Navgati

Your AI bill is too high.
Find the leak.

Navgati audits OpenAI, Azure OpenAI, Anthropic, Bedrock, Vertex, RAG, agentic, and self-hosted LLM costs so CTOs and ML platform leads can reduce inference spend before buying more capacity.

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Reduce OpenAI API CostsAzure OpenAI Cost OptimizationAWS Bedrock Cost ReviewLLM Inference CostRAG Cost LeaksSelf-Hosted LLM Cost AuditGPU Optimization AuditToken Spend Forecasting

TRUSTED BY GROWING COMPANIES

LLM SPEND OPTIMIZATION

Find the cost leak before changing providers.

Start with the bill you already have. Then isolate whether the leak is provider pricing, token volume, RAG context, agent loops, retries, GPU utilization, or a workload that needs different deployment mode.

Map the provider bill

Break down OpenAI, Azure OpenAI, Anthropic, Bedrock, Vertex, or self-hosted spend by feature, user, route, model, prompt, and failure shape.

Find workflow multipliers

Expose whether context windows, retries, tool calls, memory, embedding refreshes, and RAG retrieval are multiplying inference costs unnecessarily.

Pick the next move

Decide whether the next step is caching, routing, fine-tuning, prompt compression, GPU packing, and fallback models.

COST REPORTS

Cloud and AI Cost Reports

Free reports for operators and executives who need precise, practical answers about cloud and model spend.

NEW REPORT

Cloud Egress Costs 2026: The Hidden Transfer Tax

Why cloud data transfer fees can quietly distort architecture choices and AI infrastructure economics.

NEW REPORT

The AI Economics Report: Tokens and Sustainability

A data-first look at token pricing, inference behavior, and the economics behind production usage.

NEW REPORT

Tokens got 98.7% cheaper. Why did your AI bill spike?

A practical breakdown of the cost patterns that make inference spend rise despite lower model prices.


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AI TOOLS

Try Our AI Agents – Free

Interactive tools that give operators a structured cost reading.

On-Prem LLM Cost Estimator

See your on-prem GPU cluster, compute against cloud API and reserved inference options. Estimate utilization, capex, power, staff, and break-even thresholds.


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user: compare api vs local agent: loading model costs… agent: checking gpu utilization… result: savings path found

AI Infra Billing Agent

Upload your OpenAI, Azure, Anthropic, or Bedrock bill and get normalized leakage, routing, and optimization findings.

Prompt Cost Analyzer

Analyze prompt token usage across your product and find the expensive parts hidden inside ordinary feature flows.

AI COST INSIGHTS

Clear answers for expensive AI infrastructure decisions.

Recent answers for teams buying hosted, self-hosted, or hybrid AI infrastructure.

JUNE 2026

Self-Host LLMs vs API Calls: When Each Wins

Compare self-hosted LLMs and managed API cost across token volume, latency, privacy, operations, and inference use.

JUNE 2026

LLM Break-Even Point: API, Hybrid, or Self-Hosted?

Map GPU rental, reserve forecasts, model choice, and traffic shape into a practical break-even view.

JUNE 2026

Lambda vs H100 ROI for LLM Inference

Compare L40S and H100 hardware economics, throughput, memory utilization, capex, and amortization.

JUNE 2026

Edge RAG vs OpenAI API: When Private Retrieval Wins

Compare edge and self-hosted RAG against API workflows for privacy, latency, usage patterns, and context costs.

JUNE 2026

GPU Requirements for Hosting LLMs: Edge to H100

Find practical GPU requirements for hosting LLMs across small, medium, and H100 clusters.

LATEST TECHNICAL INSIGHTS

Latest Technical Insights

Deep dives into model optimization, GPU, token, DevOps, and production-grade AI/ML engineering.

TECHNICAL

Web Development LLMs: An Agentic Guide for Teams

Framework choices, editor loops, and deployment patterns for production web development with AI.

TECHNICAL

The Trace Tax: AI Compensation Audit of Inference Optimization Techniques

How tracing and optimization change cost profiles for production AI apps.

TECHNICAL

Embedding Search Gateway: Build a Hybrid Cloud And Edge Inference

A practical architecture for routing retrieval and inference across hybrid environments.

CLIENT OUTCOME

Optimization, Measured

Production inference work should show up in unit economics, not just benchmark charts.

CASE STUDY

From overprovisioned A100s to a leaner H100 deployment.

A client running recommendation and RAG workloads across two regions saw GPU saturation climb after model routing, prompt compaction, cache tuning, and workload batching.


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42%

GPU utilization increase

2.3x

Throughput improvement

$47K -> $28K

Monthly run-rate change

FREQUENTLY ASKED QUESTIONS

Common Questions About AI Infrastructure Cost

When should a company self-host an LLM instead of using an API?+
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What AI costs are hidden outside the model invoice?+
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Navgati

AI infrastructure economics: Navgati audits AI spend and helps teams control LLM, GPU, agentic, inference, or self-hosted bills.

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LLM Cost Optimization

GPU Accounting Services

Cloud Cost Reduction

Self-Hosted LLM Development

Agentic AI Service Governance

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