Introduction
It’s no secret that DeepSeek* has turned the AI market on its head in the last few weeks. Helping to rout financial markets in what some termed the “Sputnik Moment” with a model that can be tuned at a fraction of the typical compute cost of tuning other known LLMs.
The effect of this could open the floodgates to the development of new open source models and a new faster and lower cost on ramp for data-driven enterprises looking to achieve their AI strategic and business goals, on their own terms, without being tied to any particular model provider (OpenAI, Mistral etc.).
The question is, how can enterprises take advantage of the DeepSeek market disruption to achieve their AI strategic and business goals on their own terms without being tied to a large model provider or working in a black box?
Other leaders in the model development space such as HuggingFace and Meta have been emphasizing the importance of open source models and cross-discipline collaboration to accelerate delivering on the grand vision for AI.
However, when it comes to enterprise AI, there should be no exception as enterprise AI developers and technical decision makers will start turning their attention and focus to the following topics to be able to advance their AI projects and initiatives:
- Community-driven innovation driving rapid development and continuous improvement of open-source models resulting in the commoditization of models for building AI products, applications and services.
- Control and customization of open-source models helping to meet unique enterprise needs, avoiding vendor lock-in while unlocking the value of their customer data faster than competitors. With flexible software tools for tuning, deploying, and managing any LLM, they can operationalize data insights while maximizing existing or future hardware investments.
As exciting as the potential of open-source models is, enterprises will have to overcome the roadblocks to successful implementations – the complexity of deploying these models, the potential large cost of inference associated with them, and the need to monitor the models to maintain efficacy.
In this blog we will highlight how Wallaroo enables enterprise AI teams to streamline the deployment and management of open source LLM’s on their own infrastructure, while maximizing inference performance.
The State of Enterprise AI today
Enterprises that have invested in AI initiatives face many challenges as they attempt to work with large language models which are, in majority of cases, proving too expensive for effectively running AI inference at scale resulting in projects being abandoned after the proof of concept phase.
With the emergence of more open source LLM’s, enterprises will turn to building their own AI infrastructure for custom LLM deployments , while managing for lean CAPEX budget constraints and AI ROI still to be proven out.
Additionally, as LLMs tend to come with imperfections and flaws out of the enterprise control such as hallucinations, bias, and harmful language in their outputs, which in turn can undermine customer trust and effectiveness or at worst compliance breach. As a result, enterprise AI teams may feel discouraged to launch GenAI projects without proper customizations or controls, thus delaying further learning opportunities and ROI consequently.
Looking ahead
Disruption brought about by DeepSeek has been a bonus for the open source community in that the community will in turn constantly refine, and improve models resulting in a wide variety of lighter open models, datasets and templates that enterprises will be able to grab “off-the-shelf”.
Enterprise Data Scientists and AI/ML Engineers will seek to deploy customized models with the enterprise’s proprietary data to deliver AI products and services.
Recognizing the infrastructure challenges mentioned earlier, AI teams will also look to overcome model performance and infrastructure challenges on their finite infrastructure (CPUs/GPUs)to lower the cost of LLM adoption and scale within the enterprise.
Wallaroo enables Enterprise AI teams to realize the infrastructure and operational efficiencies they are seeking for custom open source LLMs, through Wallaroo’s inference engine driving AI inference performance optimizations and a unified operations center to achieve efficient LLM deployment and management.
AI Inference Performance Optimizations
The Wallaroo AI inference engine offers two core features to help maximize custom enterprise LLM inference performance:
- Autoscaling: Reduces latency for LLM inference requests by automatically adding and scaling resources based on configurable triggers (e.g. request queue depth). It also controls resource allocation by configuring autoscaling windows to prevent sudden resource spikes and drops. This ensures that LLM production inference operates at optimal cost by only using resources when necessary.
- Concurrent batching: Improves throughput by dynamically grouping incoming inference requests in real time to optimize processing. It’s useful for real concurrent inference requests when LLM-based or agentic AI applications run at scale, balancing latency, throughput, and resource use. As a result of quick adaptation of hardware profiles to AI workloads and efficient inference processing, enterprises are able to maximize the return on their AI infrastructure investment.
With Wallaroo we can see the benefits of its’ AI inference 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 of all scenarios.
While we see significant improvements in throughput, Wallaroo also maintains a stable average inference latency for high load scenarios:
Efficient LLM deployment and management
Enterprise Data Scientist and ML engineers play a major role in the enterprise AI strategy when equipped with the right software tools.
Once models are fine tuned in development, they require robust AI operations with model packaging, deployment, observability and iterating on the models as they run in production, to ensure they drive tangible business outcomes on enterprise data.
Wallaroo has an Intuitive toolkit with a UI and SDK empowering AI teams with a unified AI control plane to autopackage, deploy and scale standard or custom AI inference microservices effortlessly, in the cloud, on-prem or at the edge. As a result, AI teams can take the crucial step from model tuning right into production without any heavy low level infrastructure manipulation.
The table below illustrates the benefits and time savings with Wallaroo for AI developers when packaging, deploying and scaling LLMs.
Once deployed, Wallaroo also enables AI developers to centrally monitor GenAI with Wallaroo’s LLM listeners ®, tune model accuracy, with solutions such as RAG, to act quickly on data drift situations. Specifically when working with LLMs there is the risk of the generated output containing hallucinations, bias or harmful language.
Finally, In situations where teams feel the need to update the underperforming deployed model they can rectify this easily and quickly use the Wallaroo capability of in-place model updates. This capability allows AI teams to “hot swap” the model in production with a better performing model in real time without the need to interrupt the inference operations. This helps ensure that model performance and uptime are maintained helping to drive AI inference continuity.
Conclusion
The benefits of open-source model commoditization for enterprises are clear: it allows them to take control of their GenAI strategy and set their business apart from their competitors without emptying the bank on costly infrastructure upgrades.
Enterprises can have the choice between working with closed source LLMs or host their own open source LLMs tuned on their own data on their existing infrastructure, with in-house AI expertise, leveraging Wallaroo’s flexibility, to optimize all aspects of AI production costs, and operationalize GenAI with comprehensive deployment, monitoring management capabilities, while maximizing AI inference performance on their existing finite infrastructure, in the cloud, on-prem or at the edge.
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*Wallaroo.AI does not endorse DeepSeek or Llama. They are used in this context as examples of open-source model commoditization.
