AI optimization and performance services for healthcare AI workloads - cut inference, GPU, and platform spend by 40–70% without giving up accuracy or compliance posture.

Book an Intro Call Common Questions

6 levers

Cost levers in a healthcare AI workload

4 weeks

Fixed-scope cost and performance teardown

12–24 wks

Typical optimization implementation engagement

40–70%

Typical savings versus efficient cost profile

If you're operating production AI in a healthcare environment without dedicated performance engineering, you are almost certainly leaving real money on the table. Here is what we typically find in a teardown.

Cumulatively, mid-market healthcare AI programs typically run 40–70% above their efficient cost profile. At enterprise scale, the gap is measured in seven figures annually.

Pricing

What an Unoptimized Healthcare AI Workload Actually Costs You.

Pricing · 01

Inference spend 2–4x larger than required

Models running at higher precision than the use case needs. Context windows used inefficiently. Cache layers absent. Token budgets uncapped.

Pricing · 02

GPU utilization at 18–32%

Provisioned for peak, paid for 24/7. Reserved capacity locked in for instance families that have been superseded.

Pricing · 03

Vendor markups absorbed without negotiation

Foundation model API spend, embedded vendor AI features, and managed inference services priced 40–60% above viable alternatives.

Pricing · 04

Redundant model paths

Two or three teams running similar models against similar data because no central platform exists to share inference, embeddings, or evaluation infrastructure.

Pricing · 05

Latency taxing the user experience

Clinical workflows where a 4-second AI response means a clinician disengages - and the AI investment quietly underperforms its business case.

Pricing

The Six Cost Levers in a Healthcare AI Workload.

Model selection and right-sizing. Most production workloads are running models that are bigger than the task requires. The right model at the right precision is usually the single largest cost lever.

Pricing

Inference architecture. Batching, caching, speculative decoding, distillation, quantization, KV-cache management - the engineering layer that turns a model into a workload.

Pricing

Infrastructure and GPU economics. Instance family selection, reserved vs. on-demand mix, spot strategy where compliance allows, GPU sharing patterns, regional cost arbitrage.

Pricing

Foundation model vendor strategy. Negotiated pricing, multi-model fallback, open-weight alternatives where the workload tolerates them.

Pricing

Data efficiency. Right-sized context, smarter retrieval, deduplication, prompt compression - reducing the tokens that touch the model in the first place.

Pricing

Operational efficiency. Workload consolidation, shared platform infrastructure, eliminated redundant inference paths across teams.

Pricing

What a Logiciel AI Cost & Performance Teardown Looks Like.

Week 1 - Workload profiling.

What it meansWe instrument your AI workloads, measure inference patterns, GPU utilization, latency distributions, and cost per business event (per chart summarized, per claim processed, per agent response).

Week 2 - Cost decomposition.

What it meansWe decompose total AI spend by model, by workload, by team, by environment. Most clients have never seen this view before - and the line items it surfaces become the targets.

Week 3 - Optimization roadmap.

What it meansFor each cost lever in scope, we model the savings, the engineering effort, the risk to accuracy and compliance posture, and the time-to-realize.

Week 4 - Quantified business case.

What it meansA written roadmap with prioritized initiatives, expected savings ranges, accuracy and latency impact, and a sequencing plan. This is the artifact you take to your CFO and CMIO.

What we build

What the Engineering Work Actually Looks Like.

Model right-sizing and distillation. Workload-specific evaluation suites that prove the smaller, cheaper model performs as well as the larger one on the actual healthcare task.

↳ What we build

Inference engineering. Quantization (INT8, FP8 where supported), KV-cache optimization, speculative decoding, batching strategy, vLLM/TensorRT-LLM/SGLang tuning where appropriate.

↳ What we build

Infrastructure re-platforming. GPU instance migration, reserved capacity restructuring, multi-region cost optimization, shared inference cluster patterns.

↳ What we build

Vendor renegotiation support. We provide the data your procurement team needs to renegotiate foundation model and managed AI service contracts.

↳ What we build

Platform consolidation. Eliminating redundant inference paths, standing up shared embedding and evaluation infrastructure across teams.

↳ What we build

Continuous FinOps. Dashboards, alerts, and runbooks that prevent the cost drift from recurring after the engagement ends.

↳ What we build

Under the hood

What "40–70% Savings" Actually Looks Like in Numbers.

Model right-sizing + distillation:

28–42% inference cost reduction.

Included

Inference engineering (quantization, batching, caching)

Additional 18–26% reduction in residual inference cost.

Included

GPU infrastructure restructuring

22–34% reduction in compute cost layer.

Included

Vendor renegotiation

8–18% reduction in foundation model and managed service spend.

Included

Net annual savings range

$900K to $1.6M against the original $2.4M baseline.

Included

Implementation cost recovery

Typically 3–6 months.

Included

What we build

The Constraints That Generic AI FinOps Practices Miss.

PHI cannot move to the cheapest region or the cheapest model. Cost arbitrage strategies that work in retail or media don't work the same way in HIPAA-regulated workloads.

Accuracy degradation has clinical consequences. Distillation and quantization choices have to be evaluated against the clinical or operational outcome, not against a generic benchmark.

Audit trails and governance must be preserved through optimization. Every change has to flow through the model risk management program, not around it.

Vendor BAAs constrain the negotiation surface. Not every cheaper alternative is BAA-eligible, which shapes the vendor strategy.

Selected work

Tailored engineering for your industry.

Zeme · Real Estate Cut development costs 50% and launched 3× faster with dedicated dev teams.

Real Estate

Cut development costs 50% and launched 3× faster with dedicated dev teams.

Leap · Construction Scaled to 7-figure ARR with AI-augmented software teams.

Construction

Scaled to 7-figure ARR with AI-augmented software teams.

KW · Real Estate 56M+ workflows automated, saving agents 30% time with AI-powered tasks.

Real Estate

56M+ workflows automated, saving agents 30% time with AI-powered tasks.

Resources

Insights for tech leaders.

Whitepaper · PDF

Building AI-Ready Healthcare Data Infrastructure: The 90-Day Roadmap from Staging Bottleneck to Production

Whitepaper · PDF

Building AI-Ready Data Foundations in Healthcare

Whitepaper · PDF

How a Healthcare Provider Integrated LLMs into Clinical Workflows Safely

In their words

What our clients say.

Teams that needed to ship fast, and did. Here's what partnering with Logiciel felt like from the inside.

I would highly recommend them to anyone looking to scale quickly or needing support in engineering, product, or QA.

Patrick Fingles

CEO, Leap

We don't just call them Logiciel; they're part of the Zeme team. Within the first week they were contributing meaningfully to our codebase.

Elior Alayev

Founder & CEO, Zeme

The Logiciel team worked tirelessly and built everything we needed, with security and best practices across our entire platform. It allowed us to become #1 in our industry, and we couldn't have done it without them.

David Buzzelli

Co-Founder, JobProgress

Questions

Frequently asked questions.

What are AI optimization and performance services?

AI optimization and performance services are engineering engagements that reduce the cost and latency of production AI workloads while preserving accuracy and compliance posture. The work spans model right-sizing, inference engineering, infrastructure restructuring, vendor strategy, and platform consolidation. For healthcare AI workloads, the engagement is constrained by HIPAA, BAA, and clinical accuracy requirements throughout.

How much can we typically save?

Mid-market healthcare AI programs typically run 40–70% above their efficient cost profile when first profiled. Realized savings in the 12 months after a Logiciel engagement usually land in the 35–55% range against the original baseline, after factoring in implementation effort. The teardown produces your specific quantified business case before any commitment to implementation.

Will optimization affect AI accuracy or clinical outcomes?

Every optimization is evaluated against a workload-specific evaluation suite tied to the clinical or operational outcome - not against a generic benchmark. Optimizations that produce material accuracy degradation are not shipped. The teardown surfaces the accuracy headroom available against each cost lever before implementation begins.

How long does an optimization engagement take?

The teardown is a fixed 4-week engagement. A typical implementation phase runs 12–24 weeks depending on workload count and complexity. Most clients see realized cost reduction in the cloud bill within 60–90 days of starting implementation. Continuous FinOps runs ongoing, sized to the AI portfolio.

Do you work with our existing AI platform and foundation model vendors?

Yes. Logiciel's optimization practice is vendor-neutral. We work across AWS Bedrock, Azure OpenAI, GCP Vertex, Anthropic, OpenAI, open-weight models (Llama, Mistral, Qwen), and self-hosted patterns. We optimize against the platform and vendor mix you've already chosen - and recommend changes only where the savings justify the migration cost.

What does an optimization engagement cost?

The 4-week teardown is a fixed-price engagement. The implementation phase is scoped against the prioritized initiatives in the roadmap. We size engagements so that the realized first-year savings cover the engagement cost 3–6x over for typical mid-market healthcare AI programs. The teardown produces your specific numbers.

Can you help us negotiate with foundation model vendors?

Yes. The teardown produces the cost and utilization data your procurement team needs to renegotiate contracts - specifically, evidence of consumption patterns, alternative vendor benchmarks, and accuracy-equivalent fallback options. We provide the data; your procurement team runs the negotiation.

Let's build

The 10-Minute Estimator That Replaces a 60-Minute Discovery Call.

Use the AI Savings Calculator to model your inference, GPU, and vendor spend against the six optimization levers. If the savings look meaningful, book the teardown. If they don't, we'll tell you.

Calculate your AI savings

AI Optimization & Performance Services for Healthcare.

See Logiciel in action.

What an Unoptimized Healthcare AI Workload Actually Costs You.

Inference spend 2–4x larger than required

GPU utilization at 18–32%

Vendor markups absorbed without negotiation

Redundant model paths

Latency taxing the user experience

The Six Cost Levers in a Healthcare AI Workload.

Model selection and right-sizing. Most production workloads are running models that are bigger than the task requires. The right model at the right precision is usually the single largest cost lever.

Inference architecture. Batching, caching, speculative decoding, distillation, quantization, KV-cache management - the engineering layer that turns a model into a workload.

Infrastructure and GPU economics. Instance family selection, reserved vs. on-demand mix, spot strategy where compliance allows, GPU sharing patterns, regional cost arbitrage.

Foundation model vendor strategy. Negotiated pricing, multi-model fallback, open-weight alternatives where the workload tolerates them.

Data efficiency. Right-sized context, smarter retrieval, deduplication, prompt compression - reducing the tokens that touch the model in the first place.

Operational efficiency. Workload consolidation, shared platform infrastructure, eliminated redundant inference paths across teams.

What a Logiciel AI Cost & Performance Teardown Looks Like.

Week 1 - Workload profiling.

Week 2 - Cost decomposition.

Week 3 - Optimization roadmap.

Week 4 - Quantified business case.

What the Engineering Work Actually Looks Like.

What "40–70% Savings" Actually Looks Like in Numbers.

Model right-sizing + distillation:

Inference engineering (quantization, batching, caching)

GPU infrastructure restructuring

Vendor renegotiation

Net annual savings range

Implementation cost recovery

The Constraints That Generic AI FinOps Practices Miss.

Tailored engineering for your industry.

Cut development costs 50% and launched 3× faster with dedicated dev teams.

Scaled to 7-figure ARR with AI-augmented software teams.

56M+ workflows automated, saving agents 30% time with AI-powered tasks.

Insights for tech leaders.

Building AI-Ready Healthcare Data Infrastructure: The 90-Day Roadmap from Staging Bottleneck to Production

Building AI-Ready Data Foundations in Healthcare

How a Healthcare Provider Integrated LLMs into Clinical Workflows Safely

What our clients say.

Frequently asked questions.

The 10-Minute Estimator That Replaces a 60-Minute Discovery Call.