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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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For Working with Texts Purpose: Creating and optimizing commercial or informational materials. Fields: copywriting, SEO, technical documentation, scripts. Template: As a [role], write a [type of text] for [target audience]. Parameters: length → style → required elements → restrictions → SEO. Structure: [sections]. Example: As a technical writer, create a guide titled “Setting Up WordPress on Hostman” (1,500 words). Avoid jargon, include GIF instructions. ChatGPT response: Tips: Clearly define the text’s purpose: 🔴 “Write a text about clouds.” 🟢 “Write a commercial proposal for Hostman aimed at small businesses. Goal: conversion into demo requests.” Set style and tone: “Tone: friendly yet professional, as if explaining to a colleague.” “Avoid bureaucratic phrases; write naturally.” Add SEO parameters if needed: “Include keywords: ‘cloud hosting,’ ‘VPS for business,’ ‘reliable hosting.’ Keep keyword density natural.” “Add LSI words: ‘scalability,’ ‘data security,’ ‘uptime.’” Request examples and comparisons: “Provide 3 strong headline examples for this kind of article.” “Compare with competitor texts: what can be improved?” Limit length and complexity: “Max 1,000 words, divided into H2-H3 subheadings.” “Explain terms like ‘CDN’ in parentheses in simple words.” 6. For Programmers Purpose: Code generation and analysis with full documentation. Main uses: writing scripts, debugging, creating APIs, automating DevOps processes. Template: As a [language] developer of [X] level, write code for [task]. Input → expected output → constraints → requirements. Format: algorithm → code → tests. Example: As a senior Python developer, create a server monitoring script for Hostman with API integration and Telegram notifications. Requirements: async, logging. ChatGPT response: Tips: Specify exact versions: 🔴 “Write a backup script.” 🟢 “Write a Python (3.10+) script for daily MySQL (8.0) backups to Hostman S3. Requirements: async, file logging, Telegram error alerts.” Request explanations: “Add comments every 5 lines to clarify complex code sections.” “Explain why you chose this algorithm (e.g., QuickSort vs. MergeSort).” Require tests: “Add 3 unit tests with edge cases.” “How to test this API in Postman?” Ask for alternatives: “Show alternative solutions in Go and Rust. Compare performance.” Set constraints: “No external libraries.” “Execution time ≤100ms for 10K records.” 7. For Image Creation (DALL·E) Purpose: Precise technical specifications for neural image generation (DALL·E). Applications: ad banners, article illustrations, concept art, presentations. Template: As an art director, create a prompt: 1) Object → 2) Style → 3) Composition → 4) Color palette → 5) Lighting → 6) Restrictions. Goal: [usage]. Example: Create a prompt for a “Hostman Enterprise” banner: a cyberpunk-style server, palette #0A1640/#00C1FF, HUD elements, no people. ChatGPT response: Image generated by ChatGPT: Tips: Be extremely specific: 🔴 “Draw a cloud server.” 🟢 “Generate a 3D render of a Hostman server in blue-white tones. Style: cyberpunk with neon accents. Background: network map with nodes. Aspect ratio 16:9, no people.” Reference known styles: “In the style of the interfaces from the Foundation series.” “Like Wired magazine covers from the 2020s.” Control composition: “Main object centered, occupying 70% of the frame.” “Blurred background with depth-of-field effect.” Request variations: “Show 3 versions: minimalism, retro-futurism, and photorealism.” “Change only the palette to dark/light mode.” Technical constraints: “No text in the image.” “Resolution: 1024×1024, format: PNG.” 8. For Learning and Education Purpose: Designing educational programs using modern methodologies. Application: course creation, training materials, workshops, interactive modules. Template: As a professor of [subject], design a [number]-hour module. Include: goals → plan (theory/practice) → adaptations → glossary. Constraints: [parameters]. Example: Develop an 8-hour course “Cloud Fundamentals” for university students: lectures in Prezi, labs on Hostman, quizzes in Kahoot. ChatGPT response: Tips: Base on teaching models: 🔴 “Create a Python course.” 🟢 “Using the ADDIE model (Analysis, Design, Development, Implementation, Evaluation), create a 4-week course ‘Python for Data Analysis.’ Goal: teach students to visualize data using Matplotlib.” Define difficulty level: “For junior DevOps: basics of Kubernetes.” “For senior developers: algorithm optimization in C++.” Add interactive elements: “Include 3 simulated real-world cloud development cases.” “Propose a gamification format for a cybersecurity module.” Require practical tasks: “Design a lab exercise: deploying a web app on Hostman.” “Create a test assignment with automatic checking via GitHub Actions.” Consider technical limitations: “Course must run on low-end PCs (no Docker).” “Use only free tools (VS Code, Colab).” 9. For Business Purpose: Strategic market and process analysis. Applications: business planning, SWOT analysis, competitor research, financial modeling. Template: As a consultant from [company], conduct an analysis of [object] using the following framework:  1) Market Size → 2) PESTEL → 3) Benchmarking → 4) SWOT → 5) Forecasts. Data sources: [list of references]. Example: Analyze the European cloud gaming market: 2024 market size, PESTEL factors, comparison of NVIDIA GeForce Now / Shadow PC / Boosteroid, and projections through 2026. ChatGPT response: Tips: Be specific with goals: 🔴 “How to increase profits?” 🟢 “Develop 3 revenue growth strategies to increase a SaaS startup’s revenue by 30% within 6 months. Focus: upselling existing clients and reducing churn rate. Use the AARRR framework.” Ask for supporting data: “Analyze the European cloud services market (size, trends, competitors). Use sources such as Statista, Gartner, and official company reports.” “Calculate CAC for our current ad campaign.” Request alternative approaches: “What are the best options for entering the EU market: partnerships vs. independent launch?” “Compare investment risks for expanding VPS services versus cloud storage solutions.” Link to business processes: “How can the new product be integrated into our existing SaaS ecosystem?” Consider resource limitations: “Budget: up to €50,000, team of 5 people.” “Propose solutions that don’t require hiring additional staff.” 10. For Creativity Purpose: To generate compelling stories and concepts. Ideal for: Writers: for books and short stories; Screenwriters: for films and series; Game developers: for characters and worldbuilding; Musicians: for album or concept creation. Template: As a [profession], create a [type of work] in the [genre] style. Parameters: Characters → Setting → Conflict → Style. Format: Logline → Synopsis → Scene breakdown. Example: As a Black Mirror-style screenwriter, develop a concept for an episode about AI in 2045, exploring the theme “Privacy vs Convenience.” ChatGPT response: Tips: Be specific about genre and audience: 🔴 “Write a story about a scientist.” 🟢 “Write the first chapter of a science fiction story about a bioengineer who discovers how to edit DNA using quantum computers. Style: mix of Black Mirror and The Martian. Audience: hard sci-fi fans (ages 25–45), with emphasis on scientific realism.” Request structure: “Outline the plot using Joseph Campbell’s ‘Hero’s Journey’ model.” “Create a dialogue example with subtext (in the style of Aaron Sorkin).” Ask for visualization: “Describe a key cinematic shot for a poster in the style of Blade Runner.” “Which color palette best conveys the atmosphere?” Avoid clichés: “Exclude tropes like ‘the chosen one’ or ‘evil AI.’” “Suggest three unexpected plot twists.” Consider technical constraints: “Script for a 10-minute short film (maximum 5 locations).” “Concept for a mobile game with simple gameplay.” Combined Prompt Example The prompt templates presented above cover most professional user tasks. For maximum efficiency, you can combine them, for example: analysis (section 1) + text generation (section 5) + visualization (section 7). Example prompt Act as both an IT analyst and a digital marketer. I need a comprehensive comparison of cloud hosting platforms (AWS, Google Cloud, and Hostman) with materials ready for publication. Perform the following tasks sequentially: 1. Conduct a detailed analysis: Compare by: cost per vCPU, SSD size, network bandwidth, SLA uptime. Present results in a table with columns: “Feature,” “AWS,” “Google Cloud,” “Hostman.” Conclude with a recommendation for a startup with a $50/month budget. 2. Write an SEO article based on the analysis: Title: “AWS vs Google Cloud vs Hostman: An Objective Comparison for 2025.” Length: 2,000 words. Structure: Introduction (importance of choosing the right provider); Methodology; In-depth review of each provider; Summary table (from step 1); Recommendations for different use cases. Tone: Expert but accessible; Keywords: “cloud hosting,” “VPS comparison,” “Hostman review.” 3. Create visualization prompts (for DALL·E or Midjourney): Style: Corporate infographic (blue and white color palette). Elements: 3D servers with provider logos; Comparative performance and pricing charts; “Price/Performance” scale; Minimalist background with digital accents. Formats: Article cover, comparative infographic, architecture diagram. 4. Additional tasks: Suggest 3 social media posts based on the article. Format: “Did you know that…” + key takeaway + infographic. Platforms: LinkedIn, Reddit. Ensure all data is consistent across text and visuals. Numbers in the text must match tables and charts. Use professional terminology, but explain complex terms for beginners. ChatGPT response: This prompt structure provides: A unified request instead of multiple separate ones; Logical flow: analysis → writing → visuals → promotion; Consistent data across all materials; Publication-ready results. For even higher precision, you can add: “Before starting, ask 3 clarifying questions to better define the task.” This approach helps the AI better understand the project and deliver higher-quality results. Key Takeaways In this article, we explored what prompts are and how to craft them effectively, showcasing 10 universal examples across different categories. A prompt is a text instruction you send to ChatGPT to get a desired response. The clearer and more detailed the prompt, the more accurate and useful the result. Core principles of effective prompting: Clarity and detail (including timeframes, parameters, and constraints); Specify the response format (table, list, step-by-step guide); Add context (AI role, complexity level, target audience); Include examples and analogies for clarity; Note technical requirements (length, tone, restricted elements). Common mistakes: Overly vague prompts (“Write something”); No structure or logic; Ignoring context (missing role or audience); Overcomplicating with conflicting details; Poor clarification (missing data or specific names). Improvement tips: Start broad, then refine details step by step; Save successful prompts as templates; Request data sources for analytical tasks; Use iterations: “Add to the previous answer…” Additional recommendations: For creative work, include stylistic references; For technical tasks, specify software versions or languages; For business analysis, ask for alternative scenarios; Always verify critical data. ChatGPT is a tool, not a substitute for expertise. Save the templates from this guide as a quick-reference list and adapt them over time to fit your workflow. By mastering the art of crafting effective prompts, you’ll unlock ChatGPT’s full potential, transforming it into a personal assistant for work, creativity, and learning. Experiment with phrasing, analyze results, and refine your prompts: that’s how you’ll make AI a truly powerful tool in your toolkit.
31 October 2025 · 20 min to read
Infrastructure

Top ChatGPT Alternatives and How to Choose the Right One

OpenAI’s developments are undoubtedly among the best in the generative neural network market. This applies not only to ChatGPT, which generates text, but also to DALL-E, which generates images, and Sora, which generates video. However, there are many other equally effective ChatGPT alternatives, including free ones. This article focuses on them. How to Choose a ChatGPT Alternative It is worth highlighting several general parameters that allow you to clearly see the differences between existing large language model (LLM) platforms: In-depth reasoning: Support for a "Reasoning" or "Deep Thinking" feature, which improves answer accuracy. Interactive interaction: Support for a "Canvas" mode that makes working with content more interactive. Image analysis: Ability to analyze image files. Video analysis: Ability to analyze video files or links. Audio analysis: Ability to analyze audio files with speech or music. Document analysis: Ability to analyze documents in various formats, such as PDF or DOCX. Image generation: Ability to generate images, either using an internal or external model. Video generation: Ability to generate video, usually requiring a separate model. Audio generation: Ability to generate audio, in the form of speech or music. For example, for ChatGPT, depending on the subscription plan, these parameters look as follows: Feature Free Plan Paid Plans In-depth reasoning Yes Yes Interactive interaction Yes Yes Image analysis Yes Yes Video analysis No No Audio analysis No No Document analysis Yes Yes Image generation Yes Yes Video generation No Yes Audio generation Yes Yes Thus, any ChatGPT alternative can be evaluated through the lens of these parameters. 1. Gemini Gemini is a neural network created by Google in 2023. Platform: Gemini Models: Gemini Flash, Imagen, Veo Release: March 21, 2023 Developer: Google DeepMind Country: USA Capabilities The Gemini Flash language model is integrated with two other Google models: Imagen for image generation and Veo for video generation. This allows users to create images and videos directly within the Gemini chat; the results appear in the dialog window, similar to text. Additionally, Gemini is tightly connected with Google’s ecosystem, including browser and mobile applications like Gmail, Google Docs, Google Lens, and more. The experimental Canvas feature enables more interactive model interaction: editing responses, changing tone and length, refining details, and executing code. Feature Free Plan Paid Plans In-depth reasoning Yes Yes Interactive interaction Yes Yes Image analysis Yes Yes Video analysis Yes Yes Audio analysis Yes Yes Document analysis Yes Yes Image generation Yes Yes Video generation No Yes Audio generation No No Pricing Gemini Basic: Free. Provides access to basic Gemini models without deep Google ecosystem integration. Sufficient for most standard tasks. A decent free alternative to ChatGPT. Gemini Advanced: From $20/month. Provides access to the most powerful Gemini models (including experimental ones) with an extended context window for processing large volumes of information—up to 1 million tokens. 2. Claude Claude is a neural network created by Anthropic in 2023. Platform: Claude Models: Claude Release: March 14, 2023 Developer: Anthropic Country: USA Capabilities Claude’s abilities are standard for most platforms using large generative models and it can be considered as one of the best ChatGPT alternatives. However, all of Claude’s functionality is only available via a paid subscription. Unlike other platforms, it is nearly impossible to use Claude effectively for free due to numerous limitations. Feature Free Plan Paid Plans In-depth reasoning No Yes Interactive interaction No Yes Image analysis Yes Yes Video analysis No No Audio analysis No No Document analysis Yes Yes Image generation No No Video generation No No Audio generation No No Pricing Free: Limited token count, enough for 5–10 queries per day. Reduced limits, no external search, no reasoning mode, no integration with external tools. Pro: From $15/month. Increased limits, unlimited projects, external search, advanced reasoning, Google Workspace integration, and access to more Claude models. Max: From $90/month. Increased limits (up to 20x Pro), enhanced external search, access to the Claude Code agent tool, reasoning mode, early access to new features, priority request processing, and external tool integration. 3. Grok Grok is a neural network created by xAI in 2023. Platform: Grok Models: Grok, Aurora Release: November 3, 2023 Developer: xAI Country: USA Capabilities  In addition to the standard query mode, Grok offers specialized modes for specific tasks: Think: Grok spends a few seconds to minutes analyzing a query and provides a precise answer. Ideal for math, philosophy, strategy, coding, and architecture tasks. Relies solely on internal model knowledge. DeepSearch: Uses intelligent agents to search external sources for current information. Suitable for fast-changing topics like news, trends, publications, and events. DeeperSearch: An advanced version of DeepSearch, spending more time analyzing fewer sources but collecting information more thoroughly. Ideal for very narrow queries but may miss key details or focus on irrelevant sources. Grok is deeply integrated with the X platform (formerly Twitter), acting as an AI assistant and enhancing platform functionality: Grok is embedded in X’s interface: users can ask questions, analyze posts, and generate content. Grok analyzes public posts in real-time to provide up-to-date information on news, trends, and public opinion. Grok is trained on X data using xAI’s Colossus supercomputer. The Aurora model integrated into Grok allows generating photorealistic images directly within the chat. Grok also works without authorization, though dialogues are not saved in history in that mode. Feature Free Plan Paid Plans In-depth reasoning Yes Yes Interactive interaction Yes Yes Image analysis Yes Yes Video analysis No No Audio analysis No No Document analysis Yes Yes Image generation Yes Yes Video generation No No Audio generation No No Pricing Grok Basic: Free. Limited queries and images every 2 hours (exact numbers not disclosed), limited access to Thinking, DeepSearch, and DeeperSearch modes, and a limited context window. SuperGrok: From $30/month. 100 queries and images every 2 hours, 30 queries for Thinking, DeepSearch, and DeeperSearch each every 2 hours, extended context window. 4. Qwen Qwen is a neural network created by Alibaba in 2023. Platform: Qwen Models: Qwen Release: August 25, 2023 Developer: Alibaba Country: China Capabilities The Qwen‑Turbo model available on paid plans features a record-long context—up to 1,000,000 tokens. All Qwen models are multimodal, capable of processing text, images, video, and audio as input and output. Qwen’s main strength is its ability to work with a wide variety of multimedia content. Feature Free Plan Paid Plans In-depth reasoning Yes Yes Interactive interaction Yes Yes Image analysis Yes Yes Video analysis Yes Yes Audio analysis Yes Yes Document analysis Yes Yes Image generation Yes Yes Video generation Yes Yes Audio generation Yes Yes Pricing Qwen Basic: Free trial access, 1 million tokens per basic model for 180 days. Qwen Max / Plus / Turbo: Pay-as-you-go via Alibaba Cloud Model Studio. Three models differ in maximum context, quality, and generation speed. Model Context Quality Speed Input Cost Output Cost Qwen-Max 30,000 tokens High Slow $1.6/million tokens $6.4/million tokens Qwen-Plus 130,000 tokens Medium Medium $0.4/million tokens $1.2/million tokens Qwen-Turbo 1,000,000 tokens Low Fast $0.05/million tokens $0.2/million tokens 5. Mistral Mistral is a neural network created by Mistral AI in 2023. Platform: Le Chat Models: Mistral, Flux Release: September 27, 2023 Developer: Mistral AI Country: France Capabilities The first thing that stands out is how fast Mistral generates responses. No other model matches this speed. In this aspect, you could say that Mistral is better than ChatGPT. Additionally, the smooth animation of messages appearing in the chat window provides a genuinely pleasant user experience. Despite the high speed, Mistral’s responses are accurate and relevant, containing only key information without unnecessary filler. Mistral does not allow manually enabling a deep reasoning mode with access to external sources. Instead, the neural network automatically gathers information from the Internet when it deems necessary. In this sense, Mistral works “out of the box”—no additional settings are required. The user writes a query and receives a response almost instantly. For image generation, Mistral uses the Flux model from a third-party developer, Black Forest Labs. Feature Free Plan Paid Plans In-depth reasoning No No Interactive interaction Yes Yes Image analysis Yes Yes Video analysis No No Audio analysis No No Document analysis Yes Yes Image generation Yes Yes Video generation No No Audio generation No No Pricing Free: Access to the latest advanced Mistral models, data collection from external sources, file upload, advanced data analysis, image generation, and fast responses. Pro: From $14/month. Unlimited high-performance Mistral model, unlimited daily messages, advanced external data collection, advanced image generation, and extended fast response limits. Team: From $24/month. Advanced generation and data collection capabilities, centralized management and administration, and a dedicated support channel from Mistral AI. 6. DeepSeek DeepSeek is a neural network created by High-Flyer in 2023. Platform: DeepSeek Models: DeepSeek Release: November 2, 2023 Developer: High-Flyer Country: China Capabilities DeepSeek provides unlimited functionality completely free of charge, reserving the right to charge only for API usage. However, DeepSeek lacks extensive multimodal capabilities: it does not generate images, video, or audio, though it can analyze images and documents. It also does not have a Canvas-like tool for interactive work with responses (and code), common in many LLM platforms. Nevertheless, DeepSeek has standard reasoning (DeepThink) and search (Search) functions. Feature Free Plan Paid Plans In-depth reasoning Yes Yes Interactive interaction No No Image analysis Yes Yes Video analysis No No Audio analysis No No Document analysis Yes Yes Image generation No No Video generation No No Audio generation No No Pricing Browser Access: Free. Normal mode (deepseek-chat) has no limits; DeepThink mode (deepseek-reasoner) allows up to 50 messages per session. API Access: Pay-per-token for input and output; necessary only for API usage. Pricing varies by mode. Mode 1M Tokens Input 1M Tokens Output deepseek-chat $0.27 $1.10 deepseek-reasoner $0.55 $2.19 7. Reka Reka is a neural network created by Reka AI in 2024. Platform: Reka Models: Reka Release: April 18, 2024 Developer: Reka AI Country: USA Capabilities Reka can feel somewhat rough: it occasionally misinterprets context and incorrectly analyzes provided documents and media files. However, for text generation or open-source information retrieval, the model performs reasonably well. Reka’s chat includes integrated agents: Reka Vision Agent: Analyzes images. Reka Research Agent: Searches for information in open sources. Reka Speech Agent: Translates and transcribes audio in real time; a demo version is available. Reka’s main feature is the interactive Space, where texts and images can be placed. While most people interact with LLMs through standard chat, the interactive space adds visual clarity during text generation. Feature Free Plan Paid Plans In-depth reasoning Yes Yes Interactive interaction Yes Yes Image analysis Yes Yes Video analysis Yes Yes Audio analysis Yes Yes Document analysis No No Image generation No No Video generation No No Audio generation No No Pricing Browser Access: Free. Standard capabilities with no restrictions. API Access: Pay-per-token. Three model versions available in ascending power: Spark, Flash, and Core. Version Cost per 1M Input Tokens Cost per 1M Output Tokens Reka Spark $0.05 $0.05 Reka Flash $0.8 $2 Reka Core $2 $6 8. ChatGLM ChatGLM is a neural network created by Zhipu AI in 2023. Platform: ChatGLM Models: ChatGLM, CogView, Ying Release: March 13, 2023 Developer: Zhipu AI Country: China Capabilities In addition to image and document analysis, ChatGLM can generate images with CogView and videos with Ying. Audio transcription and analysis is handled by ChatGLM Voice. Special functions for media work are provided in dedicated chats. Otherwise, ChatGLM functions similarly to other neural networks. Feature Free Plan Paid Plans In-depth reasoning Yes Yes Interactive interaction No No Image analysis Yes Yes Video analysis No Yes Audio analysis No Yes Document analysis Yes Yes Image generation No Yes Video generation No Yes Audio generation No Yes Pricing Trial: Free. Upon initial registration, 1,000,000 tokens for 30 days; after identity verification, an additional 4,000,000 tokens for 30 days. Uses the lightweight ChatGLM Flash model. Paid: Pay-as-you-go. Full multimodal and generative capabilities, with four model versions in ascending power: Lite, Turbo, Std, and Pro. Version Cost per 1M Tokens ChatGLM Lite $0.28 ChatGLM Turbo $0.69 ChatGLM Std $0.69 ChatGLM Pro $1.39 Aggregator Platforms / Intermediaries There is a separate category of content generation platforms, acting as intermediaries or aggregators. Essentially, they are standard chatbots but rely on third-party models mentioned above. Platform Models Release Developer Country Microsoft Copilot GPT Feb 7, 2023 Microsoft USA You.com GPT Nov 9, 2021 YouChat USA Poe GPT, o, Claude, Llama, Gemini, Mistral, Qwen, DeepSeek Dec 21, 2022 Poe USA HuggingChat Llama, DeepSeek, Mistral, Qwen, C4AI Apr 25, 2023 Hugging Face USA Nova GPT, Gemini, Claude, DeepSeek Dec 3, 2024 HUBX USA Duck.ai GPT, o, Llama, Claude, Mistral Mar 10, 2025 DuckDuckGO USA This category also includes specialized external search services using intelligent agents to collect information. They also use third-party generative models, most often OpenAI GPT. Platform Models Release Developer Country Perplexity GPT Dec 7, 2022 Perplexity AI USA Andi GPT Jan 26, 2023 Andi USA Phind Llama Feb 23, 2023 Phind USA How to Choose a Platform AI benchmarks show significant differences in task performance for each model, but these reflect controlled “lab” conditions. In typical tasks, the differences are less noticeable, though they exist. Pricing structures are similar: basic functionality is free, enhanced features require payment, often on a pay-per-token basis. Some platforms are multimodal: they can generate text, images, video, and audio. Others can analyze multimedia data, but only generate text. When looking for an AI tool like ChatGPT, it makes sense to test several platforms for a given task and then select one or two. Suggested approach: Define requirements clearly. Identify key requirements based on the project and its tasks. Evaluate core platform parameters. Compare the requirements against the platform’s capabilities, especially generative features and ecosystem integration. Compare platforms. Select the most suitable platforms based on how well their characteristics align with project needs. Test selected platforms. Evaluate performance in real tasks to determine the best fit. Choose the most suitable platform. You don’t have to pick only one. Keep a couple of backups for tasks where they might outperform the main platform.
30 October 2025 · 13 min to read

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