rules. The system supports query pushdown, parallel paging, bulk read/write operations, streaming mode for large datasets, and cross-source reasoning via a unified semantic layer. In addition, CData’s “Talk to your Data” engine integrates with its Virtuality product to allow conversational access to BI insights and reports. -
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SAP Business Data Cloud is a fully managed SaaS solution that unifies and governs all SAP data while seamlessly connecting with third-party data, providing line-of-business leaders with the context needed to make impactful decisions. It offers mission-critical data products, granting access to SAP data across essential business processes in a deeply contextual and governed manner, thereby eliminating the high costs associated with data extraction and replication. As a leading data platform, it enables the connection of all SAP and third-party data through a fully managed SaaS solution in collaboration with Databricks. The platform delivers powerful insight applications, facilitating transformational insights for advanced analytics and planning across various lines of business. By harmonizing all mission-critical data within an open data ecosystem and leveraging a robust semantic layer, SAP Business Data Cloud provides unparalleled business understanding.
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Beye
Beye
Beye is an AI-native generative business intelligence platform that ingests and auto-cleans raw data from spreadsheets, ERPs, and cloud apps, into unified, AI-optimized dataverses in weeks rather than months. Its generative BI agent auto-builds your first data model and starter dashboards around your specific use case, applying metadata and semantic layers, measure creation, and data preparation without manual effort. Business users, managers, and executives can ask questions in plain English, no SQL or dashboard navigation required, to receive instant, high-fidelity analytics, contextualized insights, and root-cause explanations with traceable queries. It integrates seamlessly with SAP, Snowflake, Salesforce, NetSuite, and over 50 additional sources, supports collaborative channels and custom metrics, and validates answers through AI-driven workflows. -
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Kater.ai
Kater.ai
Kater is built for data professionals and data inquisitors. All organized data products are immediately usable by anyone who has a data question, without knowing a lick of SQL. Kater aims to bridge the ownership of data across all business domains in your company. Butler securely connects to your data warehouse's metadata and objects to help you code, discover data, and so much more. Optimize your data for AI with automatic intelligent labeling, categorization, and data curation. We help you define your semantic layer, metric layer, and general documentation. Validated answers are stored in the query bank for smarter, more accurate responses. -
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Graphwise
Graphwise
Graphwise is an AI platform that helps businesses automate knowledge and trust their AI by turning fragmented data into a trusted semantic backbone. The all-in-one suite makes generative AI reliable and scalable by transforming data into AI-ready, context-rich assets, deploying intelligent agent-based systems, and delivering powerful AI applications on an integrated platform. Graphwise moves beyond simple data chunks with Precise GraphRAG, using a governed knowledge graph to ground every response in verified facts, eliminate hallucinations, and provide accurate, actionable answers. It combines automated modeling, high-performance graph technology, semantic search, recommendation, taxonomy and ontology management, data automation, graph-based text mining, and enterprise-ready GraphRAG workflows. It supports use cases such as technical knowledge management, semantic digital twins, compliance intelligence, and scientific knowledge management. -
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Kyvos Semantic Layer
Kyvos Insights
Kyvos is a semantic layer for AI and BI. By standardizing how data is defined and understood, Kyvos gives organizations a single, consistent, business-friendly view of their entire data estate. The result: AI agents reason with governed context, BI tools report consistent metrics, and the business gets answers it can trust. Built for enterprise scale, Kyvos provides the speed, scale, and trust that production-grade AI and enterprise BI demand. It also maximizes analytics investments by reducing token consumption and cloud compute costs. -
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Stardog
Stardog Union
With ready access to the richest flexible semantic layer, explainable AI, and reusable data modeling, data engineers and scientists can be 95% more productive — create and expand semantic data models, understand any data interrelationship, and run federated queries to speed time to insight. Stardog offers the most advanced graph data virtualization and high-performance graph database — up to 57x better price/performance — to connect any data lakehouse, warehouse or enterprise data source without moving or copying data. Scale use cases and users at lower infrastructure cost. Stardog’s inference engine intelligently applies expert knowledge dynamically at query time to uncover hidden patterns or unexpected insights in relationships that enable better data-informed decisions and business outcomes.Starting Price: $0 -
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AtScale
AtScale
AtScale helps accelerate and simplify business intelligence resulting in faster time-to-insight, better business decisions, and more ROI on your Cloud analytics investment. Eliminate repetitive data engineering tasks like curating, maintaining and delivering data for analysis. Define business definitions in one location to ensure consistent KPI reporting across BI tools. Accelerate time to insight from data while efficiently managing cloud compute costs. Leverage existing data security policies for data analytics no matter where data resides. AtScale’s Insights workbooks and models let you perform Cloud OLAP multidimensional analysis on data sets from multiple providers – with no data prep or data engineering required. We provide built-in easy to use dimensions and measures to help you quickly derive insights that you can use for business decisions. -
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SAP Datasphere
SAP
SAP Datasphere is a unified data experience platform within SAP Business Data Cloud, designed to provide seamless, scalable access to mission-critical business data. It integrates data from SAP and non-SAP systems, harmonizing diverse data landscapes and enabling faster, more accurate decision-making. With capabilities like data federation, cataloging, semantic modeling, and real-time data integration, SAP Datasphere ensures that businesses have consistent, contextualized data across hybrid and cloud environments. The platform simplifies data management by preserving business context and logic, providing a comprehensive view of data that drives innovation and enhances business processes. -
24
Nowl
Gerling Solutions
Nowl is a semantic access layer that makes existing enterprise software and databases queryable for AI in business terms. Instead of giving an AI free SQL access, Nowl translates business questions into validated, read-only query plans and shows how every answer was derived, so results are traceable instead of a black box. A privacy layer filters data before anything is transmitted to a language model. Nowl is self-hosted (except the language model), EU data residency can be selected, and it changes nothing in the existing systems. It connects to assistants such as Claude via MCP. Typical uses: ad-hoc questions from sales, finance, service and management that standard reports do not cover. Built for mid-sized companies with grown software landscapes. Currently in a pilot program. -
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Textkernel Source & Match
Textkernel
Source & Match automatically matches the best candidates and jobs across internal and external databases. Through our semantic layer and sophisticated data enrichment everyone becomes an expert sourcing specialist. We find what you mean, not just what you type, making it easy to discover the best talent or jobs. Plus, our powerful matching engine, integrated into your ATS, CRM, Internal Mobility or Talent Management platform, automatically transforms a job posting or candidate into a comprehensive search query providing results with a shortlist of promising candidates or jobs. Our matching accuracy is the game-changer as it provides results you can rely on! - Unlock the full value of your talent and jobs database - Automate with confidence - Eliminate bias Source & Match products redefine the hiring process by enhancing efficiency, elevating the candidate experience, and empowering organizations to turn their talent data into a valuable asset. -
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Brewit
Brewit
Make data-driven decisions 10x faster with self-service analytics. Integrate with your databases and data warehouses all-in-one place (Postgres, MySQL, Snowflake, BigQuery, and more). Brewit can write SQL queries and create recommended charts based on your data questions. It also helps you drill down on the analysis. Chat with your database, visualize insights, & perform analysis. Ensure answer accuracy and consistency with a built-in data catalog. An automated semantic layer that ensures Brewit answers with correct business logic. Easily manage your data catalog & data dictionary. Building a beautiful report is as easy as writing a doc. Data without a story is useless. Our Notion-style notebook editor allows you to create reports & dashboards easily, turning raw data into actionable insights. All organized data products are usable by anyone who has a data question, regardless of their technical skills. -
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BinarBase
BinarBase
Easily connect financial data, customer data and third-party apps to our unified platform. No complex setup required. Effortless tracking and analyzing all your business data, driving informed decision-making and growth. Our semantic layer infuses meaning into your data, making it readily understandable for business users. We provide you with a comprehensive view of your business performance, enabling you to see areas for improvement and optimize operations. Identify patterns in expenses, revenue streams and cash flow trends to gain a deeper understanding of your financial health. Understand customer behavior, including purchase patterns and payment cycles. Track key metrics to assess your startup's progress and make data-driven adjustments to your strategy. -
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MetaCenter
Data Advantage Group
MetaCenter enables business and technology teams to catalog and classify an organization's information assets. Users can self-service questions about their data assets and how data flows through the business and classify how it should be used. This enables organizations to lower costs while improving agility and reducing operational risks. Search-based semantic layer automates cross-referencing models. Faceted Views of specific data assets can be published to individual roles. Lower cost of ownership and higher levels of automation deliver superior ROI compared to competing solutions. Simple GUI driven customization enables rapid application customization. No programming or professional services are required. -
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Boost.space
Boost.space
Boost.space is a no-code agentic database designed to give AI agents and automations the structured business context they need to operate effectively. It centralizes scattered data from CRM, ecommerce, billing, and support systems into a unified Single Source of Truth. The platform enables continuous two-way synchronization across tools, ensuring that information remains accurate and up to date. With built-in AI enrichment, users can classify records, normalize attributes, and generate structured metadata at scale. Boost.space also supports workflow automation through integrations with platforms like Make, Zapier, and n8n. Through its Model Context Protocol (MCP), AI agents can query live data and execute actions across connected systems without relying on static exports. Trusted by thousands of teams globally, Boost.space transforms fragmented datasets into actionable AI-ready infrastructure.Starting Price: $15/month -
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BeagleGPT
BeagleGPT
Proactive data and insights nudges for each user according to their usage pattern, automated heuristic rules, data updates, and user-cohort learnings. The semantic layer is finetuned for organizations with their nomenclatures and terminologies. User roles and preferences are considered while building responses for them. Advanced modules to answer how, why and so what scenarios. A single subscription covers the entire organization, truly propelling data democratization. Beagle is built to nudge you and your team toward data-driven decision-making. It is your personal data assistant that delivers all data-related updates and alerts in your message box. With in-built self-service functionalities, Beagle reduces the total cost of ownership by huge margins. Beagle connects with other dashboards to enhance their power and increase their reach in the organization. -
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SSAS
Microsoft
Installed as an on-premises server instance, SQL Server Analysis Services supports tabular models at all compatibility levels (depending on version), multidimensional models, data mining, and Power Pivot for SharePoint. A typical implementation workflow includes installing a SQL Server Analysis Services instance, creating a tabular or multidimensional data model, deploying the model as a database to a server instance, processing the database to load it with data, and then assigning permissions to allow data access. When ready to go, the data model can be accessed by any client application supporting Analysis Services as a data source. Models are populated with data from external data systems, usually data warehouses hosted on a SQL Server or Oracle relational database engine (Tabular models support additional data source types). -
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Microsoft Fabric
Microsoft
Reshape how everyone accesses, manages, and acts on data and insights by connecting every data source and analytics service together—on a single, AI-powered platform. All your data. All your teams. All in one place. Establish an open and lake-centric hub that helps data engineers connect and curate data from different sources—eliminating sprawl and creating custom views for everyone. Accelerate analysis by developing AI models on a single foundation without data movement—reducing the time data scientists need to deliver value. Innovate faster by helping every person in your organization act on insights from within Microsoft 365 apps, such as Microsoft Excel and Microsoft Teams. Responsibly connect people and data using an open and scalable solution that gives data stewards additional control with built-in security, governance, and compliance.Starting Price: $156.334/month/2CU -
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DataGalaxy
DataGalaxy
DataGalaxy is a next-generation data governance and intelligence platform designed to help organizations manage, understand, and maximize the value of their data. Built around a unified interface, it empowers everyone—from executives to data consumers—to collaborate seamlessly across data assets, strategies, and analytics. The platform’s automated data catalog, governance hub, and AI co-pilot reduce manual work while ensuring compliance and data quality across systems. With over 70+ integrations, including Snowflake, Databricks, Power BI, and AWS, DataGalaxy connects your data ecosystem into a single source of truth. Its value tracking center and strategy cockpit align data initiatives with business goals, driving measurable outcomes and enterprise-wide visibility. Loved by users, DataGalaxy turns governance into a strategic advantage for the modern enterprise. -
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Apache Tomcat
Apache
The Apache Tomcat® software is an open source implementation of the Jakarta Servlet, Jakarta Server Pages, Jakarta Expression Language, Jakarta WebSocket, Jakarta Annotations and Jakarta Authentication specifications. These specifications are part of the Jakarta EE platform. Apache Tomcat software powers numerous large-scale, mission-critical web applications across a diverse range of industries and organizations. Some of these users and their stories are listed on the PoweredBy wiki page. The Apache Tomcat Project is proud to announce the release of version 10.0.10 of Apache Tomcat. This release implements specifications that are part of the Jakarta EE 9 platform. -
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Apache Geronimo
Apache
Apache Geronimo is an open-source set of projects that are focused on providing JavaEE/JakartaEE libraries and Microprofile implementations. We are actively delivering reusable Java EE components though. They are widely used and still actively maintained! Apache Geronimo provides libraries for the implementations of the Java EE and Jakarta EE specifications. The implementations are also focused on providing OSGi bundle metadata. The goal of XBean project is to create a plugin-based server analogous to Eclipse is a plugin-based IDE. XBean will be able to discover, download and install server plugins from an Internet-based repository. In addition, we include support for multiple IoC systems, support for running with no IoC system, JMX without JMX code, lifecycle and class loader management, and rock-solid Spring integration. Apache Geronimo hosts several Microprofile implementations. Apache Geronimo Arthur is an effort to build a thin layer on top of Oracle GraalVM.Starting Price: Free -
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PartiQL
PartiQL
PartiQL's extensions to SQL are easy to understand, treat nested data as first class citizens and compose seamlessly with each other and SQL. This enables intuitive filtering, joining and aggregation on the combination of structured, semistructured and nested datasets. PartiQL enables unified query access across multiple data stores and data formats by separating the syntax and semantics of a query from the underlying format of the data or the data store that is being accessed. It enables users to interact with data with or without regular schema. PartiQL syntax, semantics, the embeddable reference interpreter, CLI, test framework, and tests are licensed under the Apache License, version 2.0, allowing you to freely use, copy, and distribute your changes under the terms of your choice. -
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Open edX
Axim Collaborative
Open edX is an open-source LMS and course authoring platform created by Harvard and MIT in 2012, now stewarded by the nonprofit Axim Collaborative. It serves 140M+ learners across 196 countries, deployed by universities, enterprises, governments, and NGOs. Built on Python/Django with Docker/Tutor deployment and Kubernetes support, it includes Studio for course authoring, XBlocks for custom content, LTI 1.3 Advantage Complete certification, SCORM, xAPI, native mobile apps, built-in analytics, eCommerce, SSO via SAML/OAuth/OIDC, and REST APIs. Released under AGPLv3 and Apache 2.0 with no licensing fees. The platform follows a biannual named release cycle - the latest stable release is Verawood (June 2026), the 22nd community release. Verified service providers across Europe, Asia, Africa, the Americas, and beyond offer hosting, implementation, and support via the Open edX Marketplace.Starting Price: Free (open source, self-hosted -
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Apache PredictionIO
Apache
Apache PredictionIO® is an open-source machine learning server built on top of a state-of-the-art open-source stack for developers and data scientists to create predictive engines for any machine learning task. It lets you quickly build and deploy an engine as a web service on production with customizable templates. Respond to dynamic queries in real-time once deployed as a web service, evaluate and tune multiple engine variants systematically, and unify data from multiple platforms in batch or in real-time for comprehensive predictive analytics. Speed up machine learning modeling with systematic processes and pre-built evaluation measures, support machine learning and data processing libraries such as Spark MLLib and OpenNLP. Implement your own machine learning models and seamlessly incorporate them into your engine. Simplify data infrastructure management. Apache PredictionIO® can be installed as a full machine learning stack, bundled with Apache Spark, MLlib, HBase, Akka HTTP, etc.Starting Price: Free -
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Apache Giraph
Apache Software Foundation
Apache Giraph is an iterative graph processing system built for high scalability. For example, it is currently used at Facebook to analyze the social graph formed by users and their connections. Giraph originated as the open-source counterpart to Pregel, the graph processing architecture developed at Google and described in a 2010 paper. Both systems are inspired by the Bulk Synchronous Parallel model of distributed computation introduced by Leslie Valiant. Giraph adds several features beyond the basic Pregel model, including master computation, sharded aggregators, edge-oriented input, out-of-core computation, and more. With a steady development cycle and a growing community of users worldwide, Giraph is a natural choice for unleashing the potential of structured datasets at a massive scale. Apache Giraph is an iterative graph processing framework, built on top of Apache Hadoop. -
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Apache Lucene
Apache Software Foundation
The Apache Lucene™ project develops open-source search software. The project releases a core search library, named Lucene™ core, as well as PyLucene, a python binding for Lucene. Lucene Core is a Java library providing powerful indexing and search features, as well as spellchecking, hit highlighting and advanced analysis/tokenization capabilities. The PyLucene sub project provides Python bindings for Lucene Core. The Apache Software Foundation provides support for the Apache community of open-source software projects. Apache Lucene is distributed under a commercially friendly Apache Software license. Apache Lucene set the standard for search and indexing performance. Lucene is the search core of both Apache Solr™ and Elasticsearch™. Our core algorithms along with the Solr search server power applications the world over, ranging from mobile devices to sites like Twitter, Apple and Wikipedia. The goal of Apache Lucene is to provide world class search capabilities. -
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Amazon EMR
Amazon
Amazon EMR is the industry-leading cloud big data platform for processing vast amounts of data using open-source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi, and Presto. With EMR you can run Petabyte-scale analysis at less than half of the cost of traditional on-premises solutions and over 3x faster than standard Apache Spark. For short-running jobs, you can spin up and spin down clusters and pay per second for the instances used. For long-running workloads, you can create highly available clusters that automatically scale to meet demand. If you have existing on-premises deployments of open-source tools such as Apache Spark and Apache Hive, you can also run EMR clusters on AWS Outposts. Analyze data using open-source ML frameworks such as Apache Spark MLlib, TensorFlow, and Apache MXNet. Connect to Amazon SageMaker Studio for large-scale model training, analysis, and reporting. -
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Apache Mahout
Apache Software Foundation
Apache Mahout is a powerful, scalable, and versatile machine learning library designed for distributed data processing. It offers a comprehensive set of algorithms for various tasks, including classification, clustering, recommendation, and pattern mining. Built on top of the Apache Hadoop ecosystem, Mahout leverages MapReduce and Spark to enable data processing on large-scale datasets. Apache Mahout(TM) is a distributed linear algebra framework and mathematically expressive Scala DSL designed to let mathematicians, statisticians, and data scientists quickly implement their own algorithms. Apache Spark is the recommended out-of-the-box distributed back-end or can be extended to other distributed backends. Matrix computations are a fundamental part of many scientific and engineering applications, including machine learning, computer vision, and data analysis. Apache Mahout is designed to handle large-scale data processing by leveraging the power of Hadoop and Spark. -
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Apache Hive
Apache Software Foundation
The Apache Hive data warehouse software facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage. A command line tool and JDBC driver are provided to connect users to Hive. Apache Hive is an open source project run by volunteers at the Apache Software Foundation. Previously it was a subproject of Apache® Hadoop®, but has now graduated to become a top-level project of its own. We encourage you to learn about the project and contribute your expertise. Traditional SQL queries must be implemented in the MapReduce Java API to execute SQL applications and queries over distributed data. Hive provides the necessary SQL abstraction to integrate SQL-like queries (HiveQL) into the underlying Java without the need to implement queries in the low-level Java API. -
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Gensim
Radim Řehůřek
Gensim is a free, open source Python library designed for unsupervised topic modeling and natural language processing, focusing on large-scale semantic modeling. It enables the training of models like Word2Vec, FastText, Latent Semantic Analysis (LSA), and Latent Dirichlet Allocation (LDA), facilitating the representation of documents as semantic vectors and the discovery of semantically related documents. Gensim is optimized for performance with highly efficient implementations in Python and Cython, allowing it to process arbitrarily large corpora using data streaming and incremental algorithms without loading the entire dataset into RAM. It is platform-independent, running on Linux, Windows, and macOS, and is licensed under the GNU LGPL, promoting both personal and commercial use. The library is widely adopted, with thousands of companies utilizing it daily, over 2,600 academic citations, and more than 1 million downloads per week.Starting Price: Free -
45
OLake Go
Datazip
OLake Go is an open-source data replication platform for moving operational data into data lakehouse storage. It connects to databases such as PostgreSQL, MySQL, MongoDB, Oracle Database, SQL Server, and IBM Db2, as well as Apache Kafka and object storage. The platform supports full-load and incremental ingestion, including change data capture for supported connectors, and writes data to Apache Iceberg tables and Parquet files using supported catalogs and storage targets. Data engineers and analytics platform teams can run ingestion jobs through the OLake UI or command-line workflows, deploy the software in their own infrastructure, and prepare synchronized lakehouse datasets for downstream querying and analytics. Docker-based deployment and connector-specific configuration let teams manage source connections, destinations, and data movement in a self-hosted environment.Starting Price: $0 -
46
Amazon MSK
Amazon
Amazon Managed Streaming for Apache Kafka (Amazon MSK) is a fully managed service that makes it easy for you to build and run applications that use Apache Kafka to process streaming data. Apache Kafka is an open-source platform for building real-time streaming data pipelines and applications. With Amazon MSK, you can use native Apache Kafka APIs to populate data lakes, stream changes to and from databases, and power machine learning and analytics applications. Apache Kafka clusters are challenging to setup, scale, and manage in production. When you run Apache Kafka on your own, you need to provision servers, configure Apache Kafka manually, replace servers when they fail, orchestrate server patches and upgrades, architect the cluster for high availability, ensure data is durably stored and secured, setup monitoring and alarms, and carefully plan scaling events to support load changes.Starting Price: $0.0543 per hour -
47
Apache APISIX
Apache APISIX
Apache APISIX provides rich traffic management features like Load Balancing, Dynamic Upstream, Canary Release, Circuit Breaking, Authentication, Observability, etc. Apache APISIX provides open source API Gateway to help you manage microservices, delivering the ultimate performance, security, and scalable platform for all your APIs and microservices. Apache APISIX is the first open-source API Gateway that includes a built-in low-code Dashboard, which offers a powerful and flexible UI for developers to use. The Apache APISIX Dashboard is designed to make it as easy as possible for users to operate Apache APISIX through a frontend interface. It’s open-source and ever evolving, feel free to contribute. The Apache APISIX dashboard is flexible to User demand, providing option to create custom modules through code matching your requirements, alongside the existing no-code toolchain. -
48
Qwen3-TTS
Alibaba
Qwen3-TTS is an open source series of advanced text-to-speech models developed by the Qwen team at Alibaba Cloud under the Apache-2.0 license, offering stable, expressive, and real-time speech generation with features such as voice cloning, voice design, and fine-grained control of prosody and acoustic attributes. The models support 10 major languages, including Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, and Italian, and multiple dialectal voice profiles with adaptive control over tone, speaking rate, and emotional expression based on text semantics and instructions. Qwen3-TTS uses efficient tokenization and a dual-track architecture that enables ultra-low-latency streaming synthesis (first audio packet in ~97 ms), making it suitable for interactive and real-time use cases, and includes a range of models with different capabilities (e.g., rapid 3-second voice cloning, custom voice timbres, and instruction-based voice design).Starting Price: Free -
49
Apache Derby
Apache
Apache Derby, an Apache DB subproject, is an open source relational database implemented entirely in Java and available under the Apache License, Version 2.0. Derby has a small footprint - about 3.5 megabytes for the base engine and embedded JDBC driver. Derby provides an embedded JDBC driver that lets you embed Derby in any Java-based solution. Derby also supports the more familiar client/server mode with the Derby Network Client JDBC driver and Derby Network Server. -
50
Antaeus
Antaeus
Antaeus is a semantic policy engine for developers who need to evaluate human-written rules about language, context, intent, or exceptions. Its open-source Go core and command-line interface load YAML or JSON policy artifacts, validate them against versioned schemas, and run named regression cases. The evaluator returns explicit allow, review, deny, or failure outcomes with policy identity and decision evidence, leaving authentication, authorization, and consequential actions to the application. Developers can run a deterministic, credential-free local workflow or use the experimental hosted dashboard and API to manage policies and evaluate inputs. An experimental OpenAI evaluator is also available. Antaeus is an early-stage project; hosted service capacity is shared and has no production SLA.Starting Price: Free
