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Top Kubernetes Interview Questions and Answers

Top Kubernetes Interview Questions and Answers
Hostman Team
Technical writer
Infrastructure

In today's tech landscape, the Kubernetes container orchestration platform is widely used across various projects. With its increasing popularity and widespread adoption, Kubernetes often comes up during interviews for certain IT roles, including DevOps, SRE, system administration, development, and operations. The questions can range from very simple ones about cluster components to more advanced topics like networking within the cluster and network policies. In this article, we’ll go over the top Kubernetes interview questions and provide detailed answers.

What is Kubernetes?

Kubernetes is an open-source platform for managing containerized applications. It enables the deployment, scaling, and management of containerized workloads and services.

List the Main Components of a Kubernetes Cluster

At the core of Kubernetes lies the Control Plane, which resides on the master node. The Control Plane includes the following components:

  • kube-api-server – The API server processes REST requests and serves as the "brain" of the cluster. All interactions, including object creation and deletion, go through the API server, which also manages communication between cluster components.
  • etcd – A highly available key-value store that saves configuration data and cluster state. It can be deployed externally for improved fault tolerance. etcd is an independent project maintained by a separate team.
  • kube-scheduler – The component responsible for determining which nodes will run which pods. It monitors available resources on each node to balance workload distribution.
  • kube-controller-manager – Runs controllers that monitor resources and ensure the cluster matches the desired state by making necessary changes.
  • kube-proxy – A network service that acts as a load balancer. It distributes network traffic between pods and runs on every node in the cluster.

What is a Pod in Kubernetes?

A Pod is the smallest deployable unit in Kubernetes and serves as an abstraction for running containers. A pod usually contains one or more containers, its own IP address, and data storage. Kubernetes doesn’t interact directly with containers, but rather through pods.

What is the difference between Deployment and StatefulSet?

Both Deployment and StatefulSet are Kubernetes objects for managing applications, but they serve different purposes.

Deployment:

  • Used for managing stateless applications (e.g., web servers).
  • Supports rolling updates for zero-downtime deployments.
  • Pods are ephemeral with non-persistent names and IPs.
  • No state persistence: when a pod is deleted, its data is lost.

StatefulSet:

  • Designed for stateful applications (e.g., databases).
  • Pods have stable, unique names and identifiers that persist across restarts.
  • Supports Persistent Volumes to retain data between restarts.
  • Pods are created and terminated in a specific order, one at a time.

In conclusion, data persistence is the main difference between a Deployment and a StatefulSet. Use Deployment if the application does not require state to be preserved. However, if the application needs to retain its state, then a StatefulSet is the appropriate choice.

What is a Service in Kubernetes, and What are the Types?

A Service in Kubernetes defines how to access a set of pods. It provides a stable IP and DNS name, allowing internal or external communication with pods.

Types of Services:

  1. ClusterIP – The default type. Exposes the service on an internal IP, accessible only within the cluster.
  2. NodePort – Exposes the service on a specific port across all nodes. Allows external access via NodeIP:NodePort.
  3. LoadBalancer – Provisions an external load balancer (mainly in cloud environments) and assigns a public IP for external traffic distribution.
  4. ExternalName – Maps the service name to an external hostname or IP address using a DNS CNAME record. Works purely at the DNS level.

What is Ingress in Kubernetes?

Ingress is a Kubernetes object that defines rules for routing external HTTP/HTTPS traffic to internal services within the cluster. It enables fine-grained control over how traffic is handled and directed.

What is an Ingress Controller?

An Ingress Controller is a component that implements the Ingress rules. It typically consists of:

  • A reverse proxy (e.g., Nginx, HAProxy)
  • A controller that interacts with the Kubernetes API server to apply Ingress configuration and routing rules.

The controller watches for changes to Ingress objects and configures the reverse proxy accordingly to handle incoming traffic.

How to Store Sensitive Data (Secrets), Including Logins, Passwords, Tokens, and Keys?

Kubernetes provides the Secret object for storing sensitive information. There are six types of secrets:

  1. Opaque – A general-purpose secret type used to store any data.
  2. Service Account Token – Used to work with service accounts by generating a JWT token. Typically, the token is automatically created when a service account object is created.
  3. Basic Auth – Stores login and password in Base64-encoded format.
  4. SSH Auth – Used for SSH authentication. The secret contains a pre-generated private key.
  5. TLS Certificates – Involves using certificates and their private keys, provided in the manifest's tls.crt and tls.key fields (Base64-encoded). 
  6. Bootstrap Token – A special token type used to add new nodes to the Kubernetes cluster safely.

Secrets are usually injected into containers via volumeMount or secretKeyRef.

You can also use external secret management tools like HashiCorp Vault.

What Are Labels and Selectors, and What Are They Used For?

  • Labels are key-value metadata that can be attached to any Kubernetes object. They help to identify attributes of objects that are not directly related to the running services but can provide useful information to users — for example, the purpose of a deployed application or the environment in which it will run. In other words, labels are intended to distinguish between different instances of objects.
  • Selectors are used to filter or query objects based on their labels. A selector is a request to fetch objects that match specific label criteria.

What Are Probes in Kubernetes, What Types Exist, and What Are They Used For?

Probes in Kubernetes check the health and readiness of applications. There are three types:

  1. Liveness Probe: Checks whether a pod is running correctly. If the check fails, the pod is restarted automatically.
  2. Readiness Probe: Checks whether a pod is ready to receive network traffic. If it fails, the pod is excluded from load balancing, though it continues running.
  3. Startup Probe: Used for apps that take a long time to start. This probe checks the app's initial startup before liveness and readiness checks are activated.

What Is Pod Disruption Budget (PDB) and What Is It Used For?

Pod Disruption Budget is a Kubernetes feature used to ensure a minimum number of pods are available during voluntary disruptions (e.g., node maintenance or upgrades).

Example: If you have an application with 3 replicas that can tolerate the loss of 1 pod, then the PDB should specify that no more than 1 pod can be unavailable at any time. This prevents disruptions that would make the application non-functional.

How to Control Resource Usage in Containers?

Use requests and limits in your pod definitions:

  • Requests define the minimum amount of CPU and memory required for a pod to be scheduled. If the cluster doesn't have enough resources, the pod won't be scheduled.
  • Limits define the maximum amount of CPU and memory a pod can consume. The pod will be throttled or terminated if it exceeds these limits.

You can learn more about Kubernetes requests and limits in our article.

How to Expose an Application Running in Kubernetes to the External Network?

To provide external access to an application, you can use:

  • Ingress Controller – A preferred method for managing HTTP/HTTPS access. It routes traffic to services based on defined rules.
  • NodePort – Opens a specific port on all nodes for external access.
  • LoadBalancer – Provisions an external IP through a cloud load balancer.

What Is the CNI Interface?

CNI (Container Network Interface) is a Kubernetes specification maintained by the Cloud Native Computing Foundation. It defines how network interfaces are managed in Linux containers. CNI is responsible for connecting pods to the network.

CNI features are implemented through plugins, with popular ones including:

  • Calico
  • Weave
  • Flannel
  • Cilium

What Is CRI?

CRI (Container Runtime Interface) is the primary communication interface between the kubelet component in a Kubernetes cluster and the container runtime environment. Using CRI, Kubernetes interacts with the container engine responsible for creating and managing containers (Kubernetes itself does not create containers directly). 

Popular container runtimes that implement CRI include containerd and CRI-O.

What Is a Persistent Volume (PV)?

A Persistent Volume (PV) is a Kubernetes object used to store data persistently across pod lifecycles. Volumes in Kubernetes are implemented via plugins, and the platform supports the following types:

  • Container Storage Interface (CSI)
  • Fibre Channel (FC)
  • hostPath
  • iSCSI
  • Local Storage
  • Network File System (NFS)

What Is a Persistent Volume Claim (PVC)?

A Persistent Volume Claim (PVC) is a user request for storage resources. It allows users to claim a portion of a Persistent Volume based on parameters such as requested size and access mode. PVCs enable dynamic provisioning of storage in Kubernetes, meaning the cluster can automatically create a volume that matches the claim.

How to Assign Access Rights in a Kubernetes Cluster?

Kubernetes manages access control using RBAC (Role-Based Access Control). RBAC allows administrators to define who can do what within the cluster using the following entities:

  • Role – Defines a set of permissions within a specific namespace.
  • RoleBinding – Assigns a Role to a user or group within a namespace.
  • ClusterRole – Grants permissions across the entire cluster (not limited to a single namespace).
  • ClusterRoleBinding – Binds a ClusterRole to users or groups across all namespaces.
  • ServiceAccount – An identity used by Kubernetes workloads (pods) to interact with the API.

Conclusion

In this article, we covered a list of common interview questions that candidates might encounter when applying for IT roles involving Kubernetes. These questions span a range of foundational and advanced topics, including architecture, security, networking, and storage in Kubernetes.

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Alternative Products Although VMware leads the virtualization market, there are many other software products—both free and commercial—for virtualization, including: Proxmox VE Microsoft Hyper-V XenServer Red Hat Virtualization oVirt OpenStack Nutanix AHV Oracle VirtualBox QEMU/KVM Parallels Desktop Citrix Virtual Apps and Desktops Microsoft Azure Virtual Desktop Nutanix Frame Virtualization Capabilities Virtualization offers the following advantages: Isolation: Each VM operates independently, minimizing failure risks. Flexibility: Quick creation, cloning, and migration of VMs across servers. Efficiency: Optimized use of server resources. High Availability: Technologies like vMotion and Fault Tolerance ensure uninterrupted operation. Automation: Tools simplify deployment and monitoring. Business Benefits of Virtualization Virtualization provides businesses with opportunities to optimize processes and improve efficiency: Reduce hardware costs by consolidating servers. 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Neural Networks for Presentations: Overview and Comparison

Since the advent of the first neural networks, it was believed that they would only work with text. However, progress does not stand still, and today neural networks not only generate text but also work seamlessly with photos, videos, and graphics. Yet, this is not the limit of artificial intelligence capabilities. One such capability is creating presentations. Previously, users had to manually create presentations using various office programs such as Microsoft PowerPoint, LibreOffice Impress, OpenOffice Impress, Apple Keynote, Google Slides, and others. But with the development of AI, users now only need to compose a simple text query specifying the topic and parameters for the future presentation, or use one of the ready-made templates for quickly generating complete presentations. Today we will review MagicSlides (GPT for Slides), Plus AI, Gamma, SlidesGo, SendSteps, and Pitch. 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Disadvantages: Limited trial: the free trial lasts only 7 days and requires a credit card to start. Limited usage: Plus AI works only with Google Slides and Microsoft PowerPoint. Weak customization: limited flexibility for configuring specific templates. Gamma An AI-based platform designed to simplify presentation creation. The developers position the service as an alternative to Microsoft PowerPoint, offering a more flexible and interactive approach to visualizing ideas. It has numerous features and integrations with third-party services. Advantages: Create presentations from various sources, including Word, PDF, PowerPoint files, or URLs. One-Click Polish: automatically improves slide design and formatting to make them professional without manual adjustment. Collaborative features: users can work on presentations in real time, including editing, commenting, and suggesting changes. Multimedia support: allows adding GIFs, videos, and charts. 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Disadvantages: Template quality: some templates may appear outdated or poorly designed. SendSteps An AI-powered tool for creating interactive presentations. Simplifies preparation for educational, business, and conference purposes, saving time through automation of content, design, and interactive elements. Advantages: Focus on education: provides many ready-made templates for schools and universities, helping teachers save preparation time. Interactive quizzes enhance student engagement. Support for interactive elements: live polls, quizzes, Q&A sessions, and voting can be included in presentations. Unique content generation: includes a built-in plagiarism checker to ensure content originality. Disadvantages: Technical issues: generation or interactive features may sometimes fail. Limited free version: allows creating only two presentations, in English only, with restrictions on interactive content. Pitch An AI-powered presentation tool positioned as a competitor to Microsoft PowerPoint and Apple Keynote. It is designed with a focus on simplicity, collaborative work, and stylish design. Advantages: Simple and intuitive interactive editor with full customization: when creating a presentation, users can fully customize the background, color scheme, and fonts. Collaboration features: presentations are stored in a shared workspace, allowing users to manage access and coordinate actions with others. Wide selection of templates: includes a large library of templates for different purposes, including corporate templates. Templates can be easily customized if needed. Cloud synchronization: allows work on presentations from any device. Official mobile apps for iOS and Android: mobile applications simplify working with presentations on the go. Disadvantages: No offline mode: Pitch relies on the cloud, which may limit usability without an internet connection. 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Plus: $10/mo, Pro: $20/mo; 25% annual discount. Premium: €1.99/mo; 67% annual discount. Starter: $9.50/mo, Professional: $19.50/mo, Enterprise: quote-based; 31% annual discount. Pro: $20/mo, Business: $80/mo; 15% annual discount. Third-Party Integrations Google Slides, YouTube, Wikipedia Slack, Google Slides, Notion, Confluence, Coda, Canva, Slite, Guru, Gitbook, Gamma, Tome, Fermat, Obsidian Microsoft Word, Tally, Unsplash, Calendly, Microsoft 365, PowerPoint, Airtable, Typeform, Google Slides, Google Docs, Miro, Amplitude, Google Drive, GIPHY, Figma, Power BI Export presentations to Google Slides and PowerPoint None HubSpot, Slack, Notion, Loom, Unsplash External Source Support Supports YouTube videos, PDF, URL (web scraping), Wikipedia No support, only text input or PDF/PPTX/TXT upload No support, only text or PDF No, only text input or Freepik/Pexels media No, only text input or PDF/PPTX/DOCX/TXT upload No, only text input Slide Editing Limited, editing only through Google Slides Full editing in Google Slides/PowerPoint Full built-in editor Full built-in editor Limited editing Full built-in editor Animation & Interactivity Support Yes, through Google Slides (transitions, animations) Yes, automatic animations and transitions Yes, animations, video, interactive elements Limited Yes, polls, quizzes, timers, videos Yes, animations and transitions Export Formats PPTX, PDF, Google Slides PPTX, Google Slides PDF, PowerPoint (limited in free plan) PDF, JPEG, PPTX PDF, PPTX PDF, PPTX
22 September 2025 · 8 min to read
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Gemini AI: User Guide with Instructions

Large language models (LLMs) are gaining popularity today. They are capable of generating not only text but also many other types of content: code, images, video, and audio. Major companies, having large resources, train their models on text data collected by humanity throughout its history. Naturally, the international IT giant Google is no exception: it not only created its own model, Gemini, but also integrated it into its ecosystem of services. This article will discuss the large language model Gemini, its features, and capabilities. Overview of Gemini Gemini is a family of multimodal large language models (LLMs), launched by Google DeepMind in December 2023. Before that, the company used other models: PaLM and LaMDA. As of today, Gemini is one of the most powerful and flexible LLM neural networks, capable of conducting complex dialogues, planning multitasking scenarios, and working with any type of data, from text to video. Capabilities of Gemini The Gemini model not only generates content but also provides many additional functions and broad capabilities for working with different types of content: Multimodality. Through interaction with auxiliary models (Imagen and Veo), Gemini can work with different types of content: text, code, documents, images, audio, and video. Large context window. On paid plans, Gemini can analyze up to 1 million tokens in a single session. This is approximately one hour of video or 30,000 pages of text. AI agents. With some built-in functions, Gemini can autonomously perform chains of actions to search for information in external sources: third-party sites or documents in Google Drive. Integration with services. On paid subscription plans, Gemini integrates with services from the Google ecosystem: Gmail, Docs, Search, and many others. Special API. With the API provided by the Google Cloud platform, Gemini can be integrated into applications developed by third parties. With this set of features, Gemini can be used without limitations. It serves as a universal platform both for end users who need content generation or specific information, and for developers who want to integrate a powerful multimodal AI into their applications. How to Use Gemini AI As part of the Google ecosystem, the Gemini model has many touchpoints with the user. It is available in several places: from search results in the browser to office applications on mobile devices. So technically, you can access Google Gemini AI through various interfaces; all of them are merely “windows” into the central core. Google Search Results You can see Gemini at work in Google search results: the system supplements the list of found sites with additional reference information generated by Gemini. However, this doesn’t always happen. In Google, this feature is called Generative AI Snippet. Gemini analyzes the query, gathers information, and displays a short answer below the search box. Often, such a snippet turns out to be very useful. It provides a brief summary of the topic of interest. Thus, Google search results allow you to obtain information on a certain subject without going to websites. Web Application The most common and professional tool for interacting with Gemini is a dedicated website with a chatbot designed for direct dialogues with the model. This is where all the main Gemini features are available. With such dialogues, you can communicate, create text, write code, and generate images and videos. The Gemini web application has an interface typical of most LLM services: in the center is the chat with the model, at the bottom is a text input field with an option to attach files, and on the left is a list of started dialogues. The interaction algorithm with the model is simple. The user enters a query, and the model generates a response within a few seconds. The type of response can be anything: a story, recipe, poem, reference, code, image, or video. Yes, Gemini can generate images and videos using other models developed by Google: Imagen. A diffusion model for generating photorealistic images from text descriptions (text-to-image), notable for its high level of detail and realism. Veo. An advanced model for generating cinematic videos from text descriptions (text-to-video) or other images (image-to-video), notable for its high level of coherence and dynamics. Thanks to such integration, you can enter text prompts for generating images and videos directly inside the chatbot. Quick and convenient! The web version contains a wide range of tools for professional content generation and information gathering: Deep Research. A specialized mode for conducting in-depth, multi-step research using information from publicly available internet sources. With intelligent agents, Gemini autonomously searches, reads, analyzes, and synthesizes information from hundreds or even thousands of sources, ultimately producing a full report on the topic of interest. Unlike regular search, which provides short answers and links, Deep Research mode generates detailed reports by analyzing and summarizing information. However, one should understand that such deep analysis takes time, on average, from 5 to 15 minutes. Canvas. An interactive workspace that allows users to create, edit, and refine documents, code, and other materials in real time. Essentially, it is a kind of virtual “whiteboard” for more dynamic interaction with the language model. Thus, Canvas is focused on interactive creation, editing, and real-time content collaboration, while Deep Research is aimed at collecting and synthesizing information to provide comprehensive reports.   Deep Research Canvas Purpose In-depth data collection/analysis Interactive creation and editing of content Result Detailed reports Edited documents Mode Autonomous Active Execution time Several minutes Instant Task type Research, reviews, analytics, summaries Writing, coding, prototyping Users can attach various files to their messages, from documents to images. Along with a text prompt, Gemini can analyze media files, describing their content. Thus, the user can create multimodal queries consisting of both text and media simultaneously. This approach increases the accuracy of responses and creates a wider communication channel between humans and AI. In other words, the browser version is the main way to use Gemini. It is also worth briefly discussing how to register for Gemini and what is required for this. In most LLM services, authorization is required. Gemini is no exception. To launch the chatbot, you must sign in with a Google account. The registration process is standard. You need to provide your first and last name, phone number, and desired nickname. After this, you can use not only Gemini but also the rest of the Google ecosystem applications. Mobile App for Android and iOS You can download the official Gemini mobile app from Google Play or App Store. Functionality-wise, it is not very different from the web version available in a browser, but it has deeper features for user interaction and smartphone integration. Moreover, on many Android devices, the app comes pre-installed. Essentially, it is a mobile client that expands cross-platform access to the Gemini language model. The main differences lie in optimization for specific platforms: Content management. On the browser version accessed from a computer, it is much more convenient to work with text, code, tables, graphs, diagrams, images, and video. Conversely, the mobile app interface, designed for touch and gesture interaction, simplifies use on smartphones and tablets, but does not offer the same efficiency as a keyboard and mouse. Voice input and interaction. The mobile app has more advanced voice input and live interaction features (Gemini Live), allowing you to communicate with the model in real time, using the camera to show objects, the microphone for direct conversation, and screen capture to share images. The browser version lacks this functionality. Device-specific features. The Gemini mobile app integrates closely with smartphone functions (clock, alarm, calendar, documents) for more personalized interaction. The browser version exists in a kind of vacuum and knows almost nothing about the user’s computer. Apart from accessing other websites, it has no “window” into the outside world. In rare cases, it can extract data from other Google services such as Gmail and Google Docs. Multitasking convenience. On a large computer screen, it is easier to work with multiple windows, copy and paste information, which enables more efficient interaction with Gemini. On the other hand, the portability of the mobile app makes it possible to use the model “on the go,” simplifying quick queries during travel. Nevertheless, Google regularly releases updates, and Gemini’s functionality is constantly evolving. Therefore, the differences between the web version and the mobile app change over time. Gemini Assistant On many smartphones running the Android operating system, the Gemini model is gradually replacing the classic Google Assistant. That is, when you long-press the central button or say the phrase “Hey Google,” Gemini launches. It accepts the same voice commands but generates more accurate responses with expanded explanations and consolidated information from different apps. This may also include functions for managing messages, photos, alarms, timers, smart home devices, and much more. Some smartphone manufacturers specifically add a quick-access Gemini button directly to the lock screen, allowing you to instantly continue a conversation or ask a question without unlocking the phone. Thus, Gemini is gradually bringing together multiple functions, transforming into a unified smart control center for the phone. And most likely, this trend will only continue. Chrome Browser In new versions of Google’s Chrome browser, the Gemini neural network is built in by default and is available via an icon in the toolbar or by pressing a hotkey. This way, on any page, you can run queries to analyze text, create a summary, or provide brief explanations of the content of the open site. And let’s not forget third-party extensions that allow Gemini to be integrated into the browser, expanding its basic functionality. Google Ecosystem Services On paid plans, Gemini is available in many Google Workflow services. It adds interactivity to working with documents and content: Gmail. Helps draft and edit emails based on bullet points or existing text. Docs. Generates article drafts and edits text and sentence style. Slides. Instantly creates multiple versions of illustrations and graphics based on a description of the required visuals. Drive. Summarizes document contents, extracts key metrics, and generates information cards directly in the service interface. This is only a small list of apps in the Google ecosystem where you can use Gemini. The main point of integrating the model into services is to automate routine tasks and reduce the burden on the user. Plugins and Extensions for Third-Party Applications For third-party applications, separate plugins are available for integration with Gemini. The most common are extensions for IDE editors, messengers, and CRM systems. For example, there is the official Gemini Code Assist extension, which embeds Gemini into integrated development environments such as Visual Studio Code and JetBrains IDEs. It provides autocomplete, code generation and transformation, as well as a built-in chat and links to source documentation. There are also unofficial plugins for CRM systems like Salesforce and HubSpot, as well as for messengers like Slack and Teams. In these, Gemini helps generate ad copy and support responses, as well as automates workflows through the API. Versions and Pricing Plans for Gemini First, Google offers both free and paid plans for personal use: Free. A basic plan with limited functionality. Suitable for most standard tasks. Free of charge. Access to basic models, Gemini Flash and Gemini Pro. The first is optimized for fast and simple tasks, the second offers more advanced features but with limitations. Limited context window size up to 32,000 tokens (equivalent to about 50 pages of text). No integration with Google Workspace apps (Gmail, Docs, and others). No video generation functions. Data may be used to improve models (this can be disabled in settings, but it is enabled by default). Limited usage quotas for more advanced models and functions. Advanced. An enhanced plan with extended functionality. Suitable for complex tasks requiring deep data analysis. Pricing starts at $20/month. Access to advanced and experimental models without restrictions. Increased context window size up to 1 million tokens (equivalent to about 1,500 pages of text or 30,000 lines of code). Deep integration with Google Workspace apps. Image and video generation functions. Data is not used to improve models. Expanded voice interaction capabilities via Gemini Live, including the ability to show objects through the camera. Priority access to future AI features and updates. Second, there are extended plans for commercial (business) and non-commercial (educational) organizations, offering additional collaboration and management features: Business. Provides extended functionality of the Advanced plan with additional tools for team use. Designed for small and medium businesses. Pricing starts at $24/month. Enterprise. Provides extended functionality of the Business plan with additional tools for AI meeting summaries, improved audio and video quality, data privacy, and security protection. It also has higher limits and increased priority access. Designed for large international companies with high security and scalability requirements. Pricing starts at $36/month. Education. Provides full access to Gemini’s generative capabilities for educational institutions, including many additional features tailored to the learning environment. Custom pricing. Gemini API for Developers Specifically for developers engaged in machine learning and building services based on large language models, Google provides a full API for interacting with Gemini without a graphical user interface. Moreover, Google has separate cloud platforms for more efficient development and testing of applications built with the Gemini API: Google AI Studio. A lightweight and accessible platform designed for developers, students, and researchers who want to quickly experiment with generative models, particularly the Gemini family from Google. The tool is focused on working with large language models (LLMs): it allows you to quickly create and test prompts, adjust model parameters, and get generated content. The platform offers an intuitive interface without requiring deep immersion into machine learning infrastructure. Simply put, it’s a full-fledged sandbox for a quick start in the AI industry. Vertex AI. A comprehensive artificial intelligence and machine learning platform in Google Cloud, designed to simplify the development, deployment, and scaling of models. It combines various tools and services into a unified, consistent workflow. Essentially, it is a unified set of APIs for the entire AI lifecycle, from data preparation to training, evaluation, deployment, and monitoring of models. In short, it is a complete specialized ecosystem. Gemini Gems. A set of features in Google Gemini designed to automate repetitive tasks and fine-tune model behavior. It allows you to create mini-models tailored for specific, narrow tasks: creating recipes, writing code, generating ideas, translating text, assisting with learning, and much more. In addition to manual configuration, there are many ready-made templates. Naturally, Google provides the API as a separate channel for interacting with Gemini. With its help, developers can integrate text generation, code writing, image processing, audio, and video capabilities directly into their applications. Access to the API is possible through the Google Cloud computing platform. Working with Gemini without a graphical user interface is a separate topic beyond the scope of this article. You can find more detailed information about the Gemini API in the official Google Cloud documentation. Nevertheless, it can be said with certainty that working with the Gemini API is no different from working with the API of any other service. For example, here is a simple Python code that performs several text generation requests: from google import genai # client initialization client = genai.Client(api_key="AUTHORIZATION_TOKEN") # one-time text generation response = client.models.generate_content( model="gemini-2.0-flash", contents="Explain in simple words how generative AI works", ) print(response.text) # step-by-step text generation for chunk in client.models.stream_generate_content( model="gemini-2.0-pro", contents="Write a poem about spring", ): print(chunk.text, end="", flush=True) At the same time, Google provides numerous reference materials to help you master cloud-based AI generation: Documentation. Official reference for all possible capabilities and functions of the Gemini API. GitHub Examples. Numerous examples of using the Gemini API in Go, JavaScript, Python, and Java. GitHub Cookbook. Practical materials explaining how to use the Gemini API with ready-made script examples. Thus, Gemini offers developers special conditions and tools for integrating the model into the logic of other applications. This is not surprising, since Google has one of the largest cloud infrastructures in the world. Conclusion The Gemini model stands out favorably from many other LLM neural networks, supporting working with multimodal data: text, code, images, and video. Google, with its rich ecosystem, seeks to integrate Gemini into all its services, adding flexibility to the classic user experience.
19 September 2025 · 14 min to read

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