PremAI vs Together AI for On-Premise Fine-Tuning

Key Takeaways

• Complete Data Sovereignty: PremAI’s “Not Your Weights, Not Your Model” philosophy ensures total ownership and control, with zero-copy pipelines that keep all data within your infrastructure
• 50-70% Cost Reduction: On-premise deployment with PremAI achieves breakeven in 12-18 months for organizations processing 500M+ tokens monthly, with sustained savings thereafter
• Sub-100ms Latency Performance: PremAI delivers 50% latency reduction vs cloud alternatives, enabling real-time applications impossible with API-based solutions
• No ML Expertise Required: Autonomous fine-tuning system with multi-GPU orchestration eliminates the need for specialized machine learning teams
• Built-In Compliance: GDPR, HIPAA, and SOC 2 compliance out-of-the-box with automatic PII redaction and TrustML privacy-preserving encryption
• True On-Premise Deployment: Download model checkpoints and deploy on your infrastructure with vLLM, Hugging Face, or Ollama - no external dependencies
• 8× Faster Development: Automated hyperparameter optimization and model selection accelerates time-to-production compared to manual approaches

The Enterprise On-Premise Fine-Tuning Landscape

Enterprise AI deployments face a critical decision: cloud-based API services or on-premise solutions. For organizations handling sensitive data, operating in regulated industries, or processing high volumes of inference requests, this choice has massive implications for costs, compliance, performance, and competitive positioning.

Together AI offers cloud-hosted inference and fine-tuning through API endpoints. While this approach provides quick setup, it introduces dependencies on external infrastructure, exposes proprietary data to third-party systems, and creates variable costs that scale unpredictably with usage.

PremAI takes a different approach: complete on-premise deployment with downloadable model weights, zero external dependencies, and total data sovereignty. Your data never leaves your infrastructure, your model improvements stay exclusively with your organization, and your costs become predictable and controllable.

Let’s examine why PremAI stands as the superior choice for enterprise on-premise fine-tuning.

1. Data Sovereignty: Not Your Weights, Not Your Model

PremAI’s Ownership Philosophy

PremAI’s foundational principle: “Not Your Weights, Not Your Model.” This means complete ownership and control over your AI assets and data, keeping your intellectual property secure within your perimeter.

When you fine-tune models with PremAI, you receive:

• Downloadable model checkpoints in ZIP format with full weights
• Zero-copy data pipelines where your data never touches Prem servers
• Complete infrastructure control with deployment on bare-metal, AWS VPC, or on-premises
• No vendor lock-in - models are yours to keep and deploy anywhere

Why This Matters for Competitive Advantage

Cloud-based fine-tuning services create a problematic dynamic: your proprietary data enriches their foundational models. Every query, every fine-tuning job, every piece of data makes their platform smarter and more valuable - while you pay premium prices for the privilege.

With PremAI’s on-premise approach:

• Your data enriches only YOUR models, not competitors’
• Model improvements accrue exclusively to your organization
• Proprietary information never leaves your infrastructure
• You build a lasting competitive moat through specialized AI

Together AI’s cloud-based infrastructure requires sending your data to their servers for fine-tuning and inference. This creates data exposure risks, compliance complications, and strategic disadvantages that are unacceptable for many enterprises.

2. Built-In Compliance for Regulated Industries

Compliance Out-of-the-Box

Prem Studio has compliance baked in - the platform is built to help you meet GDPR, HIPAA, SOC 2, and other standards without custom engineering work.

For organizations in regulated sectors like finance, government, and healthcare, this capability is often make-or-break. Deployment within your AWS VPC or on-premises infrastructure keeps data governance requirements satisfied automatically.

Key compliance features:

• Automatic PII redaction with built-in privacy agents
• TrustML privacy-preserving framework with state-of-the-art encryption
• Zero data exposure to third-party systems
• Complete audit trails within your controlled infrastructure
• Air-gap capability for fully isolated environments

Real-World Compliance Success

European banks use PremAI to build compliance automation agents. These highly regulated institutions require absolute data sovereignty and cannot risk sending proprietary information to cloud AI services.

Together AI’s cloud-based model requires trusting external infrastructure with sensitive data - a non-starter for many compliance-first organizations.

3. Superior Economics: Predictable Costs vs Variable Pricing

The Cost Comparison

PremAI Inference Rates (per 1M tokens):

• All SLM sizes: $0.10 input / $0.30 output = $0.40 total

Together AI Alternative (typical cloud pricing):

• GPT-4o-mini equivalent: $6.00 total (15× more expensive)
• GPT-4o equivalent: $10.00 total (25× more expensive)

For an enterprise processing 10 million tokens monthly:

• PremAI: $4.00 total cost
• Cloud alternative (mini): $60.00 (15× more)
• Cloud alternative (full): $100.00 (25× more)

Long-Term ROI

Organizations processing 500 million tokens monthly reach breakeven with PremAI’s on-premise deployment in 12-18 months. After that, enjoy 50-70% sustained cost reductions compared to cloud alternatives.

The economics transform dramatically at scale:

• Predictable infrastructure costs replace variable API pricing
• No per-request fees enable aggressive AI scaling
• A 7B-parameter model costs 10-30× less per token on-premise vs cloud at enterprise volume

Together AI’s pay-per-use model creates unpredictable costs that grow linearly with usage, making it difficult to justify aggressive AI adoption across the organization.

4. Performance: Sub-100ms Latency for Real-Time Applications

The 300ms Threshold Problem

Users notice lag in AI applications above 300ms response time. Cloud AI services regularly exceed this threshold due to:

• Network latency from geographical distance
• API queueing during peak demand
• Multi-tenant resource contention
• Internet connectivity variability

PremAI’s On-Premise Performance Advantage

PremAI consistently delivers sub-100ms response times on-premise by eliminating network overhead and API dependencies. This 50% latency reduction enables real-time applications that are impossible with cloud-based solutions:

• Real-time fraud detection in financial transactions
• Manufacturing quality control with immediate feedback
• Interactive customer support with instant responses
• High-frequency trading applications
• Real-time healthcare diagnostics

Together AI’s cloud infrastructure introduces unavoidable network latency that makes sub-100ms performance unachievable for most deployments.

5. Autonomous Fine-Tuning: No ML Team Required

Multi-Agent Orchestration System

PremAI’s autonomous fine-tuning uses a multi-GPU system that handles the complete workflow without requiring machine learning expertise:

• Automatic model selection based on your dataset characteristics
• Hyperparameter optimization that finds optimal settings autonomously
• Data processing automation with 75% reduction in manual effort
• Performance predictions before committing compute resources
• Continuous evaluation with custom metrics defined in plain English

This autonomous approach delivers 8× faster development cycles compared to traditional manual fine-tuning processes.

Technical Capabilities Without Technical Debt

Two fine-tuning methods available:

1. LoRA (Low-Rank Adaptation)
   • 3× faster than full fine-tuning (10 minutes vs 30 minutes)
   • Produces lightweight adapter files
   • Significantly lower resource requirements
   • Best for quick adaptation with limited compute
2. Full Fine-Tuning
   • Complete parameter updates for maximum customization
   • 30 minutes to 2 hours typical duration
   • Produces standalone model weights
   • Best for fundamental behavior changes

Choose from 30+ state-of-the-art base models including Anthropic Claude, OpenAI GPT, Meta Llama, Alibaba Qwen, Google Gemma, Microsoft Phi, and specialized models like DeepSeek and SmolLM.

Together AI offers fine-tuning capabilities, but requires more manual configuration and lacks PremAI’s autonomous optimization system.

6. Complete ML Lifecycle Platform

End-to-End Workflow

PremAI provides an integrated platform covering the entire machine learning lifecycle:

Stage 1 - Datasets Module

• Upload/sync/generate fine-tuning datasets
• Convert PDFs, DOCX, YouTube videos, HTML, PPTX, URLs into model-ready formats
• Automatic PII redaction with privacy agents
• Synthetic data generation (10M+ tokens for enterprise)
• Dataset versioning via snapshots

Stage 2 - Fine-Tuning Module

• Choose from 35+ base models
• LoRA or Full fine-tuning methods
• Hyperparameter recommendations for finetuning
• Launch up to 4 concurrent experiments in a single job
• Interactive training metrics visualization

Stage 3 - Evaluations Module

• Multi-faceted evaluations beyond single metrics
• LLM-as-a-judge scoring system
• Custom evaluation metrics in plain English
• Side-by-side model comparisons
• Bias and drift detection

Stage 4 - Deployment Module

• Download model checkpoints for on-premise hosting
• Deploy with vLLM, Hugging Face, or Ollama
• OpenAI-compatible API endpoints
• Smart routing to best model versions
• Usage analytics with improvement suggestions

7. True On-Premise Deployment Architecture

Download and Deploy Anywhere

PremAI provides complete flexibility in deployment:

1. Download model checkpoints directly from Prem Studio as ZIP files
2. Choose your inference engine:
   • vLLM (recommended, OpenAI-compatible)
   • Hugging Face Transformers
   • Ollama for local testing
3. Deploy on your infrastructure:
   • Bare-metal clusters
   • AWS VPC or other cloud VPCs
   • On-premises data centers
   • Kubernetes via Prem-Operator

OpenAI-Compatible Integration

Deploy PremAI fine-tuned models with OpenAI-compatible APIs for seamless integration:

import os
from openai import OpenAI

client = OpenAI(
    base_url="<https://studio.premai.io/api/v1/>",
    api_key=os.environ.get("PREMAI_API_KEY"),  # This is the default and can be omitted
)
response = client.models.list()
print(response.data)

This makes PremAI a drop-in replacement for OpenAI APIs with zero code changes beyond the base URL.

Together AI requires ongoing API connectivity and doesn’t support true on-premise deployment with downloaded model weights.

8. TrustML Privacy-Preserving Encryption

Government-Backed Security Research

PremAI’s TrustML privacy-preserving framework delivers state-of-the-art encryption for secure fine-tuning and inference without compromising confidentiality.

This isn’t marketing rhetoric - it’s an engineered capability developed with:

• University of Applied Sciences and Arts of Southern Switzerland (SUPSI)
• Cambridge University
• Support from several European governments

TrustML enables secure operations on sensitive data while delivering both privacy and performance without trade-offs.

Automatic Privacy Protection

Built-in privacy agents automatically redact PII from training data, ensuring no confidential information leaks into model outputs. This is critical for finance and healthcare industries where data exposure could trigger regulatory violations.

Together AI’s cloud infrastructure cannot provide the same level of privacy assurance for organizations with strict data sovereignty requirements.

9. Small Language Models Strategy

Efficient Models for Enterprise Tasks

PremAI focuses on Small Language Models (SLMs) under 30B parameters rather than massive 175B+ parameter models. This “microservices for AI” approach delivers:

• Faster inference speeds on standard enterprise hardware
• Lower production costs with reduced compute requirements
• Edge deployment capabilities on consumer-grade hardware
• Specialized excellence for specific enterprise tasks

Knowledge Distillation

PremAI’s knowledge distillation approach uses large foundation models as “teachers” to create smaller specialized models:

• Achieve 60% model distillation without performance loss
• Fine-tuned Qwen 2.5 1B matched GPT-4o performance with far fewer parameters
• Fine-tuned Qwen 7B significantly outperformed GPT-4o for invoice parsing
• 25× cost savings vs GPT-4o with equal or better accuracy

This proves smaller specialized models can exceed larger general-purpose models for domain-specific tasks.

10. Proven Enterprise Success

Banking & Finance

European banks use PremAI to build compliance automation agents powered by Small Language Models. These institutions require:

• Regulatory compliance workflows
• Real-time fraud detection
• Complete data sovereignty
• Audit-ready infrastructure

Healthcare & Life Sciences

PremAI supports healthcare document processing with:

• HIPAA-compliant infrastructure
• Automatic medical code suggestion
• Clinical note processing
• Research data analysis

Media & Entertainment

Partnered with the world’s largest Animation Studio to build and deliver highly personalized video generation models with character-specific consistency.

Web3 Gaming

Zero (Web3 gaming company) chose PremAI’s on-premise solution specifically for client data privacy commitments, avoiding centralized vendors while successfully delivering personalized AI experiences.

11. Framework Integration & Developer Experience

Native Framework Support

PremAI integrates with popular AI development frameworks:

• LangChain: Native ChatPremAI class with full ecosystem compatibility
• LlamaIndex: PremAI and PremAIEmbeddings classes for RAG applications
• DSPy: LLM orchestration with optimizer support

Official SDKs

• Python SDK: pip install premai
• JavaScript SDK: via npm
• REST API: OpenAI-compatible endpoints

Together AI supports some frameworks, but PremAI’s OpenAI compatibility means it works seamlessly with the entire ecosystem without custom adapters.

12. Real-World Cost Comparison: Invoice Parsing Case Study

Performance + Economics

A real invoice parsing implementation demonstrated PremAI’s superior value:

Fine-tuned Qwen 7B with PremAI:

• Significantly outperformed GPT-4o in accuracy
• 25× cost savings vs GPT-4o
• Complete data privacy with on-premise deployment
• Sub-100ms response times

Fine-tuned Qwen 2.5 1B with PremAI:

• Matched GPT-4o performance
• Even more dramatic cost savings with smaller model
• Runs on standard enterprise hardware
• Enables edge deployment scenarios

This same principle applies to other domain-specific enterprise tasks: specialized fine-tuned models beat expensive general-purpose APIs on both performance and cost.

Why PremAI Wins for On-Premise Fine-Tuning

The choice between PremAI and Together AI for on-premise fine-tuning isn’t close:

Data Sovereignty: PremAI’s “Not Your Weights, Not Your Model” philosophy with downloadable checkpoints and zero-copy pipelines beats Together AI’s cloud-dependent infrastructure.

Compliance: Built-in GDPR, HIPAA, SOC 2 support with automatic PII redaction and TrustML encryption makes PremAI the only viable choice for regulated industries.

Economics: 50-70% cost reduction with 12-18 month breakeven for high-volume users, vs Together AI’s variable cloud pricing that scales unpredictably.

Performance: Sub-100ms latency with 50% reduction vs cloud alternatives enables real-time applications impossible with API-based solutions.

Autonomy: Multi-GPU orchestration eliminates ML expertise requirements with 8× faster development cycles.

True On-Premise: Complete deployment flexibility with vLLM, Hugging Face, or Ollama - no external dependencies vs Together AI’s cloud requirement.

For enterprises serious about AI sovereignty, regulatory compliance, predictable costs, and competitive advantage through specialized models, PremAI is the clear choice.

Get started with PremAI Studio or explore the documentation to see how on-premise fine-tuning can transform your enterprise AI strategy.

Frequently Asked Questions

Can I really download the complete model weights and run them without any PremAI dependencies?

Yes. After fine-tuning in Prem Studio, you download complete model checkpoints as ZIP files containing all weights and configuration files. Deploy these models using vLLM, Hugging Face Transformers, or Ollama on your own infrastructure with zero ongoing dependencies on PremAI services. The models are yours to keep, deploy anywhere, and use indefinitely. You can even upload them to Hugging Face Hub for distribution if desired.

How does PremAI’s autonomous fine-tuning actually work without ML expertise?

PremAI’s autonomous fine-tuning system uses multiple AI agents that analyze your dataset, recommend optimal base models, configure hyperparameters, run training experiments, and evaluate results automatically. You simply upload your data (or generate synthetic data from documents), choose Quick or Deep training, and the system handles everything else. It runs hyperparameter sweeps, generates performance predictions, and provides LLM-as-a-judge evaluations - all without requiring you to understand concepts like learning rates, batch sizes, or gradient accumulation.

What hardware do I need to run fine-tuned models on-premise?

For inference, you can run models on CPU, but GPU acceleration is recommended for production. Small models (0.5B-3B parameters) need 4-8GB VRAM, medium models (7B-8B parameters) need 16-24GB VRAM, and large models (70B+ parameters) need 40GB+ VRAM or multi-GPU setups. PremAI’s focus on Small Language Models means most enterprise use cases run well on standard NVIDIA A10, A100, or H100 GPUs. The vLLM deployment guide provides specific configuration recommendations for different model sizes.

How does PremAI’s cost structure work for on-premise deployment?

PremAI offers a free developer tier with 3 full fine-tuning jobs and 3 LoRA fine-tuning jobs per month, plus 1,000 inference requests. For enterprise, contact sales for unlimited fine-tuning, dedicated reserved GPUs, and on-premise/VPC deployment options. Once you download models and deploy on your infrastructure, ongoing inference costs are just your compute infrastructure - no per-token API fees. Organizations processing 500M+ tokens monthly typically reach breakeven in 12-18 months, then enjoy 50-70% sustained savings vs cloud alternatives.

Can I fine-tune models on proprietary data without it ever leaving my network?

Yes, through PremAI Enterprise deployment. You can run Prem Studio entirely within your AWS VPC or on-premises infrastructure. Your training data never leaves your network perimeter - the platform processes everything within your controlled environment using zero-copy pipelines. This is the “Not Your Weights, Not Your Model” philosophy in action: complete data sovereignty with automatic PII redaction and TrustML encryption. This architecture is specifically designed for banks, healthcare providers, and government agencies with strict data residency requirements.

Does PremAI support continuous fine-tuning as new data becomes available?

Yes. PremAI’s production monitoring includes continuous retraining triggers that automatically detect when model performance degrades or when sufficient new data accumulates. You can configure automatic retraining schedules or trigger fine-tuning jobs programmatically via the API. The Evaluations module provides bias and drift alerts that signal when retraining is needed. Combined with the autonomous fine-tuning system, this enables continuous improvement loops where your models automatically adapt to changing data distributions without manual ML intervention.

What makes PremAI’s approach better than just using open-source tools directly?

While you could use open-source tools like Hugging Face Transformers and vLLM directly, PremAI provides: (1) Autonomous optimization that eliminates ML expertise requirements and delivers 8× faster development cycles, (2) Built-in compliance with GDPR/HIPAA/SOC 2 and automatic PII redaction, (3) TrustML privacy-preserving encryption for secure fine-tuning, (4) Integrated dataset preparation with synthetic data generation from documents, (5) Comprehensive evaluation system with custom metrics in plain English, (6) Native RAG capabilities with built-in vector database, and (7) Production monitoring with traces and analytics. You get enterprise-grade capabilities without building and maintaining complex ML infrastructure yourself. Explore the full platform to see the integrated workflow.