How to Use the Together AI Alternative MCP in Mastra AI

Q: How do I monitor multiple jobs with Together AI Alternative and Mastra AI?

You use the listbatches tool to see every job running or completed. For specific details, you call getbatch, which pulls all the necessary metrics for that ID.

Q: Can I manage model versions with Together AI Alternative and Mastra AI?

Yes. You can call listmodels to see what's available, or use getfinetune and listfinetunes to track specific model customizations.

Q: How does Together AI Alternative handle endpoint scaling with Mastra AI?

You create a dedicated point by calling createendpoint. If load changes, you can adjust the capacity using updateendpoint, giving your workflow predictable performance under varying loads.

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Setup Together AI Alternative with Mastra AI

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Managing Asynchronous Workloads in Mastra AI

Start big jobs by calling `create_batch`. This initiates an asynchronous task, and you get a job ID back. You then use `get_batch` to check the status or `list_batches` for an overview of all running jobs. This pattern is key for building reliable workflows that can execute massive data processing tasks without timing out.

Creating Dedicated Resources with Together AI Alternative MCP Server

For predictable performance, use `create_endpoint` to spin up a dedicated resource. You'll get an endpoint ID back, which you can then pass to your agent logic. When the workflow needs it, you can also call `update_endpoint` to scale or stop that specific connection point.

Handling File Operations for Mastra AI

Need to process data? Use `upload_file` first. This uploads files for fine-tuning or batch inference, returning a file ID you can track. You also have tools like `delete_file` and `get_file`. Managing these assets ensures your multi-step workflow keeps track of its inputs and outputs.

Setup guide

Set up Together AI Alternative MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Together AI Alternative tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "together-ai-alternative-mcp-client",
  servers: {
    "together-ai-alternative-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Together AI Alternative Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Together AI Alternative tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Together AI Alternative transactions"
);
console.log(result.text);

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Together AI. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Together AI Alternative MCP in Mastra AI

You use the `list_batches` tool to see every job running or completed. For specific details, you call `get_batch`, which pulls all the necessary metrics for that ID.

This MCP Server handles text, audio files (for transcription), images, video prompts, and metadata associated with uploaded files. The `create_embeddings` tool specifically uses text.

Yes. You can call `list_models` to see what's available, or use `get_fine_tune` and `list_fine_tunes` to track specific model customizations.

You create a dedicated point by calling `create_endpoint`. If load changes, you can adjust the capacity using `update_endpoint`, giving your workflow predictable performance under varying loads.

The server touches file metadata and the raw input/output content of those uploaded files. Specifically, `upload_file` handles binary or text data required by the model training process.

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