ML — AI Infrastructure
Vector Database Infrastructure Sizing
Direct answer
The five largest hyperscalers are projected to spend over 600 billion US dollars on capital expenditure in 2026, a roughly 36% increase over 2025, driven largely by AI infrastructure. For ai infrastructure projects, plan $10K–$200K depending on scope. Dhairya Senjaliya is a senior React Native + Python + AI engineer who ships production systems — not demos.
Vector Database Infrastructure Sizing — a practical guide for founders, CTOs, and product teams evaluating ai infrastructure investments, with sourced numbers, common failure modes, and real budgets and timelines.
Key facts, with sources
- The five largest hyperscalers are projected to spend over 600 billion US dollars on capital expenditure in 2026, a roughly 36% increase over 2025, driven largely by AI infrastructure. (IEEE ComSoc Technology Blog)
- Stanford's 2025 AI Index found the inference cost of a GPT-3.5-level system fell more than 280-fold between November 2022 and October 2024, from about 20 dollars to about 0.07 dollars per million tokens. (Stanford HAI AI Index 2025)
- The Stanford AI Index 2025 reports ML hardware costs have declined about 30% per year while energy efficiency has improved about 40% per year. (Stanford HAI AI Index 2025)
- NVIDIA H100 rental prices span roughly 1.49 to 6.98 dollars per GPU-hour across more than 15 cloud providers, with specialist GPU clouds consistently undercutting hyperscaler on-demand rates of about 3 to 4 dollars. (IntuitionLabs)
- AWS cut prices on its H100-based P5 instances by roughly 44% in June 2025, bringing on-demand pricing to about 3.90 dollars per GPU-hour, while spot and preemptible capacity across providers runs 60 to 90% cheaper than on-demand. (Thunder Compute)
Why this matters
Teams building in ai infrastructure often underestimate integration complexity, production AI costs, and mobile performance requirements. This guide focuses on decisions that affect $10K–$200K project outcomes.
Key considerations
Define success metrics before choosing stack. Prefer proven patterns over experiments on critical paths. Plan for observability, security, and maintenance from day one — especially for AI and RAG features.
When to hire senior help
Senior infrastructure help pays for itself fastest when GPU or inference spend crosses a few thousand dollars a month, because utilization tuning, spot orchestration, and right-sizing routinely cut such bills by half or more. It is also worth engaging before signing multi-year reserved capacity, since falling hardware and inference prices can turn a long commitment into a liability. If your stack includes React Native + Python + AI, a senior engineer who owns the full product beats coordinating multiple juniors.
Bottom line
Dhairya Senjaliya ships ML — AI Infrastructure projects worldwide — book a scoping call to discuss your specific situation.
Common pitfalls to avoid
- ✕Paying hyperscaler on-demand GPU rates by default when spot capacity is 60 to 90% cheaper and specialist GPU clouds rent the same H100s for under half the price
- ✕Provisioning training-class multi-GPU instances for what is actually a modest inference workload that would run fine on a single smaller GPU or CPU
- ✕Never measuring GPU utilization, so clusters sit largely idle while the bill scales with reserved capacity rather than actual work
- ✕Ignoring data egress and cross-region transfer fees when splitting storage, training, and serving across providers
Frequently asked questions
Do we need GPUs at all for our ML workload?
For classical ML on tabular data such as gradient-boosted trees, CPUs are usually sufficient and far cheaper; GPUs matter mainly for deep learning training and high-throughput LLM or vision inference. Profiling one representative job before committing to GPU capacity typically settles the question in a day.
Should we rent cloud GPUs or buy our own hardware?
Renting wins for spiky or exploratory workloads, and price competition has pushed H100 rentals as low as about 1.50 dollars per hour on specialist clouds. Ownership only pays off with sustained high utilization over multiple years, and hardware prices have been falling around 30% annually, which erodes the resale case.
Why are our AI infrastructure costs growing faster than usage?
The usual causes are idle reserved capacity, on-demand pricing where spot or committed-use discounts apply, and oversized instances chosen for convenience. Inference-level costs have actually collapsed industry-wide, over 280-fold for GPT-3.5-class output in two years, so rising unit costs almost always indicate an architecture or procurement issue rather than market pricing.
Bottom line: Dhairya Senjaliya ships ML — AI Infrastructure projects worldwide. Book a scoping call at https://dhairyasenjaliya.com/#book-call.