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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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In every case, the user interface includes a dialogue window, a message input field, file attachment buttons, and a panel with active sessions. To access the model, you must either register with DeepSeek using an email address or log in through a Google account. After that, a familiar chatbot page opens, where you can converse with the model and manage active sessions, just like with other LLMs such as ChatGPT, Gemini, Claude, etc. Option 2. Local Application A more advanced way is to install DeepSeek on a local machine. This is possible thanks to its open-source code, unlike many other LLM services. DeepSeek can run on Windows, macOS, and Linux. Minimum requirements: 8 GB of RAM and 10 GB of free disk space, plus Python 3.8 or higher. When running locally, there are several interaction methods: Method 1. Web interface.  A graphical UI that allows querying, viewing logs, connecting external storage, monitoring metrics, analyzing performance, and more. The local interface differs from the public one by offering advanced model management tools. It is primarily intended for internal use by individual users or companies and contains parameters that only specialists would understand. Method 2. Console terminal. Method 3. REST API. A full REST interface for sending HTTP requests to the locally installed model. Example with curl: curl -X GET 'http://localhost:8080/api/search?index=my_index&query=search' \   -H "Authorization: Bearer UNIQUE_TOKEN" This universal method does not depend on the client type, whether a console terminal or a complex C++ program. Method 4. Python script. DeepSeek provides a wrapper fully compatible with the OpenAI API, allowing use of the standard OpenAI client with only a URL change. Example: from openai import OpenAI client = OpenAI(api_key="UNIQUE_TOKEN", base_url="http://localhost:8080") response = client.chat.completions.create( model="deepseek-chat", messages=[ {"role": "system", "content": "You are a helpful assistant, DeepSeek."}, {"role": "user", "content": "Hello!"}, ], stream=False ) print(response.choices[0].message.content) Method 5. JavaScript script. Similarly, you can interact with DeepSeek using the OpenAI client in JavaScript. Example (Node.js): import OpenAI from "openai"; const openai = new OpenAI({ baseURL: 'http://localhost:8080', apiKey: 'UNIQUE_TOKEN' }); async function main() { const completion = await openai.chat.completions.create({ messages: [{ role: "system", content: "You are a helpful assistant." }], model: "deepseek-chat", }); console.log(completion.choices[0].message.content); } main(); Notably, it is precisely the open-source nature that made DeepSeek popular and competitive in the LLM market. However, the local version is intended for advanced users with deep ML knowledge and specific tasks requiring local deployment. Detailed information on local installation is available in the official DeepSeek GitHub repository and the HuggingFace page. Specialized DeepSeek Models In addition to the core model, several specialized versions exist: DeepSeek Coder. For working with code (analysis and editing) in multiple programming languages. Available on GitHub. DeepSeek Math. For solving and explaining complex mathematical problems, performing symbolic computations, and constructing formal proofs. Available on GitHub. DeepSeek Prover. For automated theorem proving. Available on HuggingFace. DeepSeek VL. A multimodal model for analyzing and generating both text and images. Available on GitHub. DeepSeek Pricing Plans The DeepSeek service provides completely free access to its core models (DeepSeek-V and DeepSeek-R) through the website and mobile app. At present, there are no limits on the number of queries in the free version. The only paid feature in DeepSeek is the API, intended for application developers. In other words, if someone wants to integrate DeepSeek into their own app, they must pay for API usage, which processes the requests. Payment in DeepSeek follows a pay-as-you-go model with no monthly subscriptions. This means that the user only pays for the actual API usage, measured in tokens. There are no minimum payments. The user simply tops up their balance and spends it as queries are made. The balance does not expire over time. You can find more details on API pricing in the official DeepSeek documentation.   DeepSeek-V DeepSeek-R 1 million tokens (input) $0.27 $0.55 1 million tokens (output) $1.10 $2.19 To control expenses, manage API tokens, and view usage statistics, DeepSeek has DeepSeek Platform. It also provides links to documentation and reference materials that describe the basics of using the model, integrating with external applications, and pricing specifics. Prompts for DeepSeek: How to Give Commands and Get Results Although prompts for DeepSeek can vary, there are several general principles to follow when writing them. Clarity and Specificity It’s important to clearly describe both the details of the request and the desired format of the answer. Avoid vague wording, and provide context if needed. For example, you can specify the target audience and the approximate output format: I’m preparing a school report on history. I need a list of the 5 most important discoveries of the early 20th century, with a short explanation of each in the format of a headline plus a few paragraphs of text. For such queries, you can use Search mode. In this case, DeepSeek will reinforce the response with information from external sources and perform better fact-checking. In some cases, you can describe the format of the response in more detail: I need a list of the 15 most important discoveries of the early 20th century in the form of a table with the following columns: Name of the discovery (column name: “Name”) Authors of the discovery (column name: “Authors”) Date of the discovery (column name: “Date”) Short description of the discovery (column name: “Description”) Hyperlinks to supporting publications (column name: “Sources”, data in the format [1], [2], [3], ... with clickable links, but no more than 5 sources) The table rows must be sorted by date in descending order. The more detail you provide, the better. When writing prompts for DeepSeek, it’s worth taking time to carefully consider what you need and in what format. You can also use text descriptions to set filters: date ranges, geography, language of sources, readability level, and many other parameters. For example: I need a table of the 15 most important discoveries of the early 20th century that were made in the UK between 1910 and 1980. The table rows must be sorted by date in descending order, and the columns should be: Name (column: “Name”) Authors (column: “Authors”) Date (column: “Date”) As you can see, filtering in DeepSeek is done through natural language text rather than the sliders or filters familiar from internet catalogs or UGC platforms. Clear Formalization In addition to detailed text descriptions, you can formalize requests with a structured format, including special symbols: [Task]: Create a table of the 10 most important discoveries of the early 20th century.   [Constraints]:   - Territory: United Kingdom   - Period: 1910–1980   [Structure]:   - Columns: number, name, author, date (day, month, year)   [Context]: For history students specializing in British history.   This creates a clear request structure: Task. What needs to be done. Context. Where to search and for whom. Constraints. What to include or exclude. You can, of course, customize the structure depending on the task. Advanced Techniques LLM-based neural networks are extremely flexible. They support more complex dialogue patterns and information-processing methods. To get more relevant answers, you can use advanced prompting techniques, often mirroring real human dialogue. Option 1. Role-based prompts Explicitly asking the model to take on a role with specific qualities can add depth and define the style of the answer. Imagine you are an expert in English history with more than 30 years of experience studying the nuances of the UK’s scientific context. In your opinion, what 10 discoveries in the UK can be considered the most important of the 20th century? Please provide a brief description of each, just a couple of words. This style of prompt works best with DeepThink mode, which helps the model immerse itself more deeply in the role and context. Option 2. Query chains In most cases, obtaining a comprehensive response requires multiple queries—initial exploratory prompts followed by more specific ones. For example: First, a clarifying question: What sources exist on scientific discoveries in the UK during the 20th century? Then, the main request: Based on these sources, prepare a concise description of 5 scientific discoveries. Format: title + a couple of explanatory paragraphs. The best results often come from combining DeepThink and Search modes. DeepSeek will both gather external information and process it in depth to synthesize a thorough answer. DeepSeek vs. Other AI Models: Comparison and Conclusions Unique Features of DeepSeek Free access. The two main models (one for simpler tasks, one for complex tasks) are available completely free of charge. Only the developer API is paid, and the pricing is usage-based, not subscription-based. No limits. All models are not only free but also unlimited, i.e., users can generate as much content as they want. While generation speed may not be the fastest, unlimited free use outweighs most drawbacks. Open source. Industry experts, AI enthusiasts, and ordinary users can access DeepSeek’s source code on GitHub and HuggingFace. Global availability. The DeepSeek website is accessible in most countries. Comparison with Other LLM Services Platform Generation Speed Free Access Pricing Model Content Types Developer Country Launch Year DeepSeek High Full Pay-as-you-go Text High-Flyer China 2025 ChatGPT High Limited Subscription Text, images OpenAI USA 2022 Gemini High Limited Subscription Text, images, video Google USA 2023 Claude Medium Limited Subscription Text Anthropic USA 2023 Grok Medium Limited Subscription Text, images xAI USA 2023 Meta AI Medium Limited Subscription / Usage Text, images Meta (banned in RF) USA 2023 Qwen Medium Full Pay-as-you-go Text Alibaba China 2024 Mistral High Limited Subscription Text Mistral AI France 2023 Reka High Full Pay-as-you-go Text Reka AI USA 2024 ChatGLM Medium Limited Pay-as-you-go Text Zhipu AI China 2023 Conclusion On one hand, DeepSeek is a fully free service, available without volume or geographic restrictions. On the other hand, it is a powerful and fast model, on par with many industry leaders. The real standout, however, is its open-source code. Anyone can download it from the official repository and run it locally. These features distinguish DeepSeek from competitors, making it not only attractive for content generation but also highly appealing for third-party developers seeking integration into their own applications. That’s why when ChatGPT or Gemini fall short, it’s worth trying DeepSeek. It just might find the right answers faster and more accurately.
17 September 2025 · 15 min to read
Infrastructure

Best Midjourney Alternatives in 2025

Midjourney is one of the most popular AI networks for image generation. The service has established itself as a leader in the field of generative AI. However, the existence of a paid subscription and access limitations (for example, the requirement to use Discord or lack of support in certain regions) increasingly prompts users to consider alternatives. We have compiled the best services that can replace Midjourney,  from simple tools to professional solutions. Why Are Users Looking for a Midjourney Alternative? Midjourney is a powerful tool, but it has its drawbacks: Paid Access: Since March 2023, Midjourney has fully switched to a paid model, with a minimum subscription of $10 per month, which may be expensive for beginner users. Usage Limitations: A Discord account is required, and for users in some countries, access is restricted due to regional limitations. Complex Interface: Beginners may find it difficult to navigate working through the Discord bot. Fortunately, there are many apps like Midjourney that offer similar functionality and more user-friendly interfaces. We will review seven of the best Midjourney alternatives. For all the AI networks considered, we will generate an image using the following prompt: “Generate an image of the Swiss Alps.” Free Alternatives First, let’s look at Midjourney alternatives that can be used for free. Playground AI Playground AI is an AI network that works on modern generative models, including Stable Diffusion XL, and allows generating images from text prompts or editing existing images. A unique feature of Playground AI is the ability not only to generate an image from scratch but also to refine it within the same interface. Users can correct individual details, replace elements (for example, hands), perform upscaling to increase detail, or draw additional parts of the image on a special working field (canvas) with a seamless continuation of the image. Using the free plan, users can generate up to 5 images every 3 hours. Advantages: Work with a library of ready-made images and prompts, and the ability to copy and refine other users’ creations. Built-in canvas tool for extending and editing images while maintaining stylistic consistency. Support for multiple models. Image generated by Playground AI using the prompt “Generate an image of the Swiss Alps” Bing Image Creator Bing Image Creator is an image generation tool from Microsoft, based on the latest version of OpenAI’s DALL·E model. The service works using a diffusion architecture: the AI network analyzes the text prompt and synthesizes a unique image considering specified styles, details, emotions, backgrounds, and objects. Users can describe the desired image in any language, and the AI interprets the prompt to generate multiple options for selection. Advantages: Completely free. Multiple image generation models to choose from. Integration with Microsoft ecosystem: Microsoft Copilot, Bing, Bing Chat, Microsoft Edge. Built-in content filtering and internal security algorithms to prevent illegal or inappropriate image generation. Image generated by Bing Image Creator using the prompt “Generate an image of the Swiss Alps” Paid Alternatives Among the paid Midjourney alternatives, the following stand out. Leonardo AI Leonardo AI functions as a cloud platform for AI-based image generation. Its main function is creating high-quality visual materials from text descriptions. Leonardo AI uses modern image generation algorithms similar to diffusion models, with additional innovative tools to improve quality and flexibility. Users can select from multiple artistic styles and genres, and also use the Image2Image feature to upload a reference image for more precise control. Users can adjust the “weight” of the generated image to balance between strict adherence to the reference and creative interpretation of the text. Advantages: Free access with a limit (up to 150 tokens per day). Ability to train custom AI models. Wide choice of styles and customization tools. Support for generating textures and 3D objects. Convenient prompt handling: a built-in prompt generator helps beginners formulate queries, while experienced users can optimize prompts for better results. Image generated by Leonardo AI using the prompt “Generate an image of the Swiss Alps” Stable Diffusion Stable Diffusion is a modern text-to-image generation model that uses diffusion model technology. Developed by Stability AI in collaboration with researchers from LMU Munich and other organizations, the model was released in 2022 and quickly gained popularity due to its openness and high efficiency. Stable Diffusion can be accessed through many services, including DreamStudio, Stable Diffusion Online, Tensor.Art, and InvokeAI. Advantages: Multiple interfaces available. Flexible settings (Negative Prompt, aspect ratio, generation steps, fine-tuning, service integration, inpainting for parts of an image, outpainting for backgrounds). Numerous custom models (anime, realism, fantasy). Possibility of local deployment on powerful PCs. Open-source code. Unlike many proprietary models (DALL-E, Midjourney), Stable Diffusion can be run, trained, and modified locally. Image generated by Stable Diffusion using the prompt “Generate an image of the Swiss Alps” NightCafe NightCafe is an online platform for generating images from text prompts and images. It uses multiple advanced algorithms and generation models, such as VQGAN+CLIP, DALL·E 2, Stable Diffusion, Neural Style Transfer, and Clip-Guided Diffusion. Users input a text prompt or upload an image, and the AI transforms it into a unique artistic work. Various styles, effects, resolution and detail settings, as well as editing and upscaling options, are available. Advantages: Numerous options for customizing generated images, suitable for digital art, NFTs, and other purposes. Built-in functionality for modifying existing images via text prompts, scaling without quality loss, and object removal. Free access with limited generations. Support for multiple styles and algorithms. User-friendly interface. Image generated by NightCafe using the prompt “Generate an image of the Swiss Alps” Artbreeder Artbreeder operates using generative adversarial networks (GANs). The main principle is creating new images by “crossing” or blending two or more images (“parents”), with fine control over parameters (“genes”) that determine various image traits. Users can interactively control the resulting image with sliders, adjusting characteristics like age, facial expression, body type, hair color, level of detail, and other visual elements. Advantages: Interactive blending allows combining different images to create unique compositions, such as portraits, landscapes, or anime styles. Detailed manual adjustments of each image parameter (brightness, contrast, facial features, accessories, etc.) allow for highly refined results. Image generated by Artbreeder using the prompt “Generate an image of the Swiss Alps” Ideogram  Ideogram is a generative AI model specialized in creating images containing text. It uses advanced deep learning and diffusion algorithms. Unlike many other AI visualization tools, Ideogram can generate clear, readable text within images, making it especially useful for designing logos, posters, advertisements, and other tasks where combining graphics and text is important. Advantages: Free generations with selectable styles. Support for integrating readable and harmonious text into images—convenient for designers, marketing teams, and social media specialists. Built-in social platform with user profiles, sharing capabilities, and community interaction. Image generated by Ideogram using the prompt “Generate an image of the Swiss Alps” Conclusion The choice of a Midjourney alternative depends on your goals and preferences: if you need the highest-quality image generation, consider Ideogram or Stable Diffusion 3. For free solutions, Leonardo AI and Playground AI are suitable, and if speed and simplicity are priorities, Bing Image Creator from Microsoft is a good option. Each service has its own advantages, whether it is accessibility, detail quality, or flexibility of settings. It’s worth trying several options to find the best tool for your needs.
11 September 2025 · 7 min to read

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