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What is a Service Level Agreement (or SLA)

What is a Service Level Agreement (or SLA)
Hostman Team
Technical writer
Infrastructure

SLA is an agreement that outlines what kind (and what level) of service a certain company can provide. This term is mostly used in industries like television or Information Technology.

Unlike regular service contracts Service Level Agreement offers an exceptional amount of detail provided with descriptions of service quality, tech support response time and other indicators.

General SLA principles

The service level agreement usually follows these principles:

  • The interaction between the provider and the client must be as transparent as possible. Every process has to have a clear and reasonable purpose. No blurred terms and puzzled wordings allowed. Both sides should avoid using specific expressions that might be misunderstood.

  • The rules and rights for both sides have to be totally understandable. For instance, a company promises that all the provided services will be accessible 99.99% of the time and if the user finds out that it is not true he should have an opportunity to receive compensation.

  • Expectations management. For example, clients expect tech support to be available at any time as well as answers to the most insignificant questions. But providers can't offer such service. Accordingly a client must change provider or lower his expectations. Or the company has to make the tech support team more performant.

SLA usually contains such data as the amount of time that is needed to resolve a client's problems or what kind of compensation and in what cases the user has the right to ask for it, etc.

SLA doesn't have to be a giant pile of sheets. The most important thing for any company is to make the service level agreement as transparent and natural as possible. Look at successful and large corporations such as Amazon. SLA for their service S3 is fully described on just one page.

Here (link to Amazon) you can read about the monthly uptime of the services and about the level of compensation you'll receive if they are not achieved.

What typical SLA consists of

We peeked into Amazon SLA a couple of lines ago. That is not a standard. It is just one of the ways to design your SLA which takes into consideration the specific characteristics of the service provided by the company (and authors of SLA).

If we're talking about the IT industry, a typical SLA would contain:

  • The rules for using the product or providing some service.

  • Responsibilities of both sides. Mechanisms that help users and providers to control each other in some way.

  • Concrete procedures that might be undertaken by the provider to fix any flaws the user stumbles upon.

You can also find the exactly how long an actual service level agreement will be legitimate. Sometimes both client and provider describe ways of adding new demands to the functionality of the services if necessary.

Moreover, it is normal to list indicators that somehow refer to the actual level of service quality.

  • The reliability and availability of the service.

  • The time it takes to react to system faults and malfunctions.

  • The time it takes to resolve system faults and malfunctions.

You might want to add the way of settling the scores with the client. As an example, some companies ask for money after providing a certain level of service, some companies insist on paying for a fixed plan, etc. Don't forget to tell users about fines if they exist. If it is possible for the client to receive compensation, the job of the service provider is to explain why, how and where the customer can get it.

Key parameters of SLA

The parameters of SLA — is a set of metrics that can be measured somehow. There's no way you would write in SLA something along the lines of "We will fix any fault before you know about it". It is an example of a blurred statement that will only make it harder to achieve a level of agreement between the service provider and the customer.

Let us talk about such a metric as operation mode. It shouldn't be abstract. It must include concrete dates and periods of time when customers can count on the technical support team.

There are examples when a company divides all the customers into separate groups. One of them is allowed to access tech support any time. The second is only allowed to ask for help on workdays. The third can't call for help at all.

Such metrics are extremely important because there's no other way to clearly understand what both sides can expect from their collaboration. That's why you have to consider a few things:

  • Metrics must be published and accessible for anyone.

  • There shouldn't be any statements that can be misunderstood.

  • Any changes in metrics should not happen without warning. Customers have the right to know about any change beforehand.

When you work on establishing metrics do not overdo it. It might increase the price of services provided by the company.

Let's see. We have a problem that might be solved in about 4 hours by a mediocre specialist. An expert can solve the same problem in 2 hours. It is not a good practice to write "2 hours" in your SLA. The job done by a specialist will become much more expensive in the quickest way possible. If you write "1 hour" you will not only pay much more but also will often pay compensations to thoughtful users who believed you but were cheated on.

Operation mode and work hours are not the only metrics that you should care about. What else is important? For example, the time it takes for tech support to respond. Metrics themselves can differ because of external variables like customer status or the seriousness of the problem.

Let's say some company is outsourcing some kind of IT service. This company has a group of users that pays for the premium plan and another group that does not. The time it takes for a tech support team to respond to clients from different groups might vary because one of them is obviously more privileged. One group might get help in 15 minutes and the other in a day. If there are such differences it is extremely important to reflect it in a service level agreement.

Beside the reaction time it is important to speak about the time it takes to resolve the problem the user has run into. The logic of regulating this metric is exactly the same. Even if the customer is really important to the company his queries might be dealt with at differing speeds depending on the seriousness of the problem.

We have a client that has an extremely severe problem — the local network is down and all the inner processes are consequently stuck. Such problems must be prioritized. SLA might include the details for this kind of problem and what type of help the client can expect.

The same customer can ask for help another day but with less critical malfunction. For example, the whole network works well but a few new devices need to be connected to it. It is ok to spend hours and days on such things.

These and a lot of other considerations should be reflected in SLA and accepted both by customer and service provider. Such an approach can help to lessen the amount of potential conflicts. Everything becomes clear and understandable for anyone.

Availability of the service

For the provider, one of the most important parameters in SLA is availability. This metric can be measured in days, hours or minutes for a certain period of time. For instance, a provider can guarantee anyone that its cloud storage will be accessible 99.99% of the time during the year.

In absolute numbers 99 and 100 seem to be quite the same thing. But the difference becomes huge if we analyze those numbers considering that this percentage refers to a period of 365 days. If we say 99% it actually means that the customers agree that the server might be not available for about 4 days per year. And when we talk about 100% there shouldn't be any stand by. But it is impossible to guarantee such reliability. It is always 99.**% with some numbers after the dot.

Considering Hostman, we guarantee 99,99% of uptime. It means that servers might not work for as long as 52 minutes per year.

You might find providers that promise uptime up to 99.9999% and swear that servers will be off for 15 minutes at most. But it's not a good idea to say such things for two important reasons:

  1. The higher the promised uptime the higher the price of the service.

  2. Not that many clients even need such uptime. In most cases 99.98% is more than enough.

The amount of 9s is less important than the actual time that is fixed in SLA. The year is the default period of time used as a metric in SLAs. That means that 99.95% of uptime is 4.5 hours of stand by per year.

But some providers might use different metrics. If there's no concrete info, the user must ask what period of time is used to evaluate the uptime. Some companies try to cheat customers and boast of 99.95% of uptime but mean results per month and not per year.

Another important point is cumulative accessibility. It is equal to the lowest indicator reflected in SLA.

Pros of SLA

Signing and observance of SLA pays off for both sides. Using SLA a company can protect itself from unexpected customer demands (like fixing a not critical problem at 3 AM) and strictly describe its own responsibilities.

There are other advantages of SLA. Providers can settle and put in order not only external processes but also inner ones. For example, with correctly composed SLA a company can implement different layers of technical support and control it in a more efficient manner.

At the same time, customers that sign an agreement will clearly understand what kind of service will be provided and how they can communicate with the company.

The difference between SLA and SLO

SLA can be used as an indication of user-satisfaction level. The highest level is 100% and the lowest is 0%.

Of course, it is impossible to achieve 100% as it is impossible to provide 100% uptime and reflect it in the company's SLA. That's why it is important to choose metrics wisely and be realistic enough about the numbers used in SLA.

If you don't have a team that is ready to work at night, don't promise your customers technical support that is available 24/7. Remember that it is possible to change SLA anytime in future when the team grows and it will be viable for the company to provide a more advanced level of support. Customers will be very happy about that.

There is another system that is used inside companies to monitor the service level. This one is called SLO. O stands for "objectives". It means that the metric is oriented at future company goals. This metric reflects what level of service the company wants to achieve in future.

Here we go again, examples based on tech support. Let's say, at the moment a company can process about 50 requests and work 5 days a week from 9 AM to 6 PM. This data should be fixed and described in SLA so the customers can see it.

At the same time a company creates a second document (service level objectives). It is a foundation of future service improvements. SLO contains current metrics and a list of tasks that should be done so the company achieves a new level of quality growth. For example, the aim to raise the amount of processed user requests from 50 to 75 during the day. The future of SLA strongly depends on a current SLO.

How to create SLA

Starting the process of SLA compiling you'd better begin with the describing part. Usually this part of SLA contains a kind of glossary, abstract system description, roles of users and tech support team, etc. In the same part you can reflect boundaries: territory where service is provided, time, functionality.

The next section — service description (what functions, features and goods a user can get by working with a certain company). In this part of SLA a company must describe in detail what the user can count on after signing the contract and on what terms.

After finishing the first part you can narrow and make further details more specific. That's the main part where the actual level of service is explained minutely. Here you would write about:

  • Metrics that reflect the quality of service provided (and they must be easy to measure).

  • The definition of every metric. That should be concrete numbers and not abstract statements so both sides can refer to this part of SLA.

It is common to put additional useful links (where another set of conditions explained in detail) in the last part of SLA.

In all the stages of preparing an SLA a company must remember that it is a regulation document that helps to control everything connected with the service. The more control a company has over all the processes the better. If SLA doesn't give a company some level of control, there's no reason for such a document to exist.

Checklist: what you should consider while compiling SLA

If you are not signing the SLA but creating your own and composing it to offer the potential clients, keep these things in mind:

  1. Customers. In large systems it is recommended to divide users into separate groups and communicate with every of them individually. This approach helps to distribute resources more effectively and do the job more effectively even in the moments of high loading.

  2. Services. At this stage it is important to consider what group of customers need certain types of services. For example, your company might offer access to a CRM system for every e-commerce business. If they can't access it their business will fail and the clients will start to lose money. And consequently it will lead them to the service provider who failed them. That's why such services get the highest importance rating and must be prioritized over some simple tasks like changing the printer or creating a new account.

  3. Parameters of service quality. These parameters should be connected with the business targets your company follows and the desires of the users. For example, time and conditions at which any service is provided. One company may want to work 24/7 and the other only offers access to a tech support team 5 days a week from 9 AM to 9 PM.

    Any changes to SLA should be explained to every user (regardless of his status or level of privilege) before the actual changes come into force.

    SLA is an ever-changing technology. In real use cases you will see that some parameters or aims do not correlate well with the general direction the business is taking. And that's why the management team often decides to correct SLA and optimize it.

    Remember, SLA is not a marketing tool, it is a way for the company to talk to its users in the clearest, most efficient way. Everyone accepts the rules in SLA.

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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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