CentML
Accelerating Smarter AI Deployment for Enterprise Success

AI adoption is steadily gaining momentum across Canadian industries. While many organizations in the early stages of exploration recognize its potential benefits—such as automating routine tasks to free up employees for more strategic work—significant challenges persist in deploying generative AI models at scale. Typical hurdles include the high cost and limited availability of infrastructure, a shortage of specialized engineering expertise and the complexity of managing and scaling advanced AI systems.

Toronto-based CentML was founded by academic leaders and industry veterans to help companies break through these barriers and make progress in their AI journey.

"We believe that AI should empower businesses, not overwhelm them," says Gennady Pekhimenko, CEO. "CentML enables organizations of all sizes to leverage cutting-edge AI without extensive technical expertise or massive investments."

At its core, CentML democratizes enterprise AI deployment, making high-performance machine learning and inference viable at scale. It enables businesses to run advanced AI systems, including large language models and generative applications, with greater speed and flexibility and at dramatically lower cost.

CentML’s unified, vendor-agnostic platform maximizes hardware efficiency and accelerates inference and training across a wide range of environments. It supports a broad range of chips, including NVIDIA and AMD GPUs, Google TPUs, and Amazon Inferentia, giving teams the freedom to choose the most cost-effective or high-performance option for each workload. Switching between environments is seamless, requiring no code changes or architecture overhauls.

Whether running in the cloud, on-premises or across hybrid environments, CentML gives enterprises the agility to scale AI on their terms. This flexibility reduces cost-per-inference, shortens time-to-value and ensures full control over infrastructure strategy. It allows businesses to move their most promising use cases out of the lab and into real-world impact.

"Whether it's a large enterprise or a lean AI team, our platform is designed to help organizations move from prototype to production without friction," says Pekhimenko. "There's no need to be an AI infrastructure expert to achieve world-class performance anymore."

Building Scalable, Compliant AI Infrastructure Without Vendor Lock-In

Many organizations begin their AI journey with proprietary APIs from providers like OpenAI, Anthropic, or Cohere. These services offer a fast path to prototyping, but as adoption grows and use cases mature, the limitations of closed systems become apparent. Companies start questioning whether they can use their data, gain control over the models they deploy, or transition away from proprietary tools altogether. As priorities shift from quick experimentation to long-term scalability, control becomes essential.

CentML enables organizations of all sizes to leverage cutting-edge AI without extensive technical expertise or massive investments

CentML empowers enterprises to take the next step—on their own terms. The platform enables a seamless transition from third-party APIs to fully scalable, internally controlled AI deployments. Whether an enterprise wants to run open-source models like LLaMA, DeepSeek, Mistral, or Qwen, CentML offers a streamlined solution through standard open APIs built on Hugging Face.

CentML also offers native support for Retrieval Augmented Generation (RAG), allowing users to inject proprietary datasets directly into a model’s inference workflow without intensive fine-tuning. This makes it easy to build AI applications like internal knowledge assistants, customer service agents, and intelligent search systems that reflect an organization’s unique data—without the usual complexity or cost.

To support a variety of use cases and technical needs, CentML offers two flexible deployment options: serverless and dedicated environments.

For teams looking to move fast, CentML’s serverless deployment delivers instant access to preloaded models that are always on and ready to use, no contract required. Users can connect via API or leverage an intuitive UI to start building applications, including ChatGPT-style interfaces. The platform makes it easy to compare model performance in real-time, helping teams choose the best option based on speed, quality and token rates. Even teams without in-house AI expertise can get up and running quickly, with CentML as an embedded guide.

While serverless deployment is ideal for rapid prototyping, many enterprises require more control and customization. For these cases, CentML offers dedicated deployment options. Companies can choose their preferred model, set performance parameters and define their use cases. CentML then spins up a tailored, containerized environment that supports everything from model fine-tuning to custom workflow execution and application development. These deployments can run on CentML’s infrastructure or the customer’s systems, offering complete flexibility.

“Our architectural approach enables high utilization and performance without requiring custom tuning from users”

This level of customization is especially valuable for enterprises with strict performance or data residency requirements. Some organizations need to ensure that their AI workloads run exclusively in specific geographic regions, such as Europe or Canada, to comply with regulatory standards. In these scenarios, CentML works closely with the customer’s cloud provider to deliver compliant, location-specific solutions.

Unlocking Peak AI Performance at Minimal Cost

Informed, cost-effective deployment decisions are possible with the CentML Planner, a hardware-aware simulation tool that models performance and cost trade-offs across a range of configurations. Users input key parameters such as expected throughput, latency goals or budget and the CentML Planner recommends an optimized deployment strategy for their needs. This eliminates guesswork and ensures that AI workloads are matched with the most effective hardware setup from the start.

Once deployed, CentML’s proprietary LLM inference engine, CServe, delivers the performance needed to scale efficiently. CServe dramatically accelerates inference and reduces costs by applying advanced techniques such as speculative decoding, intelligent batching, flash attention, and parallelism. These methods significantly lower latency and boost throughput, allowing CentML to replace expensive third-party API calls with faster, more affordable alternatives.

For companies already using OpenAI-style APIs, switching to CentML is often as simple as changing a single line of code— everything else works the same, just more efficiently. For enterprises with high-throughput demands, CentML also offers dedicated endpoints tailored to specific workloads.

Behind this ease of use lies a deeply integrated orchestration of software and hardware layers. Open-source models, whether from Hugging Face, PyTorch, or elsewhere, must still be optimized to run on silicon. CentML bridges this gap with deep compiler-level engineering. Unlike conventional approaches that rely on generic vendor libraries, CentML generates custom chip-level code for each hardware configuration, ensuring consistent high utilization and performance, even outperforming some manually tuned alternatives.

At the foundational level, CentML takes optimization a step further by tuning the computational kernels themselves. Its architecture allows multiple models and tasks to run simultaneously, enabling inference and training to share the same hardware without compromising speed or accuracy. This architectural efficiency dramatically reduces operational costs.

“Our architectural approach enables high utilization and performance without requiring custom tuning from users,” says Pekhimenko.

Scaling AI Securely in any Environment

Security and compliance are paramount in enterprise AI, and CentML addresses these issues with utmost priority. Enterprises retain full autonomy over their AI models and data throughout every stage.

CentML rigorously adheres to industry-standard compliance certifications, including SOC 2 Type 1 and Type 2, ensuring comprehensive data protection. The platform never retains or accesses client proprietary data, enabling secure deployment across public cloud, private cloud, or on-premises environments. It follows cloud provider best practices and supports configurations that keep sensitive data fully isolated within the client-controlled infrastructure.

This level of security and deployment flexibility is especially critical for highly regulated industries like finance, healthcare and insurance. These organizations require strict data integrity, transparency and compliance. CentML meets those demands while offering a scalable, cost-efficient path to operationalize AI without compromising control or trust.

Turning AI Pilots into Scalable Wins

The real-world impact of CentML’s platform is reflected in successful deployments across industries, including a leading global retailer that set out to deliver personalized visual content to millions of customers across an enormous product catalog. Manually engineering such a solution was impractical, and running large AI models at that scale was prohibitively expensive. The company approached CentML with a challenge: “What’s the highest-quality model we can run on our existing hardware, and can you make it faster and more affordable?”

CentML took the retailer’s existing models and data and applied deep optimization across their workloads. In many cases, performance was improved by two to three times compared to industry benchmarks. The speed and efficiency transformed the project from a long-term aspiration into a production-ready solution. What started as a costly proof-of-concept quickly became a scalable reality, enabling the retailer to drive a highly personalized customer engagement strategy without breaking the bank.

But the benefits aren’t limited to enterprise giants. AI startups, often constrained by lean engineering teams and tight infrastructure budgets, rely on CentML as a full-stack infrastructure partner. Instead of building complex platforms from the ground up, they tap into CentML’s tooling to access top-tier performance, simplified deployment and built-in support for fine-tuning across a wide range of workloads.

CentML is also becoming a critical enabler for regional cloud providers and emerging chip vendors. Lacking mature software ecosystems of their own, these players integrate CentML’s orchestration layer to make their hardware accessible to a broader market, unlocking performance and value they couldn’t deliver alone.

Powering Tomorrow’s AI Breakthroughs

CentML is already at the forefront of AI’s next evolutionary stage: agent-based systems. These autonomous AI models interact seamlessly to perform complex tasks without human intervention, significantly improving operational efficiency, decision-making accuracy and resource management.

Unlike traditional LLM pipelines that rely on user prompts and one-off responses, agent-based systems coordinate multiple models to handle workflows such as document triage, contract review or customer onboarding. These systems require a tightly integrated stack, where multiple models pass information to each other in real-time.

CentML is actively building the tools to support this next wave of enterprise AI innovation. Its roadmap includes intelligent orchestration between models and dynamic scaling of hardware resources and performance monitoring across agent workflows. By bringing these capabilities together, it is setting a new standard for seamless, high-performance AI deployment.

Efficiency and strong technological foundations are critical in deploying advanced AI systems. Investing in powerful tools and deep expertise is vital, and CentML delivers exactly that to enterprises, equipping them to power the next generation of AI innovation.