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 GPU machines and clusters, 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 dedicated GPUs. No shared infrastructure issues, rate limiting, or compliance concerns. Maintain complete data and model ownership.
Unmatched Cost Efficiency: Fine-tune and deploy at up to 1/5th the usual cost, with some of the lowest GPU prices on the market.
Support for Popular Open Source Models: Seamlessly deploy leading open-source models like Llama3, Qwen 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.
| Pros | Cons |
|---|---|
| ✅ 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.
| Pros | Cons |
|---|---|
| ✅ 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
- GPUs from 14+ clouds and 100+ datacenters at a fraction of the cost of big 3- AWS, Azure, GCP.
Security and Compliance
- Deploy LLMs on dedicated infra (with on-premise support)
- Zero data-flow outside your infrastructure
- Support for 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.