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ScaleGenAI At A Glance

Open Source LLMs at Scale | Private And Secure | 3x-9x Lower Cost​

ScaleGenAI is built to build business-critical genAI applications that prioritize data security and sovereignty. Deploy popular open source LLMs privately and securely on your dedicated compute (on-premise or VPCs), at a fraction of the cost—3x to 9x cheaper than traditional methods.

Scalability for Production: ScaleGenAI’s rapid elastic auto-scaling ensures LLM deployments can dynamically adjust to demand, delivering guaranteed SLAs and reliability.

Private LLMs on Dedicated Compute: Deploy on-premise or on any cloud—without shared infrastructure issues, rate limiting, or compliance concerns, maintaining full data and model ownership.

Unmatched Cost Efficiency: Fine-tune and deploy at up to 1/5th the usual cost. ScaleGenAI leverages spot instance failover, multi-cloud strategies, and heterogeneous GPU cluster support to optimize your spend.

Support for Popular OSS Models: Seamlessly deploy leading open-source models like Llama2 and Mistral, tuned to your requirements.

Challenges Users Face When Deploying Generative AI Applications​

Generative AI applications in production come with typical challenges.

High Cost of Operation: Deployment on on-premise or dedicated compute incurs high infrastructure setup and management costs. Managed, proprietary LLM APIs come with a premium pricing.

Scalability of Deployments, and Availability of Compute Resources: Compute quota restrictions, rate-limiting, and throughput-limiting can result in reduced scalability and compromised SLA performance in production settings.

Strict Requirements for Data Privacy and Compliance: Shared compute and data storage on managed platforms raises privacy and governance concerns. LLM security is a constant, continuous effort.

Customization and Control Over the Model Output: Continuous performance and system optimizations are required in addition to constant AI R&D efforts.

These are the common challenges organizations face while trying to find the perfect solution for deploying their generative AI applications.

Pros and Cons of Current Deployment Options​

Proprietary LLM Models​

Proprietary LLMs are good for POCs and initial setup, but don’t scale in production due to high cost of operation and security concerns.

ProsCons
✅ Easy to operate and setup❌ Expensive to operate
✅ Generally better model performance and accuracy❌ Lack of customization and flexibility
✅ Managed infrastructure and security❌ Rate limiting and quota restrictions on shared compute
❌ Data privacy and compliance concerns

Managed-Shared Infrastructure Providers | Open Source LLM-as-a-service Providers​

Multiple users operate on a shared compute and base LLMs deployed across multiple GPUs. Managed-shared deployments are apt for non-business-critical applications where scale, service availability, and data security are not a priority.

ProsCons
✅ Open source LLM support❌ Limited customizations and flexibility
✅ Managed infrastructure❌ Rate limiting and quota restrictions on shared compute
✅ Cheaper cost of operation❌ Data privacy and compliance concerns
❌ Shared compute leads to variable performance– impossible to deliver SLAs

Dedicated Infrastructure​

Private deployments on dedicated compute offer the reliability that’s essential for business-critical applications that require effective scaling. Ideal for organizations that value privacy and control.

Pros
✅ OSS model support❌ Requires complex infrastructure setup
✅ Complete customization and deployment flexibility❌ Manual LLMOps and security handling
✅ Private LLM deployments offer better data security and governance❌ High initial cost of setup

ScaleGenAI Offers The Advantages of Dedicated Infrastructure At 3x-9x Lower Cost, Without Any Drawbacks​

ScaleGenAI addresses the limitations and challenges of LLM deployment on dedicated infrastructures. We scale while providing security and privacy for open source LLMs on dedicated infrastructure.

Our feature offerings include:

Maximised Compute Availability​

  • Flexible choice of infrastructure
  • Single job can be scaled across multiple clouds and on-premise machines
  • Cloud-burst support

Scalability and Provisioned Throughput​

  • Elastic auto-scaling based on latency and throughput requirements
  • Rapid scaling in under 1-minute
  • No rate-limiting or throughput-limiting

Cost Optimization​

  • Spot instance automation
  • Multi-cloud strategies for cheaper compute
  • Support for cheaper tier-2 and tier-3 clouds
  • Scale-down-to-zero support in no-request scenarios
  • Heterogeneous GPU cluster and consumer-grade GPU recipes

Security and Compliance​

  • Deploy LLMs on dedicated on-premise and cloud infra
  • Zero data-flow outside your infrastructure
  • Support for AWS, Azure, GCP or custom API gateways

Easy Integrations​

  • OpenAI SDK compatible APIs
  • Support for all HuggingFace models
  • One-click switch from shared-to-private LLMs

For detailed instructions and guidance, please refer to the subsequent sections of this documentation.