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What is a CDN: Principles of Content Delivery Networks

What is a CDN: Principles of Content Delivery Networks
Hostman Team
Technical writer
Infrastructure

Latency, latency, latency! It has always been a problem of the Internet. It was, it is, and it probably will be. Delivering data from one geographic point to another takes time.

However, latency can be reduced. This can be achieved in several ways:

  • Reduce the number of intermediate nodes on the data path from the remote server to the user. The fewer the handlers, the faster the data reaches the destination. But this is hardly feasible. The global Internet continues to grow and become more complex, increasing the number of nodes. More nodes = more power. That’s the global trend. Evolution!

  • Instead of regularly sending data over long distances, we can create copies of it on nodes closer to the user. Fortunately, the number of network nodes keeps growing, and the topology spreads ever wider. Eureka!

The latter option seems like an absolute solution. With a large number of geographically distributed nodes, it's possible to create a kind of content delivery network. In addition to the main function—speeding up loading—such a network brings several other benefits: traffic optimization, load balancing, and increased fault tolerance.

Wait a second! That's exactly what a CDN is—Content Delivery Network. So, let’s let this article explain what a CDN is, how it works, and what problems it solves. 

What is a CDN?

A CDN (Content Delivery Network) is a distributed network of servers designed to accelerate multimedia content delivery (images, videos, HTML pages, JavaScript scripts, CSS styles) to nearby users.

Like a vast web, the CDN infrastructure sits between the server and the user, acting as an intermediary. Thus, content is not delivered directly from the server to the user but through the powerful "tentacles" of the CDN.

What Types of Content Exist?

Since the early days of the Internet, content has been divided into two types:

  • Static (requires memory, large in size). Stored on a server and delivered to users upon request. Requires sufficient HDD or SSD storage.

  • Dynamic (requires processing power, small in size). Generated on the server with each user request. Requires enough RAM and CPU power.

The volume of static content on the Internet far exceeds that of dynamic content. For instance, a website's layout weighs much less than the total size of the images embedded in it.

Storing static and dynamic content separately (on different servers) is considered good practice. While heavy multimedia requests are handled by one server, the core logic of the site runs on another.

CDN technology takes this practice to the next level. It stores copies of static content taken from the origin server on many other remote servers. Each of these servers serves data only to nearby users, reducing load times to a minimum.

What Does a CDN Consist Of?

CDN infrastructure consists of many geographically distributed computing machines, each with a specific role in the global data exchange:

  • User. The device from which the user sends requests to remote servers.
  • Origin Server. The main server of a website that processes user requests for dynamic content and stores the original static files used by the CDN as source copies.
  • Edge Node. A server node in the CDN infrastructure that delivers static content (copied from the origin server) to nearby users. Also called a Point of Presence (PoP).

A single CDN infrastructure simultaneously includes many active users, origin servers, and edge nodes.

What Happens Inside a CDN?

First, CDN nodes perform specific operations to manage the rotation of static content:

  • Caching. The process of loading copies of content from the origin server to a CDN server, followed by optimization and storage.
  • Purge (Cache Clearing). Cached content is cleared after a certain period or on demand to maintain freshness on edge nodes. For example, if a file is updated on the origin server, the update will take some time to propagate to the caching nodes.

Second, CDN nodes have several configurable parameters that ensure the stable operation of the entire infrastructure:

  • Time to Live (TTL). A timeout after which cached content is deleted from an edge node. For images and videos, TTL can range from 1 day to 1 year; for API responses (JSON or XML), from 30 seconds to 1 hour; HTML pages may not be cached at all. CDN nodes usually respect the HTTP Cache-Control header.
  • Caching Rule. A set of rules that determines how an edge node caches content. The primary parameter is how long the file remains in the cache (TTL).
  • Restriction. A set of rules on the edge node that moderates access to cached content for security purposes. For example, an edge node may serve requests only from nearby IP addresses or specific domains.

Thus, static content flows from the origin server through edge nodes to users, cached based on specific caching rules, and cleared once the TTL expires. Meanwhile, access restrictions are enforced on every edge node for security.

How Does a CDN Work?

Let's see how a CDN works from the user's perspective. We can divide the process into several stages:

  1. User Request Execution. When a user opens a website, the browser sends requests to CDN servers specified in HTML tags or within JavaScript code (such as Ajax requests). Without a CDN, requests would go directly to the origin server.
  2. Finding the Nearest Server. Upon receiving the request, the CDN system locates the server closest to the user.
  3. Content Caching. If the requested content is in the cache of the found CDN server, it is immediately delivered to the user. If not, the CDN server sends a request to the origin server and caches the content.
  4. Data Optimization. Content copies on CDN servers are optimized in various ways. For example, files can be compressed using Gzip or Brotli to reduce size.
  5. Content Delivery. The optimized and cached content is delivered to the user and displayed in their browser.

For instance, if a website’s origin server is in Lisbon and the user is in Warsaw, the CDN will automatically find the nearest server with cached static content—say, in Berlin.

If there is no nearby CDN server with cached content, the CDN will request the origin server. Subsequent requests will then be served through the CDN.

The straight-line distance from Warsaw to Lisbon is about 2800 km, while the distance from Warsaw to Berlin is only about 570 km.

Someone unfamiliar with networking might wonder: “How can a CDN speed up content delivery if data travels through cables at the speed of light—300,000 km/s?”

In reality, delays in data transmission are due to technical, not physical, limitations:

  • Routing. Data passes through many routers and nodes, each adding small delays from processing and forwarding packets.
  • Network Congestion. High traffic in some network segments can lead to delays and packet loss, requiring retransmissions.
  • Data Transmission Protocols. Protocols like TCP include features such as connection establishment, error checking, and flow control, all of which introduce delays.

Thus, the difference between 2800 km and 570 km is negligible in terms of signal propagation. But from a network infrastructure perspective, it makes a big difference.

Moreover, a CDN server in Berlin, finding no cached content, might request it not from the origin server but from a neighboring CDN node in Prague, if that node has the content cached.

Therefore, CDN infrastructure nodes can also exchange cached content among themselves.

What Types of CDN Exist?

There are several ways to classify CDNs. The most obvious is based on the ownership of the infrastructure:

  • Public. The CDN infrastructure is rented from a third-party provider. Suitable for small and medium-sized companies.
  • Private. The CDN infrastructure is deployed internally by the company itself. Suitable for large companies and IT giants.

Each type has its own pros and cons:

 

Public

Private

Connection speed

High

Low

Initial costs

Low

High

Maintenance complexity

Low

High

Cost of large-scale traffic

High

Low

Control capabilities

Low

High

Dependence on third parties

High

Low

Many CDN providers offer free access to their infrastructure resources to attract users. However, in such cases, there are limitations on:

  • Server capacity
  • Traffic volumes
  • Geographical coverage
  • Advanced configuration options

Paid CDN providers use various pricing models:

  • Pay-as-you-go. Costs depend on the volume of data transferred, measured in gigabytes or terabytes.
  • Flat-rate pricing. Costs depend on the chosen plan with a fixed amount of available traffic.
  • Request-based pricing. Costs depend on the number of user requests made.

Deploying your own CDN infrastructure is a serious step, usually justified by strong reasons:

  • Public CDN costs exceed the cost of running your own infrastructure. For example, high expenses due to massive multimedia traffic volumes.
  • The product hits technical limitations of public CDNs. For example, heavy network loads or a specific user geography.
  • The project demands higher reliability, security, and data privacy that public CDNs cannot provide. For example, a government institution or bank.

Here are a few examples of private CDN networks used by major tech companies:

  • Netflix Open Connect. Delivers Netflix’s streaming video to users worldwide.
  • Google Global Cache (GGC). Speeds up access to Google services.
  • Apple Private CDN. Delivers operating system updates and Apple services to its users.

What Problems Does a CDN Solve?

CDN technology has evolved to address several key tasks:

  • Faster load times. Files load more quickly (with less latency) because CDN servers with cached static content are located near the user.
  • Reduced server load. Numerous requests for static content go directly to the CDN infrastructure, bypassing the origin server.
  • Global availability. Users in remote regions can access content more quickly, regardless of the main server’s location.
  • Protection against attacks. Properly configured CDN servers can block malicious IP addresses or limit their requests, preventing large-scale attacks.
  • Traffic optimization. Static content is compressed before caching and delivery to reduce size, decreasing transmitted data volumes and easing network load.
  • Increased fault tolerance. If one CDN server fails or is overloaded, requests can be automatically rerouted to other servers.

The CDN, being a global infrastructure, takes over nearly all core responsibilities for handling user requests for static content.

What Are the Drawbacks of Using a CDN?

Despite solving many network issues, CDNs do have certain drawbacks:

  • Costs. In addition to paying for the origin server, you also need to pay for CDN services.
  • Privacy. CDN nodes gain access to static data from the origin server for caching purposes. Some of this data may not be public.
  • Availability. A site’s key traffic may come from regions where the chosen CDN provider has little or no presence. Worse, the provider might even be blocked by local laws.
  • Configuration. Caching requires proper setup. Otherwise, users may receive outdated data. Proper setup requires some technical knowledge.

Of course, we can minimize these drawbacks by carefully selecting the CDN provider and properly configuring the infrastructure they offer.

What Kind of Websites Use CDNs?

In today’s cloud-based reality, websites with multimedia content, high traffic, and a global audience are practically required to use CDN technology. Otherwise, they won’t be able to handle the load effectively.

Yes, websites can function without a CDN, but the question is, how? Slower than with a CDN.

Almost all major websites, online platforms, and services use CDNs for faster loading and increased resilience. These include:

  • Google
  • Amazon
  • Microsoft
  • Apple
  • Netflix
  • Twitch
  • Steam
  • Aliexpress

However, CDNs aren’t just for the big players — smaller websites can benefit too. Several criteria suggest that a website needs distributed caching:

  • International traffic. If users from different countries or continents visit the site. For example, a European media site with Chinese readers.
  • Lots of static content. If the site contains many multimedia files. For example, a designer’s portfolio full of photos and videos.
  • Traffic spikes. If the site experiences sharp increases in traffic. For example, an online store running frequent promotions or ads.

That said, there are cases where using a CDN makes little sense and only complicates the web project architecture:

  • Local reach. If the site is targeted only at users from a single city or region. For example, a website for a local organization.
  • Low traffic. If only a few dozen or hundreds of users visit the site per day.
  • Simple structure. If the site is a small blog or a minimalist business card site.

Still, the main indicator for needing a CDN is a large volume of multimedia content.

Where Are CDN Servers Located?

While each CDN’s infrastructure is globally distributed, there are priority locations where CDN servers are most concentrated:

  • Capitals and major cities. These areas have better-developed network infrastructure and are more evenly spaced worldwide.
  • Internet exchange points (IXPs). These are locations where internet providers exchange traffic directly. Examples include DE-CIX (Frankfurt), AMS-IX (Amsterdam), LINX (London).
  • Data centers of major providers. These are hubs of major internet backbones that enable fast and affordable data transmission across long distances.

The smallest CDN networks comprise 10 to 150 servers, while the largest can include 300 to 1,500 nodes.

Popular CDN Providers

Here are some of the most popular, large, and technologically advanced CDN providers. Many offer CDN infrastructure as an add-on to their cloud services:

  • Akamai
  • Cloudflare
  • Amazon CloudFront (AWS CDN)
  • Fastly
  • Google Cloud CDN
  • Microsoft Azure CDN

There are also more affordable options:

  • BunnyCDN
  • KeyCDN
  • StackPath

Some providers specialize in CDN infrastructure for specific content types, such as video, streams, music, or games:

  • CDN77
  • Medianova

Choosing the right CDN depends on the business goals, content type, and budget. To find the optimal option, you should consider a few key factors:

  • Goals and purpose. What type of project needs the CDN: blog, online store, streaming service, media outlet?
  • Geography. The provider's network should cover regions where your target audience is concentrated.
  • Content. The provider should support caching and storage for the type of content used in your project.
  • Pricing. Which billing model offers the best value for performance?

In practice, it’s best to test several suitable CDN providers to find the right one for long-term use.

In a way, choosing a CDN provider is like choosing a cloud provider. They all offer similar services, but the implementation always differs.

Conclusion

It’s important to understand that a CDN doesn’t fully store static data; it only distributes copies across its nodes to shorten the distance between the origin server and the user.

Therefore, the main role of a CDN is to speed up loading and optimize traffic. This is made possible through the caching mechanism for static data, which is distributed according to defined rules between the origin server and CDN nodes.

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The technology allows combining several storage devices into a single logical unit. Depending on the type of RAID array, the user gets improved fault tolerance, increased performance, or both. Its configuration in technical environments is called the RAID level. There are four common types (marked by numbers): RAID 0 — involves striping data across disks during reading and writing, resulting in nearly double the speed compared to a single drive. Fault tolerance does not increase; this is only about improved performance. RAID 1 — mirrors disks, doubling fault tolerance. However, it does not affect data transfer speeds. In case of a disk failure, the system remains operational, and after replacing the disk, the mirror is restored. RAID 5 — a combined option with striping for reading/writing and parity data for fault tolerance. Requires at least 3 drives. It offers higher read speeds and safety, but slightly slower write speeds. RAID 10 — a combination of RAID 0 and RAID 1. 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20 August 2025 · 6 min to read
Infrastructure

Top 11 AI Video Generation Tools: Review and Feature Comparison

After OpenAI's successful debut in 2022, AI tools rapidly entered everyday life.  When we talk about text generation, ChatGPT comes to mind. When it comes to image generation, we think of Midjourney. Then there are Gemini, DALL-E, Claude, Stable Diffusion, and many other leading models in the field. But what comes to mind when it comes to video generation? Or at least, what should come to mind? That’s exactly what we’ll discuss in this article. 1. Kling Kling is a Chinese AI video generation tool developed by Kuaishou in 2024. It is one of the best video generation AI tools on the market, ideal for marketers, bloggers, and large teams who need to produce high-quality videos quickly. Kling's standout feature is its balanced blend of cinematic aesthetics and flexible settings—you can get hyper-realistic or stylized clips. The model processes both text prompts and static images, turning them into dynamic, high-quality videos—up to 10 seconds long, FullHD resolution (1080p), and 30 FPS. 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You can adjust aspect ratio, animation style, motion intensity, and transition smoothness. Dream Machine supports keyframe-based generation (start and end image), has an intuitive minimalist interface, and offers an API for integration. It’s not suitable for long, complex videos. But for fast marketing and ad content, it’s a top pick.   Free Plan Paid Plans (from $9/month) Resolution up to 720p up to 1080p Duration up to 10 sec up to 10 sec Generations up to 30/month from 120/month Faster Generation no yes Watermarks yes no Upscaling no up to 4K Extension no up to 30 sec Extra Features no yes 5. Runway Runway is an American AI video platform developed by Runway AI in 2018. It's a full-fledged cloud platform for generating and storing high-quality cinematic media. Runway is both powerful and easy to use. It excels at quickly creating short clips, experimenting with visual styles, and automating parts of the creative process. It can generate videos with outstanding photorealism and character motion consistency. It's one of the most advanced commercial tools for video generation. You can create clips from text or images, restyle existing footage, or edit content. By default, videos are 720p, 24 FPS, and 5 or 10 seconds long. However, you can upscale to 4K and extend to 40 seconds. Runway offers several models: Gen-2, Gen-3 Alpha, Gen-3 Alpha Turbo, Gen-4. The latest (Gen-4) allows for deep control over generation: aspect ratio, camera behavior, style prompts, and more.   Free Plan Paid Plans (from $9/month) Resolution up to 720p up to 720p (4K upscale) Duration 5 or 10 sec 5 or 10 sec Generations up to 5/month from 25/month Faster Generation no yes Watermarks yes no Upscaling no up to 4K Extension no up to 20 sec Extra Features no yes Note: Paid plans include up to 100 GB of cloud storage, while free users get only 5 GB. 6. 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It's important to understand that AI models that don't use images or video as input require more specificity in prompts—clear descriptions of visuals, environments, and details.   Free Plan Paid Plans (from $10/month) Resolution up to 480p up to 480p Duration 5 seconds 5 seconds Generations up to 30 per month from 80 per month Faster Generation up to 2 per day from 8 per day Watermarks yes no Upscaling no no Extension no up to 12 seconds Extra Features no yes 8. Sora Sora is a neural network created by OpenAI in 2024. Based on detailed text descriptions, Sora can generate images and videos with the highest level of detail. It’s a model whose output can easily be mistaken for real photos or videos. It’s significant that Sora was developed by OpenAI, a global leader in generative AI and the company behind ChatGPT and DALL·E. Sora’s interface follows the design system used across OpenAI products—sleek black theme and minimal elements. A small sidebar is on the left, a grid of popular user-generated content in the center, and a prompt field with configuration options at the bottom. Sora-generated videos have photo-realistic detail, whether hyperrealistic or animated, almost nothing gives away the AI origin. The quality and imagination in the visuals are astounding. The videos can be up to 20 seconds long, 1080p resolution, and 30 fps—significantly more than most competitors. Sora unifies all video configuration into the prompt itself—the real power of the model lies in the quality of your description. The better the prompt, the better the result. Thus, generating video with Sora becomes a constant game of tweaking prompts, words, and phrasing. Sora can definitely be considered one of the most advanced AI models for generating images and video.   Free Plan Paid Plans (from $20/month) Resolution – up to 1080p Duration – up to 20 seconds Generations – from 50 per month Faster Generation – yes Watermarks – no Upscaling – no Extension – no Extra Features – yes The free plan in Sora does not allow video generation at all—only image generation, limited to 3 per day. 9. Pika Pika is another AI-powered video creation service, launched in 2023. The platform is easy to use and designed for everyday users who are not experts in video editing or neural networks. Its primary use case is modifying existing video footage: adding transitions, virtual characters, changing a person’s appearance, and more. Still, Pika can also generate videos from scratch. Pika’s features are standard for AI video services: generation from text, from images, or between two frames (start and end). Maximum resolution is 1080p. Frame rate is 24 fps. Video duration is up to 10 seconds. Styles can vary—from cartoony to cinematic. In short, Pika is a simple and convenient tool for quickly creating videos from text or images without powerful hardware. It’s especially useful for prototyping, social media, marketing, and advertising.   Free Plan Paid Plans (from $10/month) Resolution up to 1080p up to 1080p Duration up to 10 seconds up to 10 seconds Generations up to 16 per month from 70 per month Faster Generation no yes Watermarks yes no Upscaling no no Extension no no Extra Features no yes Pika’s free plan has generation limits—you can create videos, but in small quantities. The standard paid plan increases your generation limits and unlocks newer model versions, but does not remove watermarks. The professional plan removes all limitations, provides access to advanced tools, speeds up generation, and removes watermarks from final videos. 10. Veo Veo is a video generation model developed in 2024 by DeepMind, a Google-owned company. There are several ways to access the model: Via Google Labs tools — VideoFX or VertexAI Through Google AI Studio Veo can be considered a full-fledged tool for creating high-quality, hyperrealistic clips indistinguishable from real footage. Of course, it also supports animation. Veo generates videos at 720p resolution, 24 fps, and up to 8 seconds long. In private developer previews, 1080p resolution and 4K upscaling are available—but not yet public. It accepts both text prompts and still images as input. For the latter, the neural network preserves the original composition and color palette. Most importantly, Veo supports various cinematic effects: time-lapse, panorama, slow-mo, and many more—with flexible parameter control. Veo ensures excellent consistency, stability, and smooth motion. Every video generated includes a SynthID digital watermark, invisible to the human eye or ear—a tool developed by Google to help detect AI-generated media. Thus, any image, video, or audio can be scanned using SynthID to verify AI generation. Veo also pays attention to small details—hair movement, fabric fluttering, atmospheric behavior, and more. As they say, the devil is in the details.   Free Plan Paid Plans Resolution up to 720p up to 720p Duration up to 8 seconds up to 8 seconds Generations up to 30 per month from 50 per month Faster Generation no yes Watermarks yes no Upscaling no no Extension no no Extra Features no yes Like most Google cloud services, Veo uses pay-as-you-go pricing—$0.50 per second or $30 per minute of generated video. So, a standard 10-second clip will cost $5—cheap for professionals, pricey for casual users. 11. Vidu Vidu is a Chinese AI model developed in 2024 by ShengShu AI in collaboration with Tsinghua University.  Vidu generates smooth, dynamic, and cohesive video clips, both realistic and animated. It can also add AI-generated audio tracks to videos. Vidu can accurately simulate the physical world, creating videos with developed characters, seamless transitions, and logical event chronology. The platform offers three main tools: generation from text, from images, and from videos. Additional tools include an AI voiceover generator and a collection of templates. Maximum video resolution is 1080p. Max duration is 8 seconds. Frame rate is up to 24 fps. The model is based on a "Universal Vision Transformer" (U-ViT) architecture, which processes text, image, and video inputs simultaneously to create coherent video sequences. This ensures object consistency throughout the video. For professionals and studios, Vidu is a powerful tool with great potential; for beginners, it’s an easy gateway into generative video.   Free Plan Paid Plans (from $8/month) Resolution up to 1080p up to 1080p Duration up to 8 seconds up to 8 seconds Generations up to 40 per month unlimited Faster Generation no yes Watermarks yes no Upscaling no no Extension no up to 16 seconds Extra Features no yes Which AI to choose? The vast majority of AI video generation services have similar video parameters: resolution from 720p to 1080p, durations of 5 to 10 seconds, and frame rates around 24 fps. Almost all can generate video based on text prompts, images, or video inputs. Differences in output results are usually minor—video styles and presence of visual artifacts revealing the AI.  The choice largely depends on your input and goals: text descriptions, images, or existing video. Some AI models offer higher detail than others. Always check the sample videos shown on service homepages. And keep in mind: video is a much more complex data format than text. Unlike LLMs, completely free AI video generation tools don’t exist as training the models and powering generation requires significant resources. That said, most services offer a low-tier paid plan that removes major limitations. Name Max Duration Max Resolution Max FPS Starting Price Kling 10 seconds 1080p 30 fps $3/month Hailuo AI 6 seconds 720p 25 fps $14/month Fliki 30 minutes 1080p 30 fps $28/month Dream Machine 10 seconds 1080p 24 fps $9/month Runway 10 seconds 720p 24 fps $15/month PixVerse 8 seconds 1080p 20 fps $10/month Genmo 5 seconds 480p 30 fps $10/month Sora 20 seconds 1080p 30 fps $20/month Pika 10 seconds 1080p 24 fps $10/month Veo 8 seconds 720p 24 fps $0.50/sec Vidu 8 seconds 1080p 24 fps $8/month
08 August 2025 · 15 min to read
Infrastructure

How Perplexity AI Works

In today's article, we will take a detailed look at the Perplexity AI neural network: we'll explore how it works, how to use it, how it differs from its main competitor ChatGPT, and what opportunities it offers for everyday use. What is Perplexity AI?  Perplexity AI is an artificial intelligence-based platform that combines the functionality of a chatbot and a search engine. The service's architecture is based on the use of large language models (LLMs). When developing Perplexity AI, the creators aimed to provide an alternative to traditional search engines that could help users find accurate and meaningful answers to complex and ambiguous questions. What Does Perplexity AI Do?  As previously mentioned, Perplexity is built on large language models. The supported models include Sonar, Claude 3.5 Sonnet, GPT-4.1, Gemini 1.5 Pro, Grok 3 Beta, and o1-mini. With access to multiple models, the neural network can generate accurate and comprehensive answers to user queries in real time. A key feature of Perplexity is its ability to analyze user queries while simultaneously gathering information from the internet in real time and generating responses with a list of all sources used. You can view sources not only for the entire generated text but also for individual sentences or even specific words. The Perplexity workflow includes: Query analysis: once the user submits a prompt (text request), the neural network analyzes its context and content using built-in language models. Data search: information is retrieved from the internet. The search includes not only articles and text-based data but also videos, social media posts, and user comments. Priority is given to authoritative sources. Response generation: the collected and processed information is compiled into a single response with citations and source links. Perplexity uses different data models to ensure the response is as accurate and reliable as possible. Additional functionality (if needed): in Copilot and Deep Research modes, the system refines queries further to deliver more accurate and relevant answers. Step-by-Step Guide: How to Use Perplexity AI  Let's explore how to use the neural network in practice. We'll start with the interface and its basic functions, then move on to using prompts to evaluate the results. Go to the official website of Perplexity AI. You will see the home page. By default, the interface will be in English. To view available interface languages or switch them, click on the language at the bottom of the page. The left-hand panel includes the following elements: New Thread button (plus icon) – allows you to start a new conversation or query. In Perplexity, a Thread is a separate message chain that is not connected to previous queries. Useful for asking about new topics. Home button – takes you back to the home page at any time. Discover – lets you view and customize a news blog with trending topics. Users can choose their interests and get fresh, relevant content. Spaces – used for creating and organizing workspaces to group conversations and uploaded files by topics or projects. The query interface includes: Search mode – the default mode where the AI analyzes the query and generates an answer in real time. Research mode – used for deep analysis and information gathering. It offers a more in-depth report with comprehensive source analysis. This mode takes a bit more time. Model selection – lets you choose one of eight supported AI models. In the free plan, only Auto mode is available, where Perplexity selects the best model based on the query. Source selection – you can choose from Web (all sources), Academic (scientific sources only), or Social (social media and informal sources). File attachments – Perplexity supports uploading files with your query. For example, you can upload a file with Python code to find errors. Supported formats include text files, PDFs, and images (JPEG, PNG). You can upload files from local devices, Google Drive, or Dropbox. Dictation mode – allows you to create queries via voice input. Submission is still manual. Voice mode – enables full voice interaction. You can dictate your query and receive voice responses. Unlike Dictation, Voice mode supports hands-free interaction. Using Text Prompts  Let's test how Perplexity AI handles user prompts.  We'll start with text-based queries and create several different prompts. The first one will test how the neural network handles a complex scientific topic. First prompt: I'm writing a scientific paper. Write a text on 'Differential Equations.' The text should cover basic first-order differential equations and partial differential equations. The style should be academic. As shown in the screenshot, the AI began by explaining what differential equations are. Then, following the prompt structure, it provided a breakdown of first-order and partial differential equations, complete with equations. Perplexity provides a list of sources used, which are shown in the Sources tab.  If the query includes a practical task (e.g., solving a math problem, writing a program), the AI uses technical sources and lists them in the Tasks section. The text is accompanied by numbered source links. Clicking a number opens the relevant page. On the right, a context menu appears, breaking down the highlighted text and showing each part's source.  You can reuse the AI's response to create a new query. Select a paragraph, sentence, or word, and click Add to follow-up. The selected fragment will be added to the new prompt field. Second prompt: What is a passive source? Give real-world examples and advice for beginners. This prompt tests how the AI provides practical advice.  As per the prompt, the AI also generated a block of beginner tips. As shown in the screenshots, Perplexity provided detailed examples and actionable advice, completing the task effectively. Using Files in Queries Next, we'll test file handling. We create a text file with Python code containing an intentional error (printed instead of print): print("\nNumbers from 1 to 5:") for i in range(1, 6):   printed(i, end=" ") We save the file as .txt (other extensions like .py or .js aren't supported due to security policies). Now we ask the AI to find and fix the error.  Image Search  Perplexity AI can both generate and search for images online using text prompts. Let’s search for an image online.  Prompt: Find an image of rainy London. There should be a telephone booth in the foreground and Big Ben in the background. As shown in the screenshot, the AI found a bunch of relevant images. To view more results, go to the Images tab. Comparing Perplexity AI vs ChatGPT  Perplexity AI's main competitor is ChatGPT. Below is a comparison table of their key features: Feature Perplexity AI ChatGPT Primary Purpose General-purpose tool for various tasks. Suitable for text creation, math problems, academic and educational content. Same as Perplexity: versatile use including text generation, coding, etc. Built-in Modes Search, Research Search, Reason, Deep Research Free Access Yes, but limited: auto model selection only; max 3 file uploads/day Yes, with limits: restricted use of GPT-4o, o4-mini, and deep research mode Paid Plans One plan: Pro at $20/month Four plans: Plus ($20/mo), Pro ($200/mo), Team ($25/mo billed annually), Enterprise (custom pricing) Mobile App Yes (iOS and Android) Yes (iOS and Android) Desktop App Yes (Windows and macOS) Yes (Windows and macOS) Hidden Features of Perplexity AI  Although it may appear similar to competitors, Perplexity has unique features that enhance the user experience: Financial Data Analysis: built-in tools for viewing stock quotes and financial reports, with data from Financial Modeling Prep. YouTube Video Summaries: the AI can summarize videos, regardless of language. Focus Mode: restricts search to academic papers or specific websites for faster, more targeted results. Advantages  Key strengths of Perplexity AI include: Real-time data sourcing for up-to-date answers. Convenient source tracking and citation. File upload support in queries. Built-in financial data analysis tools. Two work modes: Search and Research. The Research mode provides deeper, more detailed answers. Integrated voice assistant for prompts and conversations. Image generation and image search features. Built-in YouTube video summarization. Disadvantages  Like any neural network, Perplexity AI has its drawbacks: Free plan limitations. Prompt-dependent accuracy: for complex scientific/technical topics, even with many sources, it can sometimes give inaccurate responses. Conclusion  In this review, we examined Perplexity AI—a powerful tool built on large language models. It is well-suited for a wide range of tasks and stands out due to its advanced source-handling features and personalized approach.
07 August 2025 · 8 min to read

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