Beinex Unveils Version 2 of the Cost Optimizer App on Snowflake

What's New in Snowflake Summit 2025

The main innovations and improvements revealed at the Snowflake summit 2025 are highlighted in this blog, but there is more to come.

1. Get Started with Agentic AI with No Code

Agentic AI refers to AI systems that proactively assist users by understanding context, initiating actions, and automating tasks. Snowflake's agentic AI experiences enable both technical and non-technical users to utilise and interact with AI and data using natural language, without writing a single line of code. The key features highlighted are:

• Data Science Agent (coming soon in private preview): Your ML copilot that can automate feature engineering, training, and more using plain English.
• Cortex AISQL (public preview): Cortex AISQL enables users to utilize SQL to analyze documents, images, and other unstructured data types. Now includes schema-aware table extraction from complex PDFs.
• Cortex AI Agents (public preview): You can use natural language to ask questions, analyze structured and unstructured data, and take action, all within the Snowflake platform.

Bonus: All of this operates within Snowflake's secure perimeter, maintaining governance and privacy by default.

2. Unified Data Engineering and Interoperability

Snowflake is solving rigid data pipelines and slow ingestion issues with new tools built for flexibility and speed. Let's have a look:

• Snowflake OpenFlow (powered by Apache NiFi): A managed, extensible, multimodal low-code ingestion platform. It includes hundreds of connectors to eliminate data silos. To facilitate smooth ETL processing for AI, OpenFlow transforms data movement within Snowflake.
• Native dbt Integration (coming soon): With Git and AI Copilot built-in, you can develop and run dbt models natively within Snowsight UI.
• Iceberg Table Enhancements: You can work effortlessly with external catalogs using the Iceberg REST API. It supports VARIANT data types and Merge-on-Read for semi-structured data.
• DevOps-Ready: Snowflake Workspaces now support custom Git URLs and Python 3.9. Terraform provider is now generally available.

3. Analytics with AI-Driven Acceleration

Analytics just got smarter, faster, and easier to scale with the following updates:

• Snowflake Semantic Views (public preview): It provides excellent support for BI teams, and you can create consistent, reusable business metrics and entities within Snowflake.
• Gen2 Warehouses: Generation 2 (Gen2) is a modernized version of Snowflake's standard warehouse, featuring enhanced hardware and improved performance capabilities. It provides a 2.1x performance boost for analytical queries with enhanced scans, DELETEs, and MERGEs.
• SnowConvert AI: A free, automatic tool for auto-converting legacy ETL/BI code to Snowflake. It reduces risk and saves migration costs.

4. Smarter Marketplace to Share, Sell & Collaborate Easily

Snowflake is making it easier to monetize AI apps and datasets using:

• Cortex Knowledge Extensions: While maintaining IP protection, embed real-time info from Stack Overflow, AP, Packt, and more into your apps.
• Semantic Model Sharing (private preview): Rely on natural language to explore datasets across internal teams or external partners.
• Agentic Snowflake Native Apps: You can easily build and sell no-code, AI-powered apps directly through the Snowflake Marketplace.

5. The Future of Platform: Adaptive, Governed & Secure

Snowflake isn't just innovating; it's redefining what a modern data platform should be in the following ways:

• Adaptive Compute (Adaptive Warehouses): You can automatically adjust resources based on workload, delivering speed without overspending.
• Horizon Catalog (private preview): It enables users to easily discover and govern external data sources, such as dashboards and relational databases.
• Copilot for Governance (coming soon): Users can utilize this feature to manage metadata, access policies, and security using natural language.
• Security Upgrades: To improve account protection, enhanced MFA, password policies, and a revamped Trust Center are being implemented.
• Snowflake Trail: You can debug pipelines and optimize your generative AI agents and apps using new observability features.

Summing Up

Snowflake Summit 2025 showcased the company's push toward an Agentic AI enterprise data platform. Snowflake's advanced features, including Cortex AISQL, OpenFlow, dbt integration, and native app frameworks, are democratizing AI and making it easier for users. Whether you're a data scientist, engineer, or business leader, Snowflake's latest innovations promise faster time-to-insight, reduced complexity, and deeper control over your data universe.

We are delighted to achieve this award with our relentless dedication and unparalleled expertise on the Alteryx platform. With ample product knowledge, technical expertise, and sufficient focus on customer satisfaction, we were able to demonstrate the value that we offer as a partner. Our determination and dedication aided in supplementing our customers with the power to deliver faster and better business outcomes by intelligent decisioning using Alteryx. Last year, we were fortunate enough to successfully transform the majority of our clients’ businesses with Analytic Process Automation by quickly automating analytics and the entire data-driven business processes, resulting in quick wins and faster returns on ROI. We were also awarded with Alteryx 2020 Partner of the Year award, Middle East. With the preferred partner status, we will be able to make even greater collaboration with the Alteryx team, helping us extract its possibilities to the next level. Alteryx always stands for developing data-driven technical solutions to business problems by empowering its clients to be self-sufficient in handling data analytics and continues to provide unmatched services, like;

• • Collecting data from multiple sources for quick analysis and faster insight generation.
  • Exploration of data from on-prem databases, the cloud, and big or small data sets, and more.
  • Analysis with maps, addressing solutions to deeply understand your customers and locations.
  • Augmenting your team’s analytic output to gain insights by using data without any coding or analytics expertise.
  • Embracing automation to effectively communicate with your stakeholders and enable intelligent decision-making to drive better, faster business outcomes.

We are incredibly honored to receive this award, and we look forward to a continued partnership as a preferred partner in 2021! Thank you, Alteryx! About Beinex Consulting Beinex is a digital transformation organization with a broad range of analytics modernization and training services. As a pioneer in analytics and cloud transformation, Beinex’s mission is to transform the way individuals and the organizations work with the data through innovation and experience. Beinex offers a broad range of robust and scalable business intelligence and analytics services to drive effective decision-making and create business value.

What is Data Visualization?

Data visualization is the process of representing data in a graphical or spatial format, allowing for easy visual analysis without technical jargon. Unlike raw numerical data, visual representations like charts, graphs, and maps help quickly identify patterns, trends, and anomalies, facilitating faster and more accurate insights.

Benefits of Data Visualization

Understanding raw data can be challenging due to its complexity. Data visualization addresses this by:

1. Simplify Data Interpretation: Converting raw data into charts and graphs makes it easier to understand underlying patterns and relationships.
2. Identify Trends and Anomalies: Visual formats highlight trends and anomalies that might be missed in numerical data.
3. Improve Accessibility: Data visualization makes information accessible to a broader audience, including those without strong analytical skills, thus improving data-driven decision-making across departments.
4. Advanced Data Storytelling: Effective visualization techniques turn data into compelling stories that facilitate better communication and understanding.

What Are Data Visualization Tools?

Data visualization tools provide designers with an efficient way to create visual representations of large data sets. When dealing with data sets that include hundreds of thousands or millions of data points, automating the visualization process simplifies the designer's job considerably.

Key Benefits of Data Visualisation Tools

1. Dashboards: To monitor and analyze key performance indicators (KPIs) and metrics in real-time.
2. Annual Reports: To present data-driven insights to stakeholders in a clear and engaging manner.
3. Sales and Marketing Materials: To showcase trends, performance, and forecasts to potential clients and customers.
4. Investor Slide Decks: To communicate financial health and growth prospects effectively.
5. General Information Interpretation: To make complex data understandable for decision-making processes in virtually any context where quick interpretation of information is necessary.

A Compact List of Top Data Visualization Tools

Here are the top enterprise data visualization tools for creating compelling visualizations:

1. Tableau
2. Google Charts
3. Zoho Analytics
4. Data Wrapper

Tableau

Tableau is a top-tier platform recognized for its user-friendly interface. It adeptly integrates data from multiple sources to create dynamic and visualisations.
Its comprehensive suite of products spans desktop applications, robust server solutions, and flexible web-hosted environments, empowering organizations to drive informed decision-making and achieve actionable insights across their operations.
Connect with us for a free demo: https://www.beinex.com/free-tableau-software

Who Should Use Tableau?

Data scientists and analysts who need to create custom dashboards and advanced visualizations will benefit from Tableau.

Key Features of Tableau

• User-Friendly Interface: Easy to learn and navigate, making it accessible for all skill levels. • Mobile-Friendly: Create reports and dashboards optimised for mobile devices, allowing you to access and analyse data on the go. • High Performance: Efficiently handles large datasets, ensuring seamless analysis without performance issues. • Interactive Visualizations: Build interactive and dynamic visualisations, allowing deeper data exploration. • Integration Capabilities: Integrates well with various data sources and other business applications, enhancing data connectivity. • Real-Time Data Updates: Provides real-time data updates, ensuring you have the most current insights. • Collaboration Tools: Facilitates easy sharing and collaboration on reports and dashboards within teams. • Customizable Dashboards: Offers highly customisable dashboards to meet specific business needs and preferences. • Advanced Analytics: Supports advanced analytics features, including trend analysis, forecasting, and statistical summaries. • Security: Ensures data security with robust access controls and permissions.

Learn more: https://www.beinex.com/tableau-partnership-and-consulting-services/

Google Charts

Google Charts is a free tool for creating interactive data visualisations, accessible through most web browsers. It supports various data sources, including spreadsheets and databases.

Who Should Use Google Charts?

Students, universities, and businesses needing fundamental charts will find Google Charts useful.

Key Features of Google Charts

• User-Friendly Interface: Easy to use with a straightforward setup process. • Wide Range of Chart Types: Supports various chart types, including line, bar, pie, scatter, and more. • Customizable: Offers extensive customization options to tailor charts to specific needs, including colors, fonts, and annotations. • Interactive Charts: Allows for interactive elements such as tooltips, zooming, and panning. • Cross-Platform Compatibility: Ensures charts work seamlessly across different browsers and devices. • Dynamic Data Updates: Supports real-time data updates, keeping charts current with live data feeds. • Integration with Google Services: Easily integrates with other Google services such as Google Sheets and Google Analytics. • Embedding Capabilities: Simple embedding in websites and applications with a few lines of code. • Data Export Options: Provides options to export charts in various formats, including PNG, SVG, and PDF. • Open Source: Free and open source, allowing for extensive customization and community support. • Support for Multiple Data Formats: Works with various data formats, including JSON, CSV, and Google Spreadsheets. • Accessibility Features: These include features to make charts accessible to all users, including screen reader support. • Responsive Design: Ensures charts are responsive and adapt to different screen sizes and resolutions. • Powerful API: Provides a robust API for developers to create complex visualisations and integrate them into applications.

Zoho Analytics

Zoho Analytics combines business intelligence and reporting services, allowing for swift data visualisation. It is user-friendly and integrates well with other Zoho products.

Who Should Use Zoho Analytics?

Analytics and sales teams, marketing teams, project managers, and more can benefit from Zoho Analytics.

Key Features of Zoho Analytics

• User-Friendly Interface: Intuitive design that simplifies data analysis and visualisation. • Wide Range of Data Sources: Connects to various data sources, including databases, cloud storage, spreadsheets, and other business applications. • Advanced Analytics: Offers features such as predictive analytics, AI-powered insights, and what-if analysis. • Interactive Dashboards: Create and customise interactive dashboards with drag-and-drop ease. • Collaboration Tools: Facilitate sharing and collaboration on reports and dashboards within teams. • Embedded Analytics: Embed reports and dashboards into websites, applications, and portals. • Automated Data Sync: Schedule data imports and synchronise data regularly. • Customizable Visualizations: Provides a variety of chart types and extensive customisation options for data visualisations. • Data Blending: Combine data from multiple sources for comprehensive analysis. • AI-Driven Insights: Leverages AI to offer advanced analytical insights and pattern detection. • Real-Time Data Access: Supports real-time data integration and live dashboards. • Mobile Access: Access and interact with reports and dashboards on mobile devices. • Data Security: Ensures robust security features, including role-based access control, encryption, and compliance with industry standards. • Report Scheduling: Automate the distribution of reports through scheduled emails. • Integrations with Other Zoho Apps: Seamlessly integrate with other Zoho applications for enhanced functionality. • API Support: Provides APIs for developers to integrate analytics capabilities into custom applications. • Data Preparation Tools: These include tools for data cleaning, transformation, and enrichment.

Data Wrapper

Data Wrapper is ideal for media enterprises and allows for the quick creation of charts, maps, and plots. It is completely web-based and easy to use.

Who Should Use Data Wrapper?

Data Wrapper can benefit media, news publications, government institutions and finance companies. It is especially useful for creating visually appealing and easily understandable visualisations.

Key Features of Data Wrapper

• User-Friendly Interface: Intuitive and easy to use, requiring no coding skills to create professional charts and maps. • Wide Range of Chart Types: Supports a variety of chart types, including bar, line, pie, scatter plots, and maps. • Customisable Visualizations: Offers extensive customisation options for colors, labels, and annotations to match branding and presentation needs. • Responsive Design: Ensures charts and maps are responsive and adapt to different screen sizes and devices. • Interactive Elements: Allows for adding interactive elements like tooltips, hover effects, and clickable legends. • Real-Time Data Integration: Supports live data updates, enabling real-time visualisation. • Embedding Capabilities: Easily embed charts and maps into websites and blogs with simple code snippets. • Export Options: This option lets users download visualisations in various formats, including PNG, PDF, and SVG. • Accessibility: Committed to creating accessible visualisations with features that support screen readers and keyboard navigation. • Data Security: Ensures data security with robust privacy policies and compliance with industry standards. • Collaboration Tools: Allows for team collaboration with shared projects and editing capabilities. • Support for Multiple Languages: Offers multi-language support for creating visualisations in different languages. • Easy Data Import: Supports importing data from various sources, including CSV files, spreadsheets, and web links. • API Integration: Provides APIs for integrating Data wrapper with other applications and services. • Customizable Templates: Use and modify templates to maintain consistency in visualisations. • Annotation Features: Add rich text annotations directly to charts and maps to provide additional context and information.

Always Go for the Best Tool

By choosing the right data visualisation tool, organizations can discover the full potential of their data, making complex information accessible and actionable for everyone. This empowers businesses to make data-driven decisions that drive growth and innovation.

Businesses are indulging in the continuous endeavour to leverage the full potential of their data, and they are constantly seeking innovative solutions for the same. A solution capable of gaining traction is the integration of Meta Llama 3 into the Snowflake ecosystem. Beinex has brought this to fruition by establishing an innovative work model that guarantees an application that functions within Snowpark Container Services and harnesses the capabilities of Llama 3. Figure 1: Screenshot from DocAI. Zaki Document Chatbot (DocAI) tapping into Llama 3 by Meta and running in the Snowflake ecosystem. Beinex tested Llama 3 on its in-house DocAI, a solution that runs on Snowflake using Snowpark Container services. The DocAI chatbot solution offers the flexibility to chat with documents such as PPT, PDF, word files, and text files. It currently uses llama3. Llama 3 is a major leap forward, establishing new standards for large language models. Its extensive training data, improved quality, and increased context length make it a powerful choice for document-related tasks, including our DocAI chatbot solution. The article will explain how Beinex deployed Llama 3 in the Snowflake ecosystem in the upcoming sections. Recently, notable advances have been made in large language models — sophisticated natural language processing (NLP) systems equipped with billions of parameters. These models have demonstrated remarkable abilities, including generating creative text, solving complex mathematical theorems, predicting protein structures, and more. They illustrate the immense potential benefits that AI can offer to billions of people on a global scale. Meta’s Llama (Large Language Model Meta AI), a state-of-the-art foundational large language model, is designed to support researchers in advancing their work within AI. By providing access to smaller yet highly efficient models like Llama, Meta aimed to empower researchers who may not have access to extensive infrastructure to delve into the study of these models. This democratization of access is pivotal in fostering innovation and progress in this dynamic and crucial field.

What is Llama 3?

Meta's latest advancement in the LLM (Large Language Model) series is Llama 3, the most sophisticated model with considerable advancements in performance and AI capabilities. Llama 3, built upon the architecture of Llama 2, is offered in 8B and 70B parameters, each featuring a base model and an instruction-tuned version tailored to enhance performance in specific tasks, particularly AI chatbot conversations. According to Meta, Llama 3 sets a new standard for open-source models, rivalling the performance of proprietary models available today. Llama 3 models will soon be accessible across various platforms, including AWS, Google Cloud, Hugging Face, Databricks, Kaggle, IBM Watson, Microsoft Azure, NVIDIA NIM, and Snowflake. Capabilities such as reasoning, code generation, and instruction following have seen substantial enhancements, rendering Llama 3 more adaptable and controllable. Meta plans to introduce additional capabilities, longer context windows, expanded model sizes, and enhanced performance. Utilizing Llama 3 technology, Meta AI emerges as one of the premier AI assistants globally, offering intelligence augmentation and support across various tasks, including learning, productivity, content creation, and connection facilitation.

Llama 2 vs Llama 3

According to Meta, the newly introduced models, Llama 3 8B with 8 billion parameters and Llama 3 70B with 70 billion parameters, represent a significant advancement in performance compared to their predecessors, Llama 2 8B and Llama 2 70B. Meta describes these models as a ‘major leap’ in performance. Llama 2 serves research and commercial purposes, excluding the top consumer companies globally. Llama 2 boasts enhancements such as training on 40% more data, doubling the context length, and leveraging a vast dataset of human preferences to ensure safety and helpfulness, backed by over 1 million annotations. On the other hand, Llama 3 represents the next step in Meta AI's LLM evolution, catering to research and commercial applications, provided monthly active users are under 700 million. Positioned as the successor to Llama 2, Llama 3 showcases state-of-the-art performance on benchmarks and is lauded by Meta as the 'best open-source model of their class.'

Ollama

There were times when accessing Large Language Models (LLMs) was restricted to cloud APIs offered by major providers like OpenAI and Anthropic. While these cloud API providers continue to dominate the market with user-friendly interfaces facilitating easy access for many users, it's important to recognize the trade-offs users make beyond the costs associated with pro plans or API usage. This trade-off involves granting providers full access to chat data. For those seeking to securely run LLMs on their hardware, the alternative has typically involved training their LLMs. Ollama, an open-source application, is designed to enable users to run, create, and share large language models locally through a command-line interface on MacOS and Linux. With Ollama, running LLMs on personal hardware requires minimal setup time. It caters to individuals seeking to run LLMs on their laptops, maintain control over their chat data without involving third-party services, and interact with models through a straightforward command-line interface. Additionally, Ollama offers various community integrations, including user interfaces and plugins for chat platforms.

Deploying Llama 3 in the Snowflake Ecosystem: What Beinex Did?

Deploying Llama 3 in the Snowflake Ecosystem means integrating the advanced language capabilities of Llama 3, the latest version of Meta’s language model, into the Snowflake data platform. It represents a significant breakthrough for organizations seeking to maintain control over their data. It allows users to directly leverage Llama 3's powerful natural language processing capabilities within Snowflake for various tasks such as data analysis, querying, and generating insights. 

Users can benefit from enhanced text understanding and generation

Enables more efficient and accurate data processing, analysis, and decision-making

Empowers users to interact with their data more intuitively and naturally

Drives greater productivity and insights within the Snowflake environment

Traditionally, utilizing modern LLMs at scale has necessitated reliance on proprietary services, often requiring organizations to abandon control over their data to service providers. This compromise between data residency and accessing advanced AI capabilities has been a conflict for many. However, a solution has emerged with Beinex's innovative approach and successful Proof of Concept (PoC). Beinex has devised a model ensuring that applications function seamlessly within Snowpark Container Services while harnessing the power of Llama 3. This integration means organizations can now deploy Llama 3 directly within the Snowflake ecosystem, eliminating the need for reliance on external proprietary services. Figure 2: Zaki Document Chatbot (DocAI) in action!

Deploying Llama 3 in the Snowflake Ecosystem: How Beinex Did it?

Here’s a detailed guide on deploying Llama 3 on Snowflake Container Services: Step 1: Create Necessary Objects -- Run by ACCOUNTADMIN to allow connecting to Hugging Face to download the model -- Stage to store LLM models CREATE STAGE <stagename> IF NOT EXISTS models DIRECTORY = (ENABLE = TRUE) ENCRYPTION = (TYPE='SNOWFLAKE_SSE'); -- Stage to store YAML specs CREATE STAGE <stagename> IF NOT EXISTS specs DIRECTORY = (ENABLE = TRUE) ENCRYPTION = (TYPE='SNOWFLAKE_SSE'); <br. -- Image repository CREATE OR REPLACE IMAGE REPOSITORY images; -- Compute pool to run containers CREATE COMPUTE POOL GPU_NV_S MIN_NODES = 1 MAX_NODES = 1 INSTANCE_FAMILY = GPU_NV_S; Step 2: Docker Image Code - ollama FROM ollama/ollama RUN $(ollama serve > output.log 2>&1 &) && sleep 10 && ollama pull llama3 && pkill ollama && rm output.log ENTRYPOINT ["ollama"] CMD ["serve"] Step 3: Tag and Push the Docker Image docker tag ollama .registry.snowflakecomputing.com/db/schema/image respository /ollama docker push .registry.snowflakecomputing.com db/schema/image repository /ollama Step 4: Docker Image - UDF FROM python:3.11 WORKDIR /app ADD ./requirements.txt /app/ RUN pip install --no-cache-dir -r requirements.txt ADD ./ /app EXPOSE 5000 ENV FLASK_APP=app CMD ["flask", "run", "--host=0.0.0.0"] App.py content is given below : from flask import Flask, request, Response, jsonify import logging import re import os from openai import OpenAI client = OpenAI( base_url='http://ollama:11434/v1', api_key="EMPTY", ) model = "llama3" app = Flask(__name__) app.logger.setLevel(logging.ERROR) def extract_json_from_string(s): logging.info(f"Extracting JSON from string: {s}") # Use a regular expression to find a JSON-like string matches = re.findall(r"\{[^{}]*\}", s) if matches: # Return the first match (assuming there's only one JSON object embedded) return matches[0] # Return the original string if no JSON object is found return s @app.route("/", methods=["POST"]) def udf(): try: request_data: dict = request.get_json(force=True) # type: ignore return_data = [] for index, col1 in request_data["data"]: completion = client.chat.completions.create( model=model, messages=[ { "role": "system", "content": "You are a bot to help extract data and should give professional responses", }, {"role": "user", "content": col1}, ], ) return_data.append( [index, extract_json_from_string(completion.choices[0].message.content)] ) return jsonify({"data": return_data}) except Exception as e: app.logger.exception(e) return jsonify(str(e)), 500 Step 6: YAML File spec: containers: - name: ollama image: <SNOW_ORG-SNOW_ACCOUNT>.registry.snowflakecomputing.com/ db/schema/image respository /llama3 resources: requests: nvidia.com/gpu: 1 limits: nvidia.com/gpu: 1 env: NUM_GPU: 1 MAX_GPU_MEMORY: 24Gib volumeMounts: - name: llm-workspace mountPath: /<stage name> - name: udf image: .registry.snowflakecomputing.com/ db/schema/image respository /ollama_udf endpoints: - name: chat port: 5000 public: false - name: llm port: 11434 public: false volumes: - name: llm-workspace source: "@<llm stage_name>" Step 7: Upload YAML File and Create Service Upload the YAML file to the created stage, where the stage name in the YAML file should match the stage created in Step 2. -- Create service create service llama3 IN COMPUTE POOL<name of compute pool created> FROM @dash_stage SPECIFICATION_FILE = '<name of yaml file uploaded>'; Step 8: Create Service Function Create a service function on the service (after it starts). create or replace function llama3(prompt text) returns text service=llama3 endpoint=chat; Check Service Status Use the following command to check the status of the service: SELECT v.value:containerName::varchar container_name, v.value:status::varchar status, v.value:message::varchar message FROM ( SELECT parse_json(system$get_service_status('<service name>')) ) t, LATERAL FLATTEN(input => t.$1) v;

Benefits of Deploying Llama 3 in the Snowflake Ecosystem

Superior AI apps courtesy of Llama 3: Allows organizations to harness the full potential of this advanced LLM.

Llama 3 offers avant-garde capabilities in natural language processing and generation, enabling the development of superior AI applications with enhanced performance, accuracy, and efficiency.

Data residency: Ensures that organizations maintain control over their data residency. With Snowflake's robust data governance and compliance features and Llama 3's deployment within the same ecosystem, organizations can confidently adhere to data residency requirements and regulations, ensuring that sensitive data remains within specified jurisdictions or regions.

Scalability: Provides organizations with remarkable scalability for their AI applications. This seamless scalability empowers organizations to handle large volumes of data and accommodate growing user demands without compromising performance.

Easy monitoring: Simplifies the monitoring and management of AI applications. Snowflake's intuitive interface and comprehensive monitoring tools enable organizations to easily track the performance, usage, and health of their deployed Llama 3 models. It facilitates proactive identification of issues, optimization of resources, and overall better management of AI workloads, ensuring smooth and efficient operations.

By leveraging Llama 3 within Snowflake, organizations can alleviate data sovereignty and control concerns. They can rest assured that their precious and sensitive data remains within their realm of control while still being able to build and scale superior applications. This development marks a significant milestone in bridging the gap between data residency requirements and accessing the most effective LLM technology.

-Mebin Joy