5 Ways to Use Local Inference with Msty Studio

When we talk with organizations exploring AI for their teams, one of the most common questions we get is: “Can we keep everything local?”

It’s a fair question, and an important one. Many companies are rightly concerned about their data leaving their network or being processed by large online LLMs.

Using local inference is a great way to maintain control and privacy while still unlocking the productivity of generative AI.

However, running models locally means you’ll need to choose how to deploy and manage them. There are several great options, each with different trade-offs around setup, cost, and scalability.

Whichever route you choose, Msty Studio acts as a powerful front-end interface for your organization’s AI workflows.

With Msty Studio Enterprise, admins can preconfigure which models employees can access, ensuring teams get started instantly, without needing to set up or manage model connections themselves.

Let’s take a look at five ways your organization can run local inference with Msty Studio, along with their advantages and disadvantages.

Option 1: Run Local Models on Employee Machines

Allow each employee to install and run models directly on their local machine using Msty Studio. This setup is perfect for smaller teams or privacy-first organizations where data must stay fully local.

Advantages:

• No additional infrastructure or hosting costs
• Complete data privacy, nothing leaves the user’s device
• Ideal for experimentation and teams with flexible setups

Disadvantages:

• Requires setup by each employee
• Performance depends on their hardware and older machines may struggle with larger models
• Harder to manage updates and consistency across multiple devices

Option 2: Use Azure AI or AWS Bedrock

Organizations can leverage Azure AI or AWS Bedrock to host and run local inference on their existing enterprise infrastructure. Msty Studio has built-in connectors for both, so setup is straightforward.

Advantages:

• Seamless integration with existing enterprise security, IAM policies, and infrastructure
• Scalable compute power with enterprise-grade reliability
• Minimal setup and Msty Studio can connect directly to your Azure or AWS endpoints

Disadvantages:

• May involve additional cloud costs, depending on usage volume
• Still requires trust in cloud provider’s handling of internal data
• Configuration complexity may increase for multi-region or hybrid deployments

Option 3: Use a Managed Service Provider (MSP)

Some organizations prefer outsourcing infrastructure setup and model management. An MSP can spin up and maintain a private inference environment, expose an OpenAI-compatible API endpoint, and integrate it easily with Msty Studio.

Advantages:

• Minimal technical overhead for your internal team
• High control over infrastructure setup and compliance requirements
• Easy integration, Msty Studio connects directly via standard API

Disadvantages:

• Ongoing MSP management and service fees
• May be limited by the provider’s technical expertise or supported models
• Reliance on third-party SLAs for uptime and performance

Option 4: Deploy a Dedicated Internal or VPS Server

Organizations can provision an internal server or a VPS (from providers like Vultr, DigitalOcean, or Hetzner) and install Msty Studio directly on it. Currently, Msty Studio requires a GUI to run, but a headless version is in development for even more flexibility.

Advantages:

• Full control over data and infrastructure
• Cost-effective and scalable for smaller or mid-sized teams
• Quick setup and easy to maintain once running

Disadvantages:

• Requires internal resources for setup, monitoring, and maintenance
• Potentially less scalable for large enterprises without automation or orchestration tools
• GUI dependency (for now) may limit remote server deployment options

Option 5: Use On-Premise Inference Servers (Air-Gapped Environments)

For highly regulated industries or organizations with strict security policies, deploying on-premise inference servers offers maximum control. This option keeps everything from model weights to inference data inside your local network or data center.

Advantages:

• Ultimate privacy and compliance with no external data transfer
• Full visibility and control over every part of the inference pipeline
• Compatible with Msty Studio via standard local API or LAN connection

Disadvantages:

• Highest upfront cost and requires enterprise hardware or GPUs
• Ongoing IT and DevOps maintenance required
• Slower to scale or upgrade models compared to cloud options

Summary

Choosing the right local inference setup depends on your organization’s size, data sensitivity, and technical capacity. Whether you prefer total control on-premise, leverage existing cloud infrastructure, or use managed services. Msty Studio provides a unified interface to make your AI systems more productive, secure, and easy to manage.

You can easily use Msty Studio as a front-end with any of these local inference setups. To try it out with your own backend, request a free Enterprise pilot at msty.ai/enterprise.

For questions or to discuss deployment options, reach out to us at [email protected]. We’re happy to help collaborate on what makes the most sense for your team.