Prompt Engineering & Compression
Reduce token usage 60-80% by optimizing prompts for clarity and brevity while maintaining output quality. Eliminate redundant instructions, compress context, and use structured formats that require fewer tokens.
Wizeb — AI Cost Optimization
Cut your AI costs 40-70% without sacrificing quality.
Cut AI costs 40-70% with intelligent model routing — the same approach AT&T used to cut its internal AI bill 56% by sending simple requests to cheaper models and reserving premium models for complex reasoning. As AI pricing shifts from per-seat to usage-based models, every inefficient prompt, redundant API call, or poorly-routed request costs real money. Wizeb builds routing, caching, prompt engineering, and model selection into your AI stack to dramatically reduce token consumption while maintaining or improving output quality.
How we optimize
Every project starts with your specific problem
These are the most common ai cost optimization systems we build. Most projects combine elements from several areas.
Smart Model Routing
Route requests to the right model for each task — Haiku for simple queries ($0.25/M tokens), Sonnet for moderate complexity ($3/M), Opus only when essential ($15/M). Same results, fraction of the cost.
Semantic Caching & Response Reuse
Cache common responses and detect semantically similar queries to eliminate redundant API calls. Reduce costs 40-60% on high-volume, repetitive use cases like customer support and FAQs.
Batch Processing & Context Management
Batch non-urgent requests for 50% API discounts. Optimize context windows by truncating intelligently, summarizing conversation history, and only including relevant information per request.
Fine-Tuning & Custom Models
For high-volume, specialized tasks, fine-tuned models eliminate the need for few-shot examples in every prompt — reducing tokens per request by 70% while improving accuracy.
Real-Time Cost Monitoring
Track token usage, costs, and efficiency metrics in real-time. Automatic alerts when spend exceeds thresholds, with detailed breakdowns by endpoint, user, or feature.
How We Work
From problem to production
No slide decks, no vague roadmaps. Here's exactly how a project runs from first call to live deployment.
01
Cost audit & baseline
We analyze your current token usage patterns, identify waste, and establish a baseline. For existing implementations, we audit prompts, API calls, and routing logic. For new projects, we design efficiency from day one.
02
Architecture optimization
We design or refactor your AI architecture for efficiency — implementing smart routing, caching layers, batch processing pipelines, and optimized context management strategies.
03
Prompt & model optimization
We compress prompts, eliminate redundancy, and select the most cost-effective models for each use case. Quality is validated against your acceptance criteria before deployment.
04
Monitor & iterate
We deploy with real-time cost tracking and continue optimizing based on usage patterns. Most clients see additional 10-20% savings in the first month as we identify new optimization opportunities.
Case Study
What this looks like in practice
A real project, real results. No client name — that's deliberate.
Client Type
B2B SaaS Customer Support
Shipped & live
“67% cost reduction — from $4,200/mo to $1,380/mo with zero quality loss”
The Challenge
"We migrated from ChatGPT Team (50 seats, $1,250/mo) to a custom API for better control. Within two months, our bill hit $4,200/mo and kept climbing. We were paying more for worse predictability."
The Solution
Wizeb implemented semantic caching for common questions (60% cache hit rate), model routing (Haiku for FAQs, Sonnet for complex queries), and prompt compression (average 220 tokens → 65 tokens). Real-time monitoring prevents cost spikes.
Key Results
- $1,380/moNew monthly cost (was $4,200)
- 67%Cost reduction vs original API spend
- 92%Customer satisfaction (unchanged)
Technologies We Use
Model-agnostic. Stack-agnostic.
We pick what's right for the problem — not the most impressive-sounding name.
Claude (Haiku/Sonnet/Opus)Models
GPT-4o / GPT-3.5Models
Gemini FlashModels
LangChainFramework
RedisCaching
Prompt caching APIsOptimization
LangSmith / PromptLayerMonitoring
Custom routing logicArchitecture
Common Questions
Things people ask before starting
Work with Wizeb
Ready to build something?
Tell us about the problem. We'll come back with a realistic picture of what's possible, what it costs, and how fast it can be running.