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How to Host a ChatGPT Alternative: The 8 Best Deployment Platforms Compared

The rise of open-source large language models has made it possible to deploy your own ChatGPT-like interface without relying on proprietary APIs or sharing sensitive data with third parties. Whether you're building for privacy, cost control, or customization, choosing the right hosting platform is critical to your success.

This guide examines the top 8 hosting solutions for deploying open-source ChatGPT alternatives in 2025, focusing on technical requirements, actual costs, and deployment complexity rather than marketing promises.

Understanding the Stack: What You're Actually Deploying

Before diving into hosting platforms, it's important to understand what you're deploying. Modern open-source ChatGPT alternatives typically consist of two components:

The Chat Interface: Web-based UI applications like LibreChat, Open WebUI, or AnythingLLM. These are typically Node.js or Python applications that run in Docker containers and require 1-2 GB of RAM, 1-2 CPU cores, and 10-50 GB of storage.

The Language Model Backend: This can be either a local model runner (Ollama, llama.cpp) requiring significant resources (16+ GB RAM for 7B parameter models, GPU recommended), or API connections to external providers (OpenAI, Anthropic, local endpoints). Most cost-effective deployments connect the UI to API services rather than running models locally.

For practical deployment on budget hosting, you'll typically deploy the chat interface and connect it to either external APIs or separately-hosted model servers.

1. Code Capsules — Best for Rapid Deployment with Integrated Services

Code Capsules is an all-in-one cloud deployment platform designed specifically for developers who want to deploy applications without managing infrastructure. The platform supports Node.js, Python, Java, and Go backends, plus Angular, React, and Vue frontends.

Technical Specifications

Code Capsules uses a "capsule" architecture where each component (frontend, backend, database) deploys independently but can communicate within the same project. This modular approach allows you to deploy a chat UI backend as a Backend Capsule and connect it to a Storage Capsule for databases like MongoDB or PostgreSQL.

The platform handles builds automatically from GitHub repositories. When you push code, Code Capsules detects the runtime environment, installs dependencies, and deploys within minutes. This eliminates the need to configure build pipelines or deployment scripts.

Pricing Structure

Code Capsules offers three primary pricing tiers:

  • Frontend Capsule: Starting at $3/month, includes subdomain, SSL certificate, custom domains, GitHub integration, metrics, and logs
  • Backend Capsule: Starting at $7/month with the same features as Frontend Capsules but supporting server-side runtimes
  • Storage Capsule: Starting at $15/month for MongoDB or SQL databases with elastic storage, backups, and instant configuration binding

For a complete ChatGPT alternative deployment (backend UI + database), you're looking at approximately $22/month minimum, though exact pricing depends on resource usage.

Deployment Process for LibreChat on Code Capsules

LibreChat is a production-ready open-source ChatGPT clone built with Node.js that supports multiple AI providers including OpenAI, Anthropic, Google, and local endpoints.

Prerequisites: - GitHub repository with your LibreChat fork or clone - Code Capsules account - API keys for your chosen LLM provider

Steps:

  1. Prepare the Repository: Fork the LibreChat repository and configure your .env file with required environment variables including MONGO_URI, SESSION_SECRET, and API keys for your LLM providers.

  2. Create a Storage Capsule: In Code Capsules, create a new Storage Capsule with MongoDB. This provides your database URL which you'll use as the MONGO_URI environment variable.

  3. Create a Backend Capsule: Link your GitHub repository containing LibreChat, select Node.js as the runtime, and configure environment variables from your .env file through the Code Capsules dashboard.

  4. Configure Build Settings: LibreChat requires Node.js v20.19.0 or higher. Code Capsules should auto-detect this from your package.json, but verify the build logs to ensure correct Node version.

  5. Deploy: Code Capsules automatically builds and deploys when you create the capsule. The build process installs dependencies with npm install and starts the application with npm start.

  6. Access Your Deployment: Once deployed, Code Capsules provides a subdomain URL. You can also configure custom domains through the dashboard.

Strengths

Code Capsules excels in simplicity. The GitHub integration means every push to your repository triggers automatic rebuilds. The included SSL certificates, metrics dashboard, and log aggregation eliminate the need for separate monitoring tools.

The capsule architecture also supports preview environments. You can create separate capsules for development, staging, and production without managing multiple servers or complex CI/CD pipelines.

Limitations

The primary limitation is less granular control compared to Infrastructure-as-a-Service providers. You cannot SSH into containers or install system-level dependencies outside the supported runtimes. For applications with custom system requirements, this may be restrictive.

Resource scaling information is not prominently documented, which makes capacity planning more difficult for high-traffic applications. The platform appears optimized for small-to-medium deployments rather than enterprise scale.

2. Railway — Best for Usage-Based Pricing

Railway provides a developer-focused platform that deploys applications from GitHub repositories or Docker images. The platform distinguishes itself through usage-based pricing that charges only for actual resource consumption rather than fixed instance sizes.

Technical Specifications

Railway uses Nixpacks to automatically detect and build applications, or it can use your Dockerfile for custom builds. This flexibility means you can deploy anything from simple Express applications to complex multi-container setups.

The platform supports deployment to four global regions and includes features like ephemeral preview environments that automatically deploy for pull requests and tear down when merged. This is particularly useful for team development workflows.

Pricing Structure

Railway offers a free trial with $5 in one-time credits that expire in 30 days. The Hobby plan provides $5 of included usage per month, and you're charged for consumption beyond that allocation. Resource usage is metered by the second for compute, with storage and bandwidth charged monthly.

There are no fixed instance prices. Instead, you pay for the CPU time, RAM, and bandwidth your application actually uses. This can be more economical for applications with variable traffic, but it also means costs can fluctuate significantly month-to-month.

Deployment Process for Open WebUI on Railway

Open WebUI is a lightweight, self-hosted chat interface that integrates with Ollama, OpenAI-compatible APIs, and other LLM providers. It's built with Python and SvelteKit, offering offline-first functionality.

Steps:

  1. Prepare Your Repository: Fork the Open WebUI repository or create a custom deployment repository with a Dockerfile. Open WebUI provides an official Docker image at ghcr.io/open-webui/open-webui:main.

  2. Create a New Project: In Railway, create a new project and select "Deploy from GitHub repo" or "Docker Image". If using the Docker image, specify ghcr.io/open-webui/open-webui:main.

  3. Configure Environment Variables: Set required variables including:

  4. WEBUI_SECRET_KEY: A random string for session encryption
  5. OLLAMA_BASE_URL: URL to your Ollama instance or API endpoint (if using external model hosting)

  6. Any provider-specific API keys (OpenAI, Anthropic, etc.)

  7. Add Persistent Storage: Open WebUI stores user data, conversation history, and settings in /app/backend/data. Create a Railway volume and mount it to this path to persist data across deployments.

  8. Deploy: Railway automatically builds and deploys the application. The deployment includes a public URL with HTTPS enabled by default.

  9. Monitor Usage: Railway's dashboard shows real-time resource usage including CPU, memory, and bandwidth consumption, helping you track costs.

Strengths

Railway's usage-based pricing prevents paying for idle resources. If your application receives minimal traffic, you only pay for the actual compute time used. The automatic preview environments for pull requests support collaborative development without manual infrastructure setup.

The platform supports private Docker registries on the Pro plan, which is useful for deploying proprietary modifications to open-source chat interfaces.

Limitations

The lack of predictable monthly costs can be problematic for budgeting. Applications with consistent traffic may end up paying more than they would on fixed-price platforms. Railway also requires the Pro plan for certain features like private Docker registries.

Resource limits are less transparent than platforms with clearly defined instance tiers. You need to actively monitor consumption to avoid unexpected charges.

3. Render — Best for Managed Databases and Zero-Downtime Deploys

Render positions itself as a modern Heroku alternative with built-in support for web services, databases, cron jobs, and static sites. The platform emphasizes reliability with features like zero-downtime deployments, automatic rollbacks, and managed PostgreSQL with point-in-time recovery.

Technical Specifications

Render supports multiple deployment sources including GitHub, GitLab, Docker images, and infrastructure-as-code blueprints. The blueprint feature allows you to define your entire application stack (web services, databases, environment variables) in a single YAML file, making deployments reproducible and version-controlled.

Every pull request can trigger a preview environment that spins up a complete copy of your application infrastructure. This feature is particularly valuable for testing database migrations or API changes before merging to production.

Pricing Structure

Render offers a free tier with significant limitations and paid plans starting at $7/month for individual services:

Free Tier: - Web services spin down after 15 minutes of inactivity and spin up when receiving requests (causing 30-60 second cold starts) - 750 free instance hours per workspace per month - PostgreSQL databases limited to 1 GB storage and expire after 30 days

Paid Plans: - Individual services start at $7/month (shared CPU with 512 MB RAM) - PostgreSQL databases start at $7/month for 1 GB storage - Professional plan at $19/month per user adds team features and higher resource limits - Enterprise plans available for compliance requirements (SOC 2 Type 2, HIPAA, ISO 27001, GDPR)

For deploying a ChatGPT alternative with a database, expect minimum costs around $14/month on paid plans, though you could test on the free tier with the understanding that cold starts will impact user experience.

Deployment Process for AnythingLLM on Render

AnythingLLM is an all-in-one AI chat application focused on document interaction and knowledge base integration through Retrieval-Augmented Generation (RAG). It supports both desktop and server deployments.

Steps:

  1. Create a Dockerfile: While AnythingLLM provides a Docker image (mintplexlabs/anythingllm:master), Render's Docker deployment requires a Dockerfile in your repository. Create one that uses this base image or builds from source.

  2. Set Up Web Service: In Render, create a new Web Service, connect your GitHub repository, and specify the Dockerfile location. Choose the service tier based on expected usage (minimum 512 MB RAM recommended).

  3. Configure Persistent Disk: AnythingLLM stores vector databases, uploaded documents, and configuration in /app/server/storage. Add a persistent disk in Render and mount it to this path. Disk storage starts at $0.25/GB/month.

  4. Environment Variables: Configure:

  5. STORAGE_DIR=/app/server/storage
  6. SERVER_PORT=3001 (or Render's PORT environment variable)
  7. API keys for LLM providers you plan to use

  8. SYS_ADMIN capability if required (may need to contact Render support)

  9. Deploy: Render builds the Docker image and deploys to your selected region. The platform provides a URL with automatic SSL.

  10. Configure Domain: Add custom domains through Render's dashboard. SSL certificates are provisioned automatically via Let's Encrypt.

Strengths

Render's managed PostgreSQL databases include automated backups, point-in-time recovery, and encryption at rest (minimum AES-128). This eliminates the need to manage database infrastructure separately.

The infrastructure-as-code blueprint approach makes complex multi-service deployments reproducible. You can define your entire stack configuration in version control and deploy identical environments for development, staging, and production.

Zero-downtime deployments with automatic health checks prevent user-facing outages during updates. If a new deployment fails health checks, Render automatically rolls back to the previous version.

Limitations

The free tier's spin-down behavior makes it unsuitable for production use. A 30-60 second delay for the first request after inactivity creates poor user experience for chat applications where response time is critical.

Render does not natively support MongoDB, which is required by LibreChat and some other chat interfaces. You would need to use an external MongoDB service like MongoDB Atlas, adding complexity and cost.

The 750 free instance hours per month across all services in a workspace is restrictive if you want to run multiple applications or environments.

4. Fly.io — Best for Global Edge Deployment

Fly.io focuses on global edge deployment, running applications close to users to minimize latency. The platform uses hardware-isolated virtual machines called "Machines" that can start in milliseconds and run for any duration from a single HTTP request to continuous operation.

Technical Specifications

Fly Machines are more flexible than traditional containers. You can start a Machine, run a workload, and stop it programmatically, paying only for runtime. This model is particularly efficient for event-driven workloads or applications with sporadic traffic.

Fly.io deploys applications to 35 global regions from Sydney to São Paulo. The platform uses Anycast routing to direct users to the nearest instance, reducing latency significantly compared to single-region deployments.

The platform includes zero-configuration private networking via WireGuard VPN, allowing secure communication between services without exposing ports to the internet.

Pricing Structure

Fly.io no longer offers a free tier for new organizations as of recent changes. Pricing is strictly usage-based:

  • Compute: Shared CPU instances start at approximately $0.0027/hour ($1.94/month for continuous operation), with dedicated CPU options available
  • Memory: Priced per GB-hour
  • Storage: Persistent volumes at $0.15/GB/month
  • Bandwidth: Outbound data starts at $0.02/GB in North America and Europe, up to $0.12/GB in other regions (inbound is free)
  • Databases: Fly Postgres costs $2-5/month for small instances, $80+ for high-availability configurations

A minimal deployment running one shared-cpu-1x instance with 256 MB RAM and 10 GB storage would cost approximately $3-5/month, though actual costs vary based on traffic and resource usage.

Deployment Process for LibreChat on Fly.io

Steps:

  1. Install Fly CLI: Download and install the Fly CLI tool, then authenticate with fly auth login.

  2. Prepare Application: Clone LibreChat and create a fly.toml configuration file defining your app name, region, and environment variables.

  3. Configure Database: Use fly postgres create to provision a Fly Postgres instance. This provides a connection string for the MONGO_URI variable. Note: LibreChat uses MongoDB, so you'll need to either run a MongoDB instance as a separate Fly app or use an external MongoDB service like MongoDB Atlas.

  4. Set Secrets: Use fly secrets set to configure sensitive environment variables: 

    fly secrets set SESSION_SECRET=your-secret-key
fly secrets set OPENAI_API_KEY=your-api-key

  5. Create Persistent Volume: LibreChat may need persistent storage for file uploads: 

    fly volumes create librechat_data --size 10

  6. Deploy: Run fly deploy to build and deploy your application. Fly detects your Dockerfile or uses Nixpacks for automatic builds.

  7. Scale Globally: Deploy to multiple regions with fly regions add syd sea (adds Sydney and Seattle). Fly automatically distributes traffic to the nearest instance.

Strengths

Fly.io's global edge deployment significantly reduces latency for geographically distributed users. A user in Australia connects to a Sydney instance while a European user connects to a Frankfurt instance, both accessing the same application.

The Machines model provides cost efficiency for variable workloads. If your chat interface receives sporadic usage, you only pay for actual runtime rather than continuous instance hours.

The integrated WireGuard VPN simplifies secure inter-service communication. You can run your LLM API on a separate Machine with GPU access and connect it privately to your chat interface without exposing API endpoints publicly.

Limitations

The absence of a free tier increases the barrier to experimentation. You need to commit to paid resources from the start, which may deter hobbyists or those evaluating the platform.

Fly.io's usage-based pricing requires active monitoring to avoid unexpected costs. Without proper resource limits, a traffic spike or runaway process can result in significant charges.

The platform is optimized for stateless applications. While persistent volumes are supported, running stateful services like databases requires more configuration than on traditional PaaS platforms.

5. DigitalOcean App Platform — Best for Predictable Pricing

DigitalOcean App Platform is a fully managed PaaS built on DigitalOcean's infrastructure. The platform abstracts away server management while providing access to the broader DigitalOcean ecosystem including Managed Databases, Spaces (object storage), and Kubernetes.

Technical Specifications

App Platform supports Node.js, Python, Ruby, PHP, Go, and .NET, plus static sites and Docker containers. Applications deploy directly from GitHub or GitLab with automatic redeployment when you push code.

The platform provides modular resource configuration. Instead of choosing predefined instance types, you select CPU type (shared or dedicated), memory allocation, and bandwidth limits independently. This granular control allows optimization for specific workload requirements.

Pricing Structure

Free Tier: - Up to 3 static site apps - 1 GiB outbound bandwidth per app per month - Additional bandwidth at $0.02/GiB

Paid Plans: - Basic apps (shared CPU, 512 MB RAM, 50 GiB bandwidth): $5/month - Professional apps (dedicated CPU options): Starting at $12/month for 1 GB RAM - Managed databases: Starting at $7/month for development databases (512 MB) - Persistent storage: Varies by configuration

A complete ChatGPT alternative deployment with database would cost approximately $12-19/month depending on resource requirements.

Deployment Process for Open WebUI on DigitalOcean App Platform

Steps:

  1. Create App: In DigitalOcean control panel, select "App Platform" and create a new app. Connect your GitHub account and select the Open WebUI repository.

  2. Configure Build: DigitalOcean auto-detects Dockerfiles. Specify the Dockerfile location if not in the repository root. Alternatively, configure build commands for non-Docker deployments.

  3. Select Resources: Choose a Basic or Professional plan. For Open WebUI, start with 1 GB RAM (Professional tier at $12/month). The platform allows you to scale resources later without redeploying.

  4. Environment Variables: Add required variables:

  5. WEBUI_SECRET_KEY
  6. OLLAMA_BASE_URL (if using external Ollama instance)

  7. LLM provider API keys

  8. Add Persistent Storage: Open WebUI needs storage for user data and conversation history. While App Platform doesn't directly support volume mounts for Dockerized apps, you can use DigitalOcean Spaces for object storage or connect to an external database for data persistence.

  9. Configure Domain: Add a custom domain through the App Platform dashboard. DigitalOcean provisions SSL certificates automatically.

  10. Deploy: App Platform builds and deploys automatically. The platform provides a URL immediately and updates the domain configuration once SSL provisioning completes.

Strengths

DigitalOcean's pricing is transparent and predictable. Unlike usage-based platforms, you know exactly what you'll pay each month. This makes budgeting straightforward for production applications.

The platform includes DDoS protection, automatic OS patching, and managed SSL certificates. These security features are particularly valuable for applications handling user data or authentication.

Integration with DigitalOcean's Managed Databases eliminates the need for separate database hosting. You can provision PostgreSQL, MySQL, MongoDB, or Redis databases directly from the same control panel.

Limitations

The lack of persistent storage volumes for Docker deployments is a significant limitation. Applications requiring file persistence need to use external object storage or databases, adding complexity.

Autoscaling is available but limited compared to platforms like AWS or Google Cloud. The platform can scale instances horizontally during traffic spikes, but configuration options are less granular.

The minimum resource tier for dynamic applications ($5/month for 512 MB RAM) may be insufficient for resource-intensive chat interfaces, forcing you to use higher-tier plans that increase costs.

6. Coolify — Best for Self-Hosting on Your Own Infrastructure

Coolify is an open-source, self-hostable alternative to Heroku and Vercel that runs on any server you control. Unlike cloud-based PaaS platforms, Coolify gives you complete infrastructure ownership while providing a simplified deployment experience similar to commercial platforms.

Technical Specifications

Coolify requires a server with SSH access running Ubuntu LTS (20.04, 22.04, or 24.04) and Docker Engine version 24+. Minimum specifications are 2 CPU cores and 2 GB RAM, though 4 GB is recommended for running multiple applications.

The platform supports deploying static sites, APIs, backends, databases, and services from Git repositories (GitHub, GitLab, Bitbucket, Gitea) or Docker images. It automatically configures and renews Let's Encrypt SSL certificates for custom domains.

Coolify supports single servers, multi-server setups, and Docker Swarm clusters, with Kubernetes support planned. This flexibility allows you to start small and scale infrastructure as needed.

Pricing Structure

Coolify itself is free and open-source (MIT license). Your only costs are:

  • Server hosting: Varies by provider (Hetzner Cloud: €3.29/month for 2 vCPU, 2 GB RAM; DigitalOcean: $12/month for similar specs)
  • Bandwidth: Included with most VPS providers (typically 1-20 TB/month depending on location)
  • Additional storage: If needed beyond base VPS allocation

For a complete ChatGPT alternative deployment on Coolify hosted on Hetzner, you could operate for approximately €3-10/month depending on resource requirements.

Deployment Process for LibreChat on Coolify

Steps:

  1. Provision Server: Create a VPS with Ubuntu LTS. Hetzner Cloud, DigitalOcean, Linode, Vultr, or any provider with SSH access works. Ensure at least 2 GB RAM and 20 GB storage.

  2. Install Coolify: SSH into your server and run the automatic installation script: 

    curl -fsSL https://cdn.coollabs.io/coolify/install.sh | bash
    This installs Docker and Coolify's management interface.

  3. Access Coolify: Navigate to http://your-server-ip:8000 in a browser. Complete the initial setup wizard to create an admin account.

  4. Create Project: In Coolify, create a new project for your ChatGPT alternative deployment.

  5. Add Resource: Add a new resource, select "Docker Compose" as the type, and connect your LibreChat repository or use the official Docker Compose configuration.

  6. Configure Services: LibreChat requires MongoDB and MeiliSearch. Coolify can deploy these as additional containers:

  7. Add MongoDB container with persistent volume
  8. Add MeiliSearch container

  9. Configure LibreChat container with environment variables linking to these services

  10. Environment Variables: Set required variables including MONGO_URI, SESSION_SECRET, and API keys through Coolify's interface.

  11. Configure Domain: Add your custom domain in Coolify. The platform automatically requests and configures a Let's Encrypt SSL certificate.

  12. Deploy: Coolify builds and starts all containers. Access your deployment at your configured domain.

Strengths

Coolify provides complete control over your infrastructure and data. There are no vendor lock-in concerns, and you can migrate to different hosting providers by simply pointing Coolify at a new server.

The cost advantage is significant for persistent workloads. A €3.29/month Hetzner VPS running Coolify can host multiple applications, whereas each application would incur separate charges on commercial PaaS platforms.

Built-in database backup functionality protects against data loss without requiring separate backup solutions. Coolify can perform scheduled backups to local storage or external providers.

Limitations

Self-hosting requires more technical knowledge than managed PaaS platforms. You're responsible for server security, updates, and troubleshooting infrastructure issues.

Coolify lacks the global edge distribution of platforms like Fly.io or Cloudflare. Your application runs from a single region unless you manually configure multi-region deployments with load balancing.

The platform is still maturing compared to established commercial alternatives. While actively developed, some advanced features available on platforms like Render or Railway may be missing or less polished.

7. Vercel — Best for Frontend-Heavy Applications (With Limitations)

Vercel is a serverless platform optimized for frontend frameworks and static sites, particularly Next.js. While not ideal for traditional backend deployments, it can host chat interfaces that rely primarily on client-side rendering with API routes for backend logic.

Technical Specifications

Vercel supports 35+ frameworks including Next.js, React, Vue, Angular, and Svelte. The platform uses serverless functions for backend logic with built-in edge function support for globally distributed compute.

Critically, Vercel does not support Docker deployments or long-running processes. This limits its applicability for many open-source ChatGPT alternatives that rely on Docker or require persistent backend services.

Pricing Structure

Hobby Plan (Free): - Unlimited static deployments - 100 GB bandwidth per month - Serverless functions with 10-second execution limit - 1 hour of runtime log retention

Pro Plan ($20/user/month): - Everything in Hobby plus usage credits - 60-second function execution limit - 1 day of runtime log retention - Advanced analytics

Enterprise (Custom): - Custom resource limits - 99.99% SLA - Dedicated support - Enhanced security features

Deployment Considerations

Most Docker-based ChatGPT alternatives (LibreChat, Open WebUI, AnythingLLM) cannot deploy directly to Vercel due to the lack of Docker support and long-running process requirements.

However, you could deploy a Next.js-based chat interface that uses Vercel's API routes to proxy requests to external LLM APIs. This would involve building a custom frontend rather than using existing open-source solutions.

Strengths

Vercel excels at static site and frontend deployment with automatic global CDN distribution. If you're building a custom chat interface with Next.js, the platform provides excellent performance and developer experience.

The automatic preview deployments for pull requests enable collaborative development workflows. Each PR gets its own URL for testing before merging.

Built-in analytics and Web Vitals monitoring help optimize application performance without third-party tools.

Limitations

The lack of Docker support eliminates most existing open-source ChatGPT alternatives from consideration. You would need to rebuild the application using Vercel-compatible architecture.

Serverless function time limits (10 seconds on free tier, 60 seconds on Pro) can be problematic for LLM requests that may take longer to complete, especially for complex prompts or rate-limited APIs.

No persistent storage or WebSocket support restricts functionality for chat applications that require real-time updates or maintaining state between requests.

For most users deploying existing open-source ChatGPT alternatives, Vercel is not a practical option.

8. Hetzner Cloud — Best for Cost-Effective VPS Hosting

Hetzner Cloud provides Infrastructure-as-a-Service (IaaS) rather than Platform-as-a-Service (PaaS), meaning you manage the operating system and application deployment manually. However, when combined with Docker and tools like Docker Compose, it offers an extremely cost-effective hosting solution.

Technical Specifications

Hetzner offers VPS instances with AMD EPYC processors and Arm64 architecture options. Servers deploy in European locations (Germany, Finland, Austria), US locations (Ashburn, Hillsboro), and Singapore.

Each VPS includes generous bandwidth allocations: 20 TB/month for European locations, 1 TB/month for US and Singapore. This is significantly more than many PaaS platforms provide in their base tiers.

Pricing Structure

Hetzner's pricing is remarkably competitive:

  • CPX11 (2 vCPU, 2 GB RAM, 40 GB SSD, 20 TB bandwidth): €4.15/month
  • CPX21 (3 vCPU, 4 GB RAM, 80 GB SSD, 20 TB bandwidth): €7.90/month
  • CPX31 (4 vCPU, 8 GB RAM, 160 GB SSD, 20 TB bandwidth): €14.00/month

Backups cost an additional 20% of the server price (e.g., €0.83/month for CPX11).

Hetzner uses both monthly pricing caps and hourly billing. If you delete a server mid-month, you only pay the hourly rate for actual usage. This provides flexibility for testing and development environments.

Deployment Process for LibreChat on Hetzner Cloud

Steps:

  1. Create Server: In Hetzner Cloud Console, create a new VPS. Select Ubuntu 22.04 LTS, choose location (Europe for best pricing), and select CPX21 or higher for running LibreChat with moderate traffic.

  2. Initial Server Setup: SSH into your server and perform initial configuration: 

    apt update && apt upgrade -y
apt install -y docker.io docker-compose git
systemctl enable docker

  3. Configure Firewall: Set up UFW or use Hetzner's Cloud Firewall to allow only ports 22 (SSH), 80 (HTTP), and 443 (HTTPS): 

    ufw allow 22
ufw allow 80
ufw allow 443
ufw enable

  4. Clone LibreChat: Clone the LibreChat repository and navigate to the directory: 

    git clone https://github.com/danny-avila/LibreChat.git
cd LibreChat

  5. Configure Environment: Copy the example environment file and edit with your configuration: 

    cp .env.example .env
nano .env
    Set SESSION_SECRET, API keys, and other required variables.

  6. Deploy with Docker Compose: Start LibreChat and its dependencies (MongoDB, MeiliSearch): 

    docker-compose up -d

  7. Configure Reverse Proxy: Install and configure Nginx to handle SSL and proxy requests to LibreChat: 

    apt install -y nginx certbot python3-certbot-nginx
    Create an Nginx configuration for your domain and obtain an SSL certificate: 
    certbot --nginx -d your-domain.com

  8. Access Deployment: Navigate to your domain in a browser. LibreChat should be accessible with HTTPS.

Strengths

Hetzner's pricing offers exceptional value. A CPX21 instance (€7.90/month) provides more resources than many PaaS platforms' entry tiers at a fraction of the cost.

The included bandwidth (20 TB in Europe) is far more generous than most PaaS platforms that charge per GB for overages. This is particularly valuable for chat applications that may transfer significant data during heavy usage.

Full root access allows complete customization. You can install any dependencies, modify system configurations, and run multiple applications on a single VPS.

Limitations

Hetzner requires manual server management. You're responsible for security updates, SSL certificate renewal (even with certbot automation, you need to monitor it), and troubleshooting infrastructure issues.

The lack of managed databases means you deploy and maintain MongoDB, PostgreSQL, or other databases yourself. This adds operational complexity compared to platforms with managed database offerings.

Hetzner does not provide PaaS features like automatic deployments from Git, preview environments, or built-in CI/CD. You would need to set up these workflows manually using tools like GitHub Actions or Jenkins.

Geographic availability is limited compared to global platforms like Fly.io or AWS. If your users are primarily in South America, Africa, or Oceania, latency will be higher than with edge-distributed platforms.

Choosing the Right Platform for Your Needs

The optimal hosting platform depends on your specific requirements, technical expertise, and budget:

Choose Code Capsules if you want the fastest path from code to deployment with minimal configuration. The integrated capsule architecture and automatic builds make it ideal for developers who want to focus on application logic rather than infrastructure. Best for small-to-medium applications where simplicity outweighs cost optimization.

Choose Railway if you have variable traffic patterns and want to pay only for actual resource usage. The usage-based pricing can provide significant savings for applications with sporadic usage. The automatic preview environments support collaborative development workflows.

Choose Render if you need managed databases with automated backups and point-in-time recovery. The infrastructure-as-code blueprint approach makes complex multi-service deployments reproducible. Best for teams that value reliability and zero-downtime deployments.

Choose Fly.io if you have globally distributed users and latency is critical. The edge deployment model provides the best performance for international user bases. The Machines architecture offers flexibility for event-driven or sporadic workloads.

Choose DigitalOcean App Platform if you want predictable monthly costs and transparent pricing. The integration with DigitalOcean's broader ecosystem (Managed Databases, Kubernetes, Spaces) provides expansion options as your application grows.

Choose Coolify if you want complete infrastructure control and the lowest possible operating costs. Self-hosting on Hetzner with Coolify can cost 70-80% less than commercial PaaS platforms while providing similar deployment convenience. Best for technically proficient users comfortable managing servers.

Choose Vercel if you're building a custom Next.js chat interface and don't need Docker or long-running processes. Not suitable for deploying existing open-source ChatGPT alternatives without significant modification.

Choose Hetzner Cloud if you need maximum cost efficiency and have the technical skills to manage servers manually. The generous resource allocations and bandwidth make it ideal for high-traffic applications where PaaS costs would be prohibitive.

Technical Considerations Across All Platforms

Regardless of which platform you choose, several technical considerations apply to all ChatGPT alternative deployments:

Environment Variables: All chat interfaces require secure storage of API keys and configuration. Use the platform's secrets management rather than committing sensitive data to Git repositories.

Database Persistence: Ensure conversation history and user data persist across deployments. This typically requires persistent volumes, managed databases, or external database services.

SSL/TLS: HTTPS is essential for chat applications handling user data. Most platforms provide automatic SSL certificates, but verify configuration to ensure encryption is active.

Scaling Strategy: Determine whether vertical scaling (larger instances) or horizontal scaling (more instances) makes sense for your traffic patterns. Some platforms handle this automatically; others require manual configuration.

Monitoring and Logging: Implement monitoring to track application health, resource usage, and errors. Many platforms include basic monitoring, but you may need external tools like Sentry or LogRocket for production deployments.

Backup Strategy: Beyond database backups, consider backing up configuration, custom code, and user-uploaded files. Test restoration procedures to ensure backups are actually recoverable.

Conclusion

Deploying an open-source ChatGPT alternative has become increasingly accessible in 2025, with platforms ranging from fully managed PaaS solutions to cost-effective self-hosted options. Code Capsules leads in deployment simplicity with its integrated capsule architecture, automatic builds, and unified project management. For developers prioritizing rapid deployment and operational simplicity, it provides the most streamlined experience.

However, the right choice depends on your specific context. Railway and Fly.io excel in usage-based pricing and global distribution. Render provides enterprise-grade reliability with managed databases. DigitalOcean offers predictable costs and ecosystem integration. Coolify and Hetzner deliver exceptional value for those willing to manage infrastructure.

The open-source AI landscape continues to evolve rapidly, with new models and interfaces emerging regularly. Choosing a flexible deployment platform that can adapt to changing requirements will serve you better than optimizing solely for initial costs or features. Start with the platform that matches your current technical expertise and scaling needs, knowing you can migrate as requirements change.

For most developers deploying their first ChatGPT alternative, Code Capsules offers the optimal balance of simplicity, features, and cost. The automatic builds, integrated databases, and included monitoring provide everything needed for a production deployment without requiring deep infrastructure knowledge. As your application grows and requirements evolve, you can reassess whether the additional control and cost savings of other platforms justify the increased operational complexity.