#Custom Python Development Services

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[webstep-technologies]

Custom Python Development Services 

In today's fast-paced digital landscape, businesses are constantly seeking innovative solutions to stay ahead of the competition and meet the evolving needs of their customers. Python, a versatile and dynamic programming language, has emerged as a preferred choice for building custom software solutions that drive efficiency, productivity, and growth. Let's delve into the transformative capabilities of Python and how custom Python development services can unlock your business's full potential. 

Why Choose Python for Custom Development? 

Python's popularity stems from its simplicity, readability, and extensive ecosystem of libraries and frameworks. Whether you're developing web applications, data analysis tools, artificial intelligence algorithms, or automation scripts, Python offers a robust platform to bring your ideas to life. Here are some compelling reasons to opt for Python for custom development: 

Ease of Learning and Use: 

Python's syntax is clean, concise, and easy to understand, making it an ideal choice for developers of all skill levels. Its simplicity enables rapid prototyping and iteration, allowing businesses to accelerate the development process and adapt to changing requirements seamlessly. 

Rich Ecosystem of Libraries and Frameworks: 

Python boasts a vast array of libraries and frameworks that cover virtually every aspect of software development. From Django and Flask for web development to NumPy and Pandas for data analysis, Python offers a treasure trove of tools to streamline development and enhance functionality. 

Cross-Platform Compatibility: 

Python is inherently cross-platform, meaning that code written in Python can run seamlessly on various operating systems, including Windows, macOS, and Linux. This flexibility ensures broad compatibility and simplifies deployment across diverse environments. 

Unlocking the Potential of Custom Python Development Services 

Tailored Solutions for Your Unique Needs: 

Custom Python development services focus on understanding your business objectives and crafting bespoke solutions that address your specific challenges and opportunities. Whether you need a scalable web application, a data-driven analytics platform, or an automation tool to streamline workflows, Python can be tailored to suit your requirements. 

Expertise in Full-Stack Development: 

A reputable Python development service provider brings expertise in full-stack development, encompassing both front-end and back-end technologies. This comprehensive approach ensures a cohesive and integrated solution that delivers a seamless user experience and robust functionality. 

Data Analysis and Machine Learning Capabilities: 

Python's extensive ecosystem of libraries, including NumPy, Pandas, SciPy, and scikit-learn, makes it a powerhouse for data analysis and machine learning applications. Custom Python development services leverage these tools to extract insights from data, automate decision-making processes, and drive actionable intelligence. 

Integration with Third-Party Services and APIs: 

Python seamlessly integrates with a wide range of third-party services and APIs, allowing for seamless connectivity and interoperability with existing systems and platforms. Whether you're integrating with payment gateways, social media APIs, or cloud services, Python provides the flexibility and versatility to meet your integration needs. 

Scalability and Performance Optimization: 

Python's scalability and performance have been further enhanced with advancements in optimization techniques and asynchronous programming models. Custom Python development services employ best practices to ensure that your applications can scale effortlessly to handle growing user demands while maintaining optimal performance and reliability. 

Conclusion:   In conclusion, Python stands out as a powerful and versatile language that empowers businesses to innovate, streamline operations, and drive growth. By harnessing the expertise of custom Python development services, businesses can unlock the full potential of this dynamic language and realize their vision for success. 

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[kalyanifidel]

From Idea to Implementation: Harnessing Python Development Services for Success

In today's fast-paced software development landscape, Python emerges as a dominant force, offering a plethora of capabilities for building resilient and scalable applications. Known for its simplicity, adaptability, and extensive library ecosystem, Python has become the top choice for developers worldwide.

At Fidel, we provide comprehensive outsourced Python development services led by a team of seasoned experts. With our wealth of experience and commitment to value-driven approaches, we consistently exceed expectations.

We'll delve into the Python development services offered by Fidel, exploring their features, advantages, and practical applications.

Empower Your Business with Our Python Development Services:

Custom Python Development: We tailor solutions precisely to your business requirements, leveraging Python's clean syntax and flexibility. Whether it's web apps, enterprise software, or automation, Python enables efficient scaling.

Python CMS Development: Utilizing Django CMS, Wagtail, and Plone, Python offers robust content management solutions. You can create dynamic websites and applications, streamline workflows, and adapt to market changes seamlessly.

Python for Machine Learning: With powerful tools like TensorFlow, PyTorch, and sci-kit-learn libraries, Python excels in AI and ML. Simplifying development processes, businesses can extract insights, automate tasks, and foster innovation.

Python Hybrid Programming: Python seamlessly integrates with other languages and technologies, facilitating hybrid solutions efficiently. By combining Python with C/C++ or JavaScript, businesses can build adaptable solutions.

Python Migration and Upgradation: Our services ensure smooth transitions and harness the latest features, minimizing disruption and maximizing ROI. Whether migrating legacy systems or upgrading versions, we prioritize continuity and efficiency.

Python QA and Testing: Python's testing frameworks guarantee reliability and security. Through automation and performance testing, businesses can deliver high-quality software that exceeds user expectations.

Benefits of Python Development Services:

Rapid Development: Python's concise syntax and extensive libraries accelerate the development process, enabling faster time-to-market.

Cost-Effectiveness: Python's open-source nature and abundant resources minimize development costs, making it an economical choice for businesses of all sizes.

Flexibility: Python's versatility allows developers to work across diverse domains, catering to a broad spectrum of project requirements.

Scalability: Python's scalability accommodates projects ranging from small startups to large enterprises, ensuring seamless growth and expansion.

Future-Proofing: Python's popularity and widespread adoption ensure a steady supply of skilled developers and continued support, safeguarding investments in Python-based solutions.

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[salasarcyber]

ChatGPT and AI: Transforming IT Services for a Smarter Tomorrow

In the realm of IT services, staying ahead of the curve isn't just an advantage; it's a necessity. In this rapidly evolving landscape, Artificial Intelligence (AI) and ChatGPT have emerged as groundbreaking technologies, reshaping how IT services are delivered, managed, and experienced. Let's explore the transformative impact of ChatGPT and AI in the realm of IT services, and how these innovations are paving the way for a smarter, more efficient tomorrow.

1. Enhanced Customer Support : AI-powered chatbots, fueled by ChatGPT, are revolutionizing customer support in IT services. These intelligent chatbots provide instant responses to customer queries, troubleshoot technical issues, and offer tailored solutions round the clock. This level of immediate and personalized support not only boosts customer satisfaction but also allows IT service providers to handle a higher volume of inquiries effectively.

2. Predictive Maintenance : AI algorithms, when integrated into IT systems, enable predictive maintenance. By analyzing vast amounts of data, these algorithms can anticipate hardware failures or software glitches before they occur. This proactive approach allows IT professionals to address potential issues before they escalate, minimizing downtime and ensuring uninterrupted service for clients.

3. Smart IT Management : AI-driven tools equipped with ChatGPT interfaces are transforming IT management. These tools can interpret natural language queries, allowing IT professionals to access information, generate reports, or even execute commands using simple text or voice inputs. This streamlines administrative tasks, making IT management more intuitive and efficient.

4. Efficient Data Analysis : AI technologies facilitate deep data analysis, aiding IT services in understanding client needs and user behaviors. By processing vast datasets, AI algorithms can uncover valuable insights, helping IT professionals make data-driven decisions. These insights inform service enhancements, enabling IT providers to tailor their offerings for maximum client satisfaction.

5. Cybersecurity Reinforcement : ChatGPT and AI-powered security systems offer robust cybersecurity solutions. These technologies can identify patterns in data traffic, detect anomalies, and recognize potential security threats. By continuously monitoring networks and user activities, AI-driven cybersecurity solutions bolster IT services, ensuring a secure digital environment for both clients and their customers.

6. Natural Language Interfaces : Natural language interfaces, powered by ChatGPT and AI, simplify interactions between users and IT services. Clients can articulate their issues or requirements in natural language, and these interfaces interpret, process, and respond accordingly. This user-friendly approach not only enhances client experience but also ensures efficient communication between IT professionals and clients.

7. Continuous Learning and Improvement : AI-driven systems have the capacity to learn from patterns and user interactions. This learning capability enables IT services to continuously improve their offerings. Whether it's optimizing software performance, refining customer support processes, or enhancing cybersecurity protocols, AI's ability to adapt and evolve ensures that IT services are always at the cutting edge of innovation.

In conclusion, the synergy between ChatGPT and AI is revolutionizing IT services. By leveraging these technologies, IT service providers can offer more efficient, personalized, and secure solutions to their clients. As the digital landscape evolves, embracing these innovations is not just an option; it's a strategic imperative. By integrating ChatGPT and AI into their service offerings, IT professionals are not only enhancing their capabilities but also shaping the future of IT services, one intelligent interaction at a time.

[paradisetechsoftsolution]

Artificial Intelligence service provider

Paradise Techsoft Solution is a leading company for Artificial Intelligence service provider. Our expert team of AI developers build chatbots for web and mobile applications that can be used by businesses to improve customer service and sales, as well as generate leads.

[mostlysignssomeportents]

What kind of bubble is AI?

My latest column for Locus Magazine is "What Kind of Bubble is AI?" All economic bubbles are hugely destructive, but some of them leave behind wreckage that can be salvaged for useful purposes, while others leave nothing behind but ashes:

https://locusmag.com/2023/12/commentary-cory-doctorow-what-kind-of-bubble-is-ai/

Think about some 21st century bubbles. The dotcom bubble was a terrible tragedy, one that drained the coffers of pension funds and other institutional investors and wiped out retail investors who were gulled by Superbowl Ads. But there was a lot left behind after the dotcoms were wiped out: cheap servers, office furniture and space, but far more importantly, a generation of young people who'd been trained as web makers, leaving nontechnical degree programs to learn HTML, perl and python. This created a whole cohort of technologists from non-technical backgrounds, a first in technological history. Many of these people became the vanguard of a more inclusive and humane tech development movement, and they were able to make interesting and useful services and products in an environment where raw materials – compute, bandwidth, space and talent – were available at firesale prices.

Contrast this with the crypto bubble. It, too, destroyed the fortunes of institutional and individual investors through fraud and Superbowl Ads. It, too, lured in nontechnical people to learn esoteric disciplines at investor expense. But apart from a smattering of Rust programmers, the main residue of crypto is bad digital art and worse Austrian economics.

Or think of Worldcom vs Enron. Both bubbles were built on pure fraud, but Enron's fraud left nothing behind but a string of suspicious deaths. By contrast, Worldcom's fraud was a Big Store con that required laying a ton of fiber that is still in the ground to this day, and is being bought and used at pennies on the dollar.

AI is definitely a bubble. As I write in the column, if you fly into SFO and rent a car and drive north to San Francisco or south to Silicon Valley, every single billboard is advertising an "AI" startup, many of which are not even using anything that can be remotely characterized as AI. That's amazing, considering what a meaningless buzzword AI already is.

So which kind of bubble is AI? When it pops, will something useful be left behind, or will it go away altogether? To be sure, there's a legion of technologists who are learning Tensorflow and Pytorch. These nominally open source tools are bound, respectively, to Google and Facebook's AI environments:

https://pluralistic.net/2023/08/18/openwashing/#you-keep-using-that-word-i-do-not-think-it-means-what-you-think-it-means

But if those environments go away, those programming skills become a lot less useful. Live, large-scale Big Tech AI projects are shockingly expensive to run. Some of their costs are fixed – collecting, labeling and processing training data – but the running costs for each query are prodigious. There's a massive primary energy bill for the servers, a nearly as large energy bill for the chillers, and a titanic wage bill for the specialized technical staff involved.

Once investor subsidies dry up, will the real-world, non-hyperbolic applications for AI be enough to cover these running costs? AI applications can be plotted on a 2X2 grid whose axes are "value" (how much customers will pay for them) and "risk tolerance" (how perfect the product needs to be).

Charging teenaged D&D players $10 month for an image generator that creates epic illustrations of their characters fighting monsters is low value and very risk tolerant (teenagers aren't overly worried about six-fingered swordspeople with three pupils in each eye). Charging scammy spamfarms $500/month for a text generator that spits out dull, search-algorithm-pleasing narratives to appear over recipes is likewise low-value and highly risk tolerant (your customer doesn't care if the text is nonsense). Charging visually impaired people $100 month for an app that plays a text-to-speech description of anything they point their cameras at is low-value and moderately risk tolerant ("that's your blue shirt" when it's green is not a big deal, while "the street is safe to cross" when it's not is a much bigger one).

Morganstanley doesn't talk about the trillions the AI industry will be worth some day because of these applications. These are just spinoffs from the main event, a collection of extremely high-value applications. Think of self-driving cars or radiology bots that analyze chest x-rays and characterize masses as cancerous or noncancerous.

These are high value – but only if they are also risk-tolerant. The pitch for self-driving cars is "fire most drivers and replace them with 'humans in the loop' who intervene at critical junctures." That's the risk-tolerant version of self-driving cars, and it's a failure. More than $100b has been incinerated chasing self-driving cars, and cars are nowhere near driving themselves:

https://pluralistic.net/2022/10/09/herbies-revenge/#100-billion-here-100-billion-there-pretty-soon-youre-talking-real-money

Quite the reverse, in fact. Cruise was just forced to quit the field after one of their cars maimed a woman – a pedestrian who had not opted into being part of a high-risk AI experiment – and dragged her body 20 feet through the streets of San Francisco. Afterwards, it emerged that Cruise had replaced the single low-waged driver who would normally be paid to operate a taxi with 1.5 high-waged skilled technicians who remotely oversaw each of its vehicles:

https://www.nytimes.com/2023/11/03/technology/cruise-general-motors-self-driving-cars.html

The self-driving pitch isn't that your car will correct your own human errors (like an alarm that sounds when you activate your turn signal while someone is in your blind-spot). Self-driving isn't about using automation to augment human skill – it's about replacing humans. There's no business case for spending hundreds of billions on better safety systems for cars (there's a human case for it, though!). The only way the price-tag justifies itself is if paid drivers can be fired and replaced with software that costs less than their wages.

What about radiologists? Radiologists certainly make mistakes from time to time, and if there's a computer vision system that makes different mistakes than the sort that humans make, they could be a cheap way of generating second opinions that trigger re-examination by a human radiologist. But no AI investor thinks their return will come from selling hospitals that reduce the number of X-rays each radiologist processes every day, as a second-opinion-generating system would. Rather, the value of AI radiologists comes from firing most of your human radiologists and replacing them with software whose judgments are cursorily double-checked by a human whose "automation blindness" will turn them into an OK-button-mashing automaton:

https://pluralistic.net/2023/08/23/automation-blindness/#humans-in-the-loop

The profit-generating pitch for high-value AI applications lies in creating "reverse centaurs": humans who serve as appendages for automation that operates at a speed and scale that is unrelated to the capacity or needs of the worker:

https://pluralistic.net/2022/04/17/revenge-of-the-chickenized-reverse-centaurs/

But unless these high-value applications are intrinsically risk-tolerant, they are poor candidates for automation. Cruise was able to nonconsensually enlist the population of San Francisco in an experimental murderbot development program thanks to the vast sums of money sloshing around the industry. Some of this money funds the inevitabilist narrative that self-driving cars are coming, it's only a matter of when, not if, and so SF had better get in the autonomous vehicle or get run over by the forces of history.

Once the bubble pops (all bubbles pop), AI applications will have to rise or fall on their actual merits, not their promise. The odds are stacked against the long-term survival of high-value, risk-intolerant AI applications.

The problem for AI is that while there are a lot of risk-tolerant applications, they're almost all low-value; while nearly all the high-value applications are risk-intolerant. Once AI has to be profitable – once investors withdraw their subsidies from money-losing ventures – the risk-tolerant applications need to be sufficient to run those tremendously expensive servers in those brutally expensive data-centers tended by exceptionally expensive technical workers.

If they aren't, then the business case for running those servers goes away, and so do the servers – and so do all those risk-tolerant, low-value applications. It doesn't matter if helping blind people make sense of their surroundings is socially beneficial. It doesn't matter if teenaged gamers love their epic character art. It doesn't even matter how horny scammers are for generating AI nonsense SEO websites:

https://twitter.com/jakezward/status/1728032634037567509

These applications are all riding on the coattails of the big AI models that are being built and operated at a loss in order to be profitable. If they remain unprofitable long enough, the private sector will no longer pay to operate them.

Now, there are smaller models, models that stand alone and run on commodity hardware. These would persist even after the AI bubble bursts, because most of their costs are setup costs that have already been borne by the well-funded companies who created them. These models are limited, of course, though the communities that have formed around them have pushed those limits in surprising ways, far beyond their original manufacturers' beliefs about their capacity. These communities will continue to push those limits for as long as they find the models useful.

These standalone, "toy" models are derived from the big models, though. When the AI bubble bursts and the private sector no longer subsidizes mass-scale model creation, it will cease to spin out more sophisticated models that run on commodity hardware (it's possible that Federated learning and other techniques for spreading out the work of making large-scale models will fill the gap).

So what kind of bubble is the AI bubble? What will we salvage from its wreckage? Perhaps the communities who've invested in becoming experts in Pytorch and Tensorflow will wrestle them away from their corporate masters and make them generally useful. Certainly, a lot of people will have gained skills in applying statistical techniques.

But there will also be a lot of unsalvageable wreckage. As big AI models get integrated into the processes of the productive economy, AI becomes a source of systemic risk. The only thing worse than having an automated process that is rendered dangerous or erratic based on AI integration is to have that process fail entirely because the AI suddenly disappeared, a collapse that is too precipitous for former AI customers to engineer a soft landing for their systems.

This is a blind spot in our policymakers debates about AI. The smart policymakers are asking questions about fairness, algorithmic bias, and fraud. The foolish policymakers are ensnared in fantasies about "AI safety," AKA "Will the chatbot become a superintelligence that turns the whole human race into paperclips?"

https://pluralistic.net/2023/11/27/10-types-of-people/#taking-up-a-lot-of-space

But no one is asking, "What will we do if" – when – "the AI bubble pops and most of this stuff disappears overnight?"

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2023/12/19/bubblenomics/#pop

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

CC BY 3.0 https://creativecommons.org/licenses/by/3.0/deed.en

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Best Web Development Company in India

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Outsource Data Engineering for Business Success | Tech Active

Explore how outsourcing data engineering can fuel business success with Tech Active's expert guide. Learn to reduce costs, gain expertise, and make data-driven decisions for growth. 

[izicodes]

hi hi! love your blog! I am also working on building sites for my portfolio but am a little stumped on how/where to deploy them. would you mind sharing what you are using for deployment? thanks!

Places to Deploy Your Website

Hiya! I know a few places I've tried in the past and some I am yet to try but I know other developers use them!

GitHub pages

GitHub Pages is a free static site hosting service that allows you to publish your website directly from a GitHub repository. It supports HTML, CSS, and JavaScript, as well as Jekyll, a static site generator. I used GitHub pages a lot since I use GitHub to keep all my repositories.

Replit

Replit is a cloud-based development environment that provides an integrated IDE, code editor, and hosting platform all in one place. With Replit, you can easily create and deploy web apps, games, and other projects in multiple programming languages such as Python, HTML, CSS, and JavaScript. I use Replit a lot too for my other much smaller projects that I can’t upload on GitHub to run the program online!

Netlify

Netlify offers a free plan for static site hosting that includes features such as continuous deployment, custom domains, and SSL encryption. It supports HTML, CSS, and JavaScript, as well as serverless functions and other backend technologies. 

Heroku

Heroku offers a free plan for hobbyist developers that allows you to deploy up to 5 applications. It supports many languages and frameworks, including Ruby, Node.js, Python, Java, PHP, and Go. Heroku allows free hosting for small applications.

Firebase Hosting

Firebase Hosting is a free service that allows you to host and deploy your web app or static content to a global content delivery network (CDN) with SSL encryption. It supports HTML, CSS, JavaScript, and other static assets. It allows free hosting for small applications.

Surge

Surge is a free static site hosting service that allows you to publish your website with a custom domain or a Surge subdomain. It supports HTML, CSS, JavaScript, and other static assets. Allows free hosting with unlimited bandwidth.

Each of these free deployment options has its own cons such as: 

Its lack of server-side functionality

Limited database support

The cost of advanced features

Limited control over the infrastructure

May not be suitable for more complex websites or applications 

However, for small projects, I think you’ll be fine with the free options!

Hoped this helps and good luck with your websites’ deployments! 🥰🙌🏾💗

[visiontrek]

Why Python Development Services Are Crucial for Data Science and Machine Learning

 In the dynamic landscape of technology, Python has emerged as a powerhouse for data science and machine learning applications. Its versatility, ease of use, and extensive libraries make it a preferred choice for developers and organizations alike. In this blog, we'll delve into the significance of Python Development Services, exploring how Python plays a pivotal role in data science, machine learning, and artificial intelligence.

read more at https://visiontrekcommunication.blogspot.com/2023/12/why-python-development-services-are.html

[govindhtech]

Gemini Code Assist Enterprise: AI App Development Tool

Introducing Gemini Code Assist Enterprise’s AI-powered app development tool that allows for code customisation.

The modern economy is driven by software development. Unfortunately, due to a lack of skilled developers, a growing number of integrations, vendors, and abstraction levels, developing effective apps across the tech stack is difficult.

To expedite application delivery and stay competitive, IT leaders must provide their teams with AI-powered solutions that assist developers in navigating complexity.

Google Cloud thinks that offering an AI-powered application development solution that works across the tech stack, along with enterprise-grade security guarantees, better contextual suggestions, and cloud integrations that let developers work more quickly and versatile with a wider range of services, is the best way to address development challenges.

Google Cloud is presenting Gemini Code Assist Enterprise, the next generation of application development capabilities.

Beyond AI-powered coding aid in the IDE, Gemini Code Assist Enterprise goes. This is application development support at the corporate level. Gemini’s huge token context window supports deep local codebase awareness. You can use a wide context window to consider the details of your local codebase and ongoing development session, allowing you to generate or transform code that is better appropriate for your application.

With code customization, Code Assist Enterprise not only comprehends your local codebase but also provides code recommendations based on internal libraries and best practices within your company. As a result, Code Assist can produce personalized code recommendations that are more precise and pertinent to your company. In addition to finishing difficult activities like updating the Java version across a whole repository, developers can remain in the flow state for longer and provide more insights directly to their IDEs. Because of this, developers can concentrate on coming up with original solutions to problems, which increases job satisfaction and gives them a competitive advantage. You can also come to market more quickly.

GitLab.com and GitHub.com repos can be indexed by Gemini Code Assist Enterprise code customisation; support for self-hosted, on-premise repos and other source control systems will be added in early 2025.

Yet IDEs are not the only tool used to construct apps. It integrates coding support into all of Google Cloud’s services to help specialist coders become more adaptable builders. The time required to transition to new technologies is significantly decreased by a code assistant, which also integrates the subtleties of an organization’s coding standards into its recommendations. Therefore, the faster your builders can create and deliver applications, the more services it impacts. To meet developers where they are, Code Assist Enterprise provides coding assistance in Firebase, Databases, BigQuery, Colab Enterprise, Apigee, and Application Integration. Furthermore, each Gemini Code Assist Enterprise user can access these products’ features; they are not separate purchases.

Gemini Code Support BigQuery enterprise users can benefit from SQL and Python code support. With the creation of pre-validated, ready-to-run queries (data insights) and a natural language-based interface for data exploration, curation, wrangling, analysis, and visualization (data canvas), they can enhance their data journeys beyond editor-based code assistance and speed up their analytics workflows.

Furthermore, Code Assist Enterprise does not use the proprietary data from your firm to train the Gemini model, since security and privacy are of utmost importance to any business. Source code that is kept separate from each customer’s organization and kept for usage in code customization is kept in a Google Cloud-managed project. Clients are in complete control of which source repositories to utilize for customization, and they can delete all data at any moment.

Your company and data are safeguarded by Google Cloud’s dedication to enterprise preparedness, data governance, and security. This is demonstrated by projects like software supply chain security, Mandiant research, and purpose-built infrastructure, as well as by generative AI indemnification.

Google Cloud provides you with the greatest tools for AI coding support so that your engineers may work happily and effectively. The market is also paying attention. Because of its ability to execute and completeness of vision, Google Cloud has been ranked as a Leader in the Gartner Magic Quadrant for AI Code Assistants for 2024.

Gemini Code Assist Enterprise Costs

In general, Gemini Code Assist Enterprise costs $45 per month per user; however, a one-year membership that ends on March 31, 2025, will only cost $19 per month per user.

Read more on Govindhtech.com