Introduction
In today’s competitive business landscape, organizations are eager to modernize their IT infrastructure by embracing the power of AI, particularly through the rapidly evolving world of Large Language Models (LLMs), Generative AI, and Agentic AI applications. However, many organizations face challenging hurdles when attempting to establish effective, secure, and scalable AI production operations within their own data centers.
The low barrier-to-entry appeal of public cloud AI applications and services is completely understandable for enterprises seeking to quickly initiate modernization through these GenAI and Agentic AI applications for pilots, feasibility experiments, and expertise development . However, forward momentum becomes constrained when organizations consider long-term strategic requirements over crucial production-grade aspects such as data privacy, risk management, and security prompting a transition of these workloads to on-premise solutions.
Challenges intensify when migrating from public inference endpoints such as OpenAI to private, on-premise, or even air-gapped environments, while striving for maximum inference performance at a low cost across diverse hardware infrastructures. To succeed in this strategic shift, AI teams need solutions that ensure the portability of AI Agents & applications without sacrificing performance, offering seamless hardware versatility across existing and new infrastructure, while providing comprehensive security and governance for the end-to-end Agentic AI application lifecycle.
In this blog, we will address how the Wallaroo Universal AI Platform rises to meet these rigorous demands, empowering enterprise AI developers to seamlessly transition their Agentic AI applications from public inference endpoints to their own secure infrastructure, while maintaining absolute control and maximizing performance.
Secure On-prem Agentic AI applications with Wallaroo
Agentic AI Deployment to Hybrid Environments with OpenAI Compatibility
When migrating and securing LLMs, GenAI and Agentic AI applications from public inference endpoints, managing the portability of AI models & agents to on-prem, air-gapped, or edge endpoints can significantly slow down the time to production. Any extension to the production process ultimately inhibits the return on value and compounds market lag.
Streamlining deployment is quick and efficient with the Wallaroo AI developer toolkit, which simplifies and automates the process of packaging and deploying AI inference microservices with any LLM whether the target production environment is on-prem, at the edge, or air-gapped.
Through the Wallaroo Auto-packaging capability, all necessary Agentic AI workflow steps are bundled into a single distributable package. LLMs are converted from any common or custom framework into an open format that Wallaroo uses to run the models. This can be done via the UI, SDK, and API depending on where the model artifacts are being managed after training for additional flexibility.
The benefit of Auto-packaging is that it helps ensure consistency, portability, scalability, and reproducibility across platforms and environments including secure air-gapped setups. This abstracts away the complexity of LLM onboarding leading to a modern streamlined deployment process, resulting in a reduction in time to production from months to hours or days regardless of the endpoint environment.
Learn how RealPage streamlined model deployment time from months to days .
Learn more: Deploy LLMs with OpenAI Compatibility
Agentic AI Inference Performance: Wallaroo AI Inference Engine
Migrating from OpenAI to on-prem endpoints often challenges desired outcomes through reduced inference performance of latency and throughput.
Fortunately with the Wallaroo Universal AI Platform, this challenge is less of a concern due to the foundational power unit of the platform: the Wallaroo AI Inference Engine, which helps deliver ultra-high-speed performance inference latency, and throughput, through a design for turnkey optimized inference that is agnostic to model types and hardware, and is future-proofed with seamless support for newer models and hardware acceleration, leading to rapid and efficient updates.
The rust-based Wallaroo Inference Engine features an optimized, multi-threaded inference serving and orchestration layer that manages incoming requests, prepares data, and executes models optimally. The Wallaroo AI Inference Engine Autoscaling and smart batching capabilities enable efficient hardware resource adjustment and maximum utilization at scale, ensuring optimal inference latency and throughput for AI applications and agents.
Additionally, for Agentic AI and reasoning LLMs, Wallaroo applies state-of-the-art techniques with multi-stage inference serving including KV Cache transfers across prefill & decode steps orchestrated across GPU nodes.
We can see the benefits and impact of the Wallaroo AI inference engine performance optimization capabilities first hand through the inference and throughput benchmark test results seen in the graph illustrations below, where Wallaroo has consistently the highest throughput performance in the scenarios tested.
This performance optimization is not only seen through significant improvements in throughput, Wallaroo also maintains a stable average inference latency for high load scenarios as seen below.
Hardware Versatility
For enterprise production AI, hardware versatility is not just a feature, it is a non-negotiable imperative for maximizing ROI and achieving strategic agility. In heterogeneous IT environments, a performant AI inference platform must seamlessly adapt to and optimize across a diverse range of hardware architectures, including various CPUs, GPUs, and specialized accelerators from different hardware vendors.
The advanced hardware versatility delivered by the Wallaroo ultra-fast inference engine kicks open the door for enterprises to modernize by leveraging and optimizing existing infrastructure investments, avoiding vendor lock-in, and running Agentic AI applications on the most cost-effective and energy-efficient compute resources available, whether on-prem, air-gapped, or at the edge. Ultimately, the Wallaroo hardware versatility ensures that Agentic AI applications can be run where the enterprise data resides, minimizing latency, enhancing data sovereignty, and providing the scalability needed to meet evolving business demands for high-volume, reliable Agentic AI applications.
This is accomplished through the Wallaroo platform’s ability to empower efficient, multi-step Agentic AI workflows through sophisticated CPU and GPU task orchestration. Achieved by offering granular and dynamic allocation of compute resources (both CPU and GPU) specifically tailored for each task within Agentic AI workflows. Furthermore, Wallaroo significantly reduces data transfer overhead across these complex workflow steps, ensuring that data moves efficiently between different processing stages, thereby maximizing overall performance and minimizing latency for demanding Agentic AI applications.
The benefit is not just from expanding hardware choice, but in achieving peak inference performance and optimizing total cost of ownership by precisely matching the workload to the right hardware, across x86, GPU, IBM Power (PPC) and Arm-based architectures thereby gaining sovereignty and control over enterprise AI production.
Learn more about Wallaroo Hardware Versatility:
- Wallaroo.AI Software on Arm®: A Best-of-Breed AI Solution
- Unleashing Enterprise Agentic AI with Wallaroo.AI and Ampere® Computing
- Optimizing AI Inference and Governance with Wallaroo on IBM® Power®
- Go, Llama. Go! Accelerate AI Inference on GPUs, No Compromise.
Control & Governance
Running Agentic AI applications on any hardware configuration with high volume token throughput, and low latency does not fully realize modernized operational gains if the AI team does not have the integrated tooling to control, secure and govern the end-to-end AI lifecycle.
This is where the Wallaroo AI Control Plane equips AI teams with effective, efficient control and collaboration tools. The Wallaroo AI Control Plane is specifically designed as a centralized AI operations center to simplify and automate the end-to-end production AI lifecycle. It streamlines everything from automated model packaging for rapid scale while simultaneously enabling continuous model delivery for reduced manual intervention.
Whether the AI application inference endpoints are on-prem, air-gapped, or at the edge, AI teams are empowered with integrated, intuitive tools and comprehensive control through centralized lifecycle management for secure workspaces and robust automated observability at scale, ensuring AI applications are healthy, perform optimally, and meet defined operational throughput and latency requirements.
Agentic AI Observability
The key to effective Agentic AI observability is the practice of evaluating and monitoring the health and reliability of Agentic AI applications and LLMs in production. This is crucial to maintain a tight feedback loop as application usage grows. To that effect, Wallaroo’s LLM ListenersTM offer a suite of tools to automatically scan Agentic AI application inferences for unwanted behaviors and results such as toxicity and harmful language, and enable AI developers to proactively address production issues.
The Wallaroo LLM ListenersTM can be easily orchestrated to generate real-time monitoring reports and metrics for understanding how the LLM or the overall Agentic AI application is behaving and ensure that it is effective in production allowing AI teams to iterate quickly without impacting the bottom line.
For example an alert can be generated for poor quality responses immediately or even autocorrect that behavior from the LLM that can be done in-line. The Wallaroo LLM ListenersTM can also be orchestrated to generate real-time monitoring reports and metrics to understand how Agentic AI applications are behaving and ensure they are effective in production. AI teams can also iterate on the LLM Listener and keep the endpoint static while everything that happens behind it can remain fluid to allow AI teams to iterate quickly on the LLMs without impacting the bottom line which could be business reputation, revenue costs, customer satisfaction, ROI, etc.
Learn more about Wallaroo LLM ListenersTM: How to Monitor LLMs With Wallaroo LLM ListenersTM
Lifecycle Model Management
Centralized model management in Wallaroo is anchored in the concept of workspaces. Wallaroo Workspaces are a supercharged version of the typical model registry helping AI teams scale their deployments by adding more control, context and security around their model rollouts. The workspaces concept is used by enterprises to bring flexible, organized rollouts by market, region, location, depending on the use case. This delivers comprehensive security & governance for full lifecycle management and control of AI application inference endpoints.
Continuous Model Delivery
Through centralized AI Workflow Automation, AI teams have the autonomy to ensure that AI applications are always up-to-date for performance optimization. Workload orchestration empowers AI teams by reducing manual deployment, errors, risks and delays, saving valuable time and resources in the process by effortlessly constructing automated workflows that continuously operate their inference pipelines. The scale and flexibility that this delivers means that workflows can be scheduled, or run as a continuous service until terminated. The benefit is that AI teams have control for rapid prototyping of AI workflows and scaling both workload and pipeline resources to manage demanding production tasks.
Model Swapping
An important capability for managing models in production at scale, data scientists have the flexibility to choose different rollout strategies such as A/B testing, Canary deploys, Shadow deploys, and Blue/Green methods to identify the best performing models to run. Once that decision happens, swapping an existing production model with a better performing model can take place in seconds through in-line model updates and without any inference interruption.
RAG (Retrieval-Augmented Generation) for Continuous Agentic AI Refinement
Driving IT modernization through Agentic AI application adoption within an organizations’ own data centers, RAG customization emerges as a pivotal method for establishing effective, secure, and scalable AI production, especially in on-premise and air-gapped environments. This level of customization is vital for maintaining data sovereignty and compliance in sensitive sectors, allowing organizations to bring powerful AI applications in-house while precisely controlling data flow, maximizing inference performance on diverse hardware and securing their valuable intellectual property, thereby realizing significant competitive advantages.
In addition, for effectively tailoring AI applications to specific organizational needs, RAG is pivotal for establishing effective, secure, and scalable Agentic AI production. especially in on-premise and/or air-gapped environments. Wallaroo, with the ability to customize and deploy RAG pipelines for various vector embedding strategies, in addition to seamlessly integrating private vector databases, provides a flexible and robust RAG solution driving effective Agentic AI applications in production.
Learn more: On-prem Agentic AI with Wallaroo and MongoDB® on IBM® Power®10
Conclusion
As organizations aggressively pursue IT modernization with AI and Agentic AI applications, the shift from public cloud endpoints to secure, controlled environments with hybrid infrastructure is imperative. The Wallaroo Universal AI Platform provides the complete solution for this complex transition, addressing critical challenges around orchestrating the deployment, inference and management of AI agents & applications across diverse infrastructure & silicon.
Post-deployment orchestration via the Wallaroo AI Control Plane intelligently allocates and optimizes the utilization of underlying hardware resources, ensuring efficient usage combined with high performance for diverse workloads. Wallaroo’s underlying orchestration capabilities streamline complex multi-stage inference steps to ensure there is no overhead for inference serving. Such steps include: load balancing, data input and preprocessing, prefill & decode across different hardware nodes, in addition to autoscaling and smart batching for realtime and offline inference workloads .
When it comes to production AI deployment & maintenance, Wallaroo’s workload orchestration empowers developers to automate AI deployment, management and monitoring tasks, which results in saving valuable time and resources in launching new AI initiatives, while ensuring proactive governance of their AI pipelines. As a result, organizations are able to scale AI solutions seamlessly from development to secure, production-grade deployment across hybrid, on-prem, and air-gapped environments.
Through seamless deployment & migration, high-performance inference, and comprehensive lifecycle management via the AI Control Plane (including unified management, automated observability, and continuous delivery), Wallaroo empowers AI developers and enterprises to fully orchestrate Agentic AI inference across hybrid infrastructure, ultimately driving significant competitive advantage and maximizing ROI.
Additional Resources
