GenAI as a Service For Enterprises

Understanding GenAI as a Service

For platform engineers, GenAI as a Service means building a system that allows different teams—data scientists, application developers, and business users—to seamlessly access, deploy, and experiment with AI models without worrying about infrastructure and operational bottlenecks.

While the idea of GenAI sounds exciting, the reality is platform teams are under immense pressure to deliver a scalable, cost-efficient, and secure AI infrastructure. They face tight deadlines, evolving enterprise needs, and rapidly changing AI models, making GenAI deployment a constantly moving target.

The Core Challenge: Model Proliferation and Infrastructure Complexity

One of the biggest headaches for platform teams is that models are becoming a commodity. Every few weeks, new and improved LLMs, embedding models, and rerankers, etc are being released. Business teams want to integrate them immediately, but this creates a nightmare for infrastructure planning.

• How do you swap in and swap out LLMs without disrupting existing applications?
• How do you ensure different teams get access to the right model without duplicating efforts?
• How do you keep models running cost-effectively when GPU resources are limited?

Enterprises need a centralized system that abstracts these complexities, allowing teams to consume AI services without breaking infrastructure.

Challenges in Operationalizing GenAI as a Service

1. Model Deployment Hurdles

Deploying GenAI models internally is far more complex than running a standard software application -

1. Support for diverse models
   
   1. Support for multiple Open-source models (e.g., Llama,) and proprietary APIs (e.g., OpenAI, Anthropic) models . 
   2. Enterprises need to support various models such as embedding models, rerankers, etc for different tasks.
2. Multi-Cloud and On-Prem Deployment: Enterprises need flexibility to deploy models across cloud providers (AWS, GCP, Azure) or on-premise based on cost, compliance, and GPU availability‍
3. GPU Orchestration is Non-Trivial: Kubernetes, Ray, and Slurm are often required to dynamically allocate GPUs. Also, switching between providers (e.g., from AWS A100 to GCP TPU) requires custom work. ‍
4. Containerization and Orchestration: Without containerization of models, teams struggle with dependency mismatches, software conflicts, and versioning issues.It also provided added benefits of auto -scaling, GPU scheduling, fault tolerance etc which are pretty important in production environment.‍
5. Deploying on Different Infra Configurations: Some workloads require ultra-low latency for production, while development and experimentation can tolerate higher latencies.
   Example: A company might need two different instances of Llama—one running efficiently on T4 or A10G GPUs for cost-effectiveness, while another runs on H100 GPUs for high-priority, latency-sensitive applications.‍
6. Integration with Model Registries: Organizations often maintain multiple model registries (e.g., MLflow, SageMaker, Hugging Face), requiring seamless integration for version control and auditing.‍
7. Handling Fine-Tuned Models: Data scientists frequently fine-tune models, and platform teams must ensure these models are deployed efficiently and securely. 

2. Enabling Secure and Scalable Inferencing

Once deployed, the challenge shifts to making these models available for inferencing across various enterprise applications. 

1. Access Control on Models: Defining RBAC (Role-Based Access Control) to manage model access based on teams or users 
2. APIs & Standardization: Enabling teams to easily create inferencing endpoints and swap in/swap out multiple LLMs through a self-serving portal.
3. Custom Quotas & Rate Limiting: Defining quotas on model usage at the user, team, or organizational level to ensure fair resource allocation.
4. Failover Mechanisms: Implementing fallback solutions to prevent production outages, such as automatically switching to another model provider (e.g., OpenAI to an alternative model).
5. Semantic Caching: Leveraging caching strategies to ensure that similar queries do not require redundant computation, improving efficiency.‍
6. Observability of Model Usage: Capturing all user requests, model responses, and API calls for governance, debugging, and billing.

3. Observability & Governance 

GenAI models are not static; they need continuous evaluation and improvement. Platform teams struggle with:

1. GPU Availability & Usage Insights: Offering transparency into GPU utilization to optimize resource allocation.
2. Logging and Debugging: Capturing all usage metrics, including user prompts and model outputs, for better tracking and analysis.
3. LLM Benchmarking: Providing empirical data on LLM performance to ensure that chosen models meet the desired quality and reliability standards of the enterprise.
4. Security Guardrails: Integrating with pre-defined or custom guardrails to avoid exposure of PII data and other sensitive information ‍
5. Key Management Complexity: Managing API keys, secrets, and authentication across different cloud environments adds security risks and operational overhead.

How TrueFoundry Enables GenAI as A service 

TrueFoundry provides an end-to-end AI infrastructure platform that simplifies model deployment, inferencing, and governance—allowing platform teams to focus on scalability, efficiency, and security rather than infrastructure bottlenecks.‍

The All-in-One Platform for Unified Deployments

1. TrueFoundry offers a Kubernetes-native AI platform that automates model deployment and infrastructure management, eliminating the need for manual configuration.
2.  Cross cloud/On prem support - With multi-cloud and on-prem support, enterprises can deploy models on AWS, GCP, Azure, or private data centers without additional operational overhead. 
3. Supports deployment of models across diverse Model Frameworks,Types, and Servers. Also suports deployment of embedding, reranker models.
4. The platform automatically selects the best Kubernetes deployment configuration based on model architecture, GPU availability, and throughput requirements. 
5. TrueFoundry also optimizes infrastructure by providing auto-scaling capabilities that reduce model scaling time by 3-5x, significantly lowering cold-start delays. 
6. Also supports  advanced features like image streaming, sticky routing for LLMs, and intelligent GPU recommendations
7. Additionally, TrueFoundry enables self-serve model deployment, allowing data scientists to deploy models without Kubernetes expertise, reducing dependencies on platform engineers and accelerating AI adoption across teams.
8. Full Gitops support to ease the lives of platform teams

Unified & Scalable Model Inferencing

1. TrueFoundry simplifies model inferencing by providing a centralized AI Gateway, ensuring seamless access to models across different cloud environments. 
2. With a single API, platform teams can manage open-source models (Llama), commercial solutions (OpenAI, Bedrock, Mistral), and enterprise fine-tuned models. This unification ensures consistent inferencing experiences across worklflows. 
3. It also supports rate limiting to ensure quotas across users/teams/models, load balancing, and automated failover to prevent disruptions.In case of service outages or performance degradation, models can seamlessly fall back to alternative providers without manual intervention. 
4. Additionally, semantic caching reduces redundant computations, optimizing response time and reducing operational costs.
5. TrueFoundry also natively integrates reranker and embedding models, making it easier to build retrieval-augmented generation (RAGs) a common use case

Observability, Security & Governance

1. Platform teams can track model usage in real time, monitor who is invoking which models and how often, and analyze system performance to optimize resource allocation.
2.  The platform offers detailed logging and debugging tools, enabling engineers to trace issues efficiently, reducing downtime and improving reliability.
3.  Security is a core focus, with centralized API key management, preventing unauthorized access and ensuring that authentication processes remain secure across cloud environments. TrueFoundry also ensures enterprise-grade data privacy by deploying all AI workloads within the organization’s VPC infrastructure, eliminating risks of external data exposure. 
4. Additionally, the platform seamlessly integrates with guardrails such as Nemo guardrails, Arize etc, for PII detection, etc