Almost every web project eventually reaches the point where "let's add some AI" turns from a one-line idea into a small infrastructure problem. The moment you want more than a single model, you discover that GPT lives behind one account, Claude behind another, and Gemini behind a third. Each provider has its own SDK, its own API key, its own billing portal, its own rate limits and its own quirks. For a freelancer or a small team that simply wants a chatbot, a content generator or an image feature on a hosted site, that sprawl becomes a real maintenance burden, not because any single integration is hard, but because you now have three or four of them to keep alive.
Over the past year a much cleaner pattern has taken hold, and it is worth knowing if you run applications on a server: route every model through one OpenAI-compatible gateway. Instead of integrating five vendors, you point your code at a single base URL with a single key, and you change models by editing one string. This article walks through how that works in practice and why it tends to be the right default for hosted projects.
Why "OpenAI-compatible" is the key phrase
The reason this approach is so frictionless is historical. The OpenAI chat API became the de facto standard, and almost every AI library, framework and desktop client now speaks its request format. So a gateway that implements the same /v1/chat/completions and /v1/models endpoints is a genuine drop-in replacement. Your existing OpenAI SDK keeps working; tools such as Open WebUI, LibreChat, Cursor, Cline or the Python and Node SDKs keep working. You change only two things: the base URL and the API key.
avots.ai exposes exactly this kind of OpenAI-compatible API. One account and one balance give you Claude, GPT, Gemini, DeepSeek and Grok, all addressable by a model id. A minimal request looks identical to a normal OpenAI call:
curl https://api.avots.ai/openai/v1/chat/completions
-H "Authorization: Bearer YOUR_KEY"
-H "Content-Type: application/json"
-d '{
"model": "anthropic/claude-sonnet-4.6",
"messages": [{"role": "user", "content": "Hello!"}]
}'If you have ever made an OpenAI call, there is nothing new to learn here. That is precisely the point.
Switching models becomes a config change
The practical payoff for a hosted application is that model choice stops being an architectural decision and becomes a configuration value. Need a cheap, fast model to generate first drafts or to classify support tickets, and a stronger one only for the final answer? You change the model id: no new SDK, no second billing relationship, no extra secrets in your environment, no redeploy of a whole new client library. It also makes it trivial to A/B different models, to fall back to a cheaper option under load, or to adopt a newer model the day it ships, without touching your integration code.
Beyond chat: tools through MCP
Chat completions are only half of what a modern AI setup needs. The other half is tools, the ability for an assistant to actually do something: generate an image, render a short video, synthesise speech, or build a talking-avatar clip. This is handled by a newer standard called the Model Context Protocol (MCP), which lets AI clients such as Claude Desktop, Cursor and Cline call external capabilities in a structured way.
With an MCP server, those creative tools become available inside the client you already use, authenticated with the same key as the chat API. For a developer that is a meaningful simplification: image or media generation turns into a tool call rather than yet another third-party service to evaluate, sign up for and wire into your backend. The chat API gives your apps a brain, MCP gives them hands, and both run off one balance.
What this looks like in a real hosted project
Concretely, the unified approach shows up in everyday features. A customer-support widget can route routine questions to a cheap model and escalate hard ones to a stronger model, all through the same endpoint. A content site can generate drafts, summaries and translations from a single integration. An internal tool can produce illustrations or short social clips on demand through MCP. None of these require a separate vendor relationship, and none of them lock you into one model family. If a better or cheaper option arrives, you change the string and move on.
One balance and EU-friendly billing
The gateway model also cleans up the unglamorous side of running AI in production. A single top-up covers chat, images, audio and video; usage is metered per request rather than spread across several invoices; and you are not storing card details with half a dozen vendors. For teams in Latvia, the Baltics and the wider EU, paying one European provider in euros is simply easier to reconcile than juggling several USD bills with a moving exchange rate. The platform that sits behind both the chat API and the MCP tools is avots.ai.
Getting started
The on-ramp is short. Create a key, set your client's base URL to the gateway, choose a model id such as anthropic/claude-sonnet-4.6, and send a first request. If you already have code that talks to OpenAI, you are most of the way there: repoint the base URL and you are done. When you later want media generation, add the MCP server to your AI client with the same key. The whole arc, from "we should add AI" to "it is live on our server", is measured in minutes rather than days, and you finish with one integration to maintain instead of five.