Paying Too Much to Run Your GenAI Models?
We Can Help You Slash Costs

We help you optimize your GenAI stack to significantly reduce costs without compromising performance. By focusing on inference optimization, efficient resource utilization, right-sizing, and tuning for high goodput, we ensure every dollar spent delivers maximum value. Our clients save up to 60% on their GenAI infrastructure through intelligent, data-driven cost strategies.

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How We Help You Reduce Your
GenAI Deployments Costs

Optimizing GenAI workloads requires more than just turning off idle resources — it demands a deep understanding of model behavior, infrastructure efficiency, and throughput economics. Our approach blends system-level optimization with model-aware tuning to deliver sustained cost reductions across inference, training, and deployment pipelines.

Inference Optimization: We reduce per-request compute cost by selecting the most efficient model size for the task, enabling mixed-precision inference, and leveraging optimized runtimes like Bud Runtime. We also integrate caching layers and batch processing to optimize token costs across calls.

Resource Efficiency & Autoscaling: Through fine-grained telemetry, we identify underutilized GPUs, CPU bottlenecks, and memory overhead across your stack. Our system recommends and enforces intelligent autoscaling policies, spot instance utilization, and horizontal vs. vertical scaling based on real-time demand patterns.

Right-Sizing & Goodput Maximization: We analyze your workload characteristics to match model deployment sizes (parameter count, quantization level) with use-case precision needs. By optimizing for goodput (useful tokens/sec per dollar) rather than raw throughput, we ensure you're not overpaying for excess compute that doesn't translate into actual business value.

GenAI Cost Optimization
Strategies

Optimal Model Selection and Right-Sizing

Opt for domain and task specific fine-tuned Small Language Models instead of large models. Use quantized models to cut compute costs and reduce model size, maintaining accuracy while optimizing efficiency. These approaches balance performance with resource savings, especially during inference.

Hybrid Inferencing with SLMs and LLMs

Hybrid inferencing combines Small Language Models (SLMs) on local hardware with Large Language Models (LLMs) in the cloud. By evaluating each generated token’s quality, it selectively uses the LLM only when necessary. This approach balances cost and performance, ensuring efficient, high-quality AI outputs while reducing cloud dependency.

Heterogeneous Hardware Parallelism

Heterogeneous hardware inferencing uses a mix of CPUs, commodity GPUs, and high-end GPUs to serve large language models efficiently. By splitting tasks across available hardware, it reduces costs, improves resource utilization, and maintains performance—offering a smarter, more scalable alternative to GPU-only deployments.

Prompt Engineering and Optimizations

Optimize prompts to minimize token usage by keeping them concise and efficient. Long prompts increase costs. Use system-level instructions strategically—reuse system prompts when possible and avoid redundancy in API calls to enhance performance and reduce resource consumption.

Caching and Response Reuse

Cache common queries and responses to reduce repeated GenAI API calls and improve efficiency. Use deterministic prompting—structure prompts to yield consistent outputs—so cached results remain reliable and reusable. This approach lowers costs and enhances performance.

Monitoring, Analytics, and Governance

Monitor usage and costs by user and task to identify high-cost areas. Set thresholds and alerts to stay within budget. Regularly audit and refine model use based on performance, usage patterns, and business needs to ensure efficient and aligned GenAI operations.