Every task uses the most expensive model
Classification and structured extraction get the same frontier model as complex reasoning. You pay premium token rates even when a smaller model would do.
Production engineering
Enterprise AI cost optimization
Scale AI usage without letting costs scale unchecked. PhiGrad AI engineers model routing, caching, infrastructure and spending controls around your production workloads, with quality measured at every step.
20% to 50%
Target reduction in inference & compute spend
Workload-dependent, validated against quality and security requirements. Not a guaranteed saving.
The scaling dilemma
A promising pilot. An unpredictable production bill.
Low initial API spend can hide the economics of serving hundreds or thousands of users. Token consumption, repeated calls and dedicated GPU overhead grow, while finance and engineering struggle to forecast the next bill.
The objective is not simply to use a cheaper model. It is to lower the cost of each successful task and make AI spending predictable as adoption grows.
No limits on what the system can spend
Agent loops, recursive calls and traffic spikes run without spending caps, team quotas or circuit-breakers.
Too much context, too many repeated calls
Long system prompts, repeated documents and uncached queries turn avoidable token consumption into recurring cost.
Nobody can see where the money goes
An aggregate invoice hides the cost of individual features, teams and users. Without attribution, it is hard to know what to fix first.
The four optimization levers
Reduce waste at every layer of the AI stack.
We diagnose the biggest cost drivers, benchmark the alternatives and implement the changes that improve your unit economics.
Intelligent model routing
Match each request to the model it needs. Route routine work to smaller or specialized models and reserve frontier LLMs for complex reasoning.
- Task-complexity triage
- Frontier LLM and SLM selection
- Quality thresholds and fallback paths
Semantic caching & prompt compression
Reuse verified responses where it is safe, and remove context that does not improve the answer. Reduce redundant model calls and unnecessary tokens.
- Semantic response caching
- Context pruning and token deduplication
- Freshness, permissions and invalidation controls
Model & infrastructure right-sizing
Fit model size and serving capacity to real workload requirements. Reduce idle compute and over-provisioning, with quantization evaluated against your quality baseline.
- Model-size and quantization benchmarks
- GPU utilization and capacity tuning
- Inference throughput and latency optimization
Budget caps & AI FinOps governance
Make spending visible and enforceable. Attribute costs to the teams and features creating them, and stop runaway usage before it becomes a surprise invoice.
- Hard and soft budgets, team quotas
- Spend telemetry and anomaly alerts
- Agent loop limits and circuit-breakers
Inside your stack
Your models. Your infrastructure. Your controls.
Integrate optimization into the systems you already run, rather than replacing your application with a locked-in platform.
INPUT
Your apps & agents
Existing APIs and workflows
OPTIMIZATION LAYER
Budget → Cache → Route
Policy and quality thresholds
MODEL BACKENDS
Right-sized inference
Commercial APIs or self-hosted models
Fit your environment
Work with OpenAI- and Anthropic-compatible APIs, LangChain, LlamaIndex and existing gateways. Integration scope is assessed before implementation.
Respect your data boundaries
Support private deployment across AWS, Azure, GCP and hybrid environments. Provider access, retention and data flows are reviewed against your security requirements.
Keep ownership
Retain your routing rules, benchmarks and deployment configuration. Compare commercial providers and self-hosted models without tying the architecture to one vendor.
Business outcomes
Lower cost. Clearer economics. Measured quality.
Budget predictability
Spending policies, quotas and anomaly detection make usage easier to forecast and unexpected spikes easier to contain.
Unit economic visibility
Understand cost per query, successful task, user, feature and team, rather than relying on a single monthly total.
Quality preservation
Compare every optimization against agreed evaluation baselines. Track accuracy and reliability alongside savings.
IN PRACTICE · INSURTECH
Re-engineered an insurance claims pipeline to lower processing costs by 50%, with accuracy improving from 60% to 80%.
A specific project outcome, not a forecast for every engagement.
Delivery
From spending baseline to ongoing control.
We start with a scoping call to understand your infrastructure, models and current spend, then refine the approach around your workload.
01 · 2–3 weeks
Strategic discovery
Analyze API logs, token volumes, prompts, model deployments and GPU utilization. Establish the cost and quality baseline, identify waste and agree the optimization roadmap.
Prioritized opportunities, evaluation baseline and target savings.
02 · 2–5 weeks
Implementation & integration
Deploy routing, caching and context improvements inside your architecture. Configure budgets, quotas and telemetry, then benchmark the changes before rollout.
Tested optimizations and operational spending controls.
03 · Ongoing
Optimization & monitoring
Transfer knowledge to your team, tune routing and budget policies, and evaluate new models as prices and workload requirements change.
Continuous cost visibility, quality checks and model reviews.
Timelines are indicative. Final scope and timing depend on your environment and are agreed after scoping.
Questions
AI cost optimization, explained.
- What is AI cost optimization?
- AI cost optimization is the engineering of model selection, prompts, caching, inference infrastructure and spending controls to reduce the cost of running AI. The useful measure is cost per successful task, not just the price of a token.
- How much can you reduce our AI costs?
- Our offering targets a 20% to 50% reduction in inference and compute spend across suitable workloads. This is a target, not a guarantee. The opportunity depends on your existing architecture, traffic, model choices and quality requirements, and is refined after discovery.
- Will cheaper models reduce output quality?
- They can, which is why model price alone is not a selection criterion. We benchmark routing, caching, prompt changes and quantization against task-level evaluations and regression tests. Changes that fail the agreed quality thresholds are not treated as successful optimizations.
- Can you optimize our existing stack?
- Yes. We work with commercial model APIs and self-hosted models, existing gateways and orchestration frameworks, and cloud or hybrid infrastructure. Discovery establishes integration requirements, data boundaries and any application changes needed.
- How do you control runaway AI agent costs?
- Use a combination of per-request budgets, limits on tool calls and iterations, team quotas, alerts and circuit-breakers. Cost attribution and tracing help identify whether spending comes from legitimate usage, retries or looping agent behavior.
- How long does an engagement take?
- Indicative stages are 2–3 weeks for strategic discovery and 2–5 weeks for implementation and integration, followed by ongoing optimization and monitoring where needed. We refine the scope and timeline after understanding your infrastructure, models and spending baseline.
Make your AI economical to scale.
Tell us what you run, where costs are growing and what quality you need to preserve.