AI is working. AI costs aren't.
We move the right workloads off expensive managed cloud and onto private or hybrid infrastructure - without giving up latency, reliability or compliance. It starts with an assessment of what you run today and what each workload actually costs.
Easy to prototype. Hard to run cost-efficiently at scale.
Moving fast with AI usually means defaulting to managed cloud services and proprietary APIs. That works — until the invoice starts scaling with usage rather than with value.
Multiple model endpoints, underutilised GPUs, fragmented workloads and opaque pricing add up to runaway spend that nobody owns. We evaluate what you run today across cloud, on-prem and everything between, then rebuild the parts where the economics have broken.
Actual impact depends on your current setup, scale and workloads. We quantify the potential savings up front, during the assessment, rather than after the work.
Five stages, assessment through execution.
1 · Holistic cost & architecture assessment
We map your AI workloads, infrastructure and cost profile — model endpoints, orchestration, storage and the rest.
- Identify idle and underutilised GPUs and CPUs
- Analyse per-workload cost against performance
- Surface both quick wins and deep structural issues
2 · Cloud-to-private / hybrid migration
Not every workload belongs on hyperscale cloud. We decide what stays and what moves.
- Design private clusters for inference and training
- Evaluate GPU, CPU and accelerator options for cost/performance
- Plan staged migrations that avoid downtime and surprises
3 · Workload & model-level optimisation
We optimise how models run, not only where they run.
- Right-size models and quantisation strategies per use case
- Batching, caching and scheduling for higher throughput
- Consolidate endpoints and cut over-provisioning
4 · FinOps, observability & guardrails
You cannot optimise what you cannot see.
- Per-team, per-workload and per-model cost reporting
- Alerting on anomalies and cost regression
- Policies that keep spend under control over time
5 · Execution support & change management
We work alongside your teams to implement the architecture, not hand over a deck.
- Hands-on support for infrastructure setup and migration
- Collaboration with security, compliance and finance
- Playbooks and training so your team can run the new stack
Where we work.
Cost-aware AI architecture
- Platforms across cloud, private and hybrid
- Infrastructure choices aligned to CFO and CTO priorities
- Performance, compliance and TCO balanced explicitly
Cloud-to-private infrastructure
- Managed cloud AI to self-hosted and private
- Enterprise hardware vendors and colocations
- Migrations with rollbacks and blue-green cutover
Performance & workload engineering
- Profiling against real workloads, not synthetic benchmarks
- Throughput, latency and concurrency tuning
- Hardware-aware scheduling and capacity planning
Enterprise-grade delivery
- Integration with existing MLOps, security and data platforms
- Collaboration across infra, platform and finance
- Continuous support as usage and models evolve
Four people usually start the conversation.
Why Bud
We sit at the intersection of LLM infrastructure and deployment, open-source and self-hosted model serving, hardware-aware optimisation, and MLOps for AI systems. The goal is not a smaller invoice next month — it is a stack that stays efficient as you scale.
Start with a cost assessment.
Share your current AI setup and we will come back with a high-level TCO view and the optimisation options, before any commitment.