Interaction Cost Minimization

After optimizing costs for many AI systems, I've developed a systematic approach that consistently delivers cost reductions of 60-80%. Here's my playbook, in order of least to most effort: Step 1: Optimizing Inference Throughput Start here for the biggest wins with least effort. Enabling caching (LiteLLM (YC W23), Zilliz) and strategic batch processing can reduce costs by a lot with very little effort. I have seen teams cut costs by half simply by implementing caching and batching requests that don't require real-time results. Step 2: Maximizing Token Efficiency This can give you an additional 50% cost savings. Prompt engineering, automated compression (ScaleDown), and structured outputs can cut token usage without sacrificing quality. Small changes in how you craft prompts can lead to massive savings at scale. Step 3: Model Orchestration Use routers and cascades to send prompts to the cheapest and most effective model for that prompt (OpenRouter, Martian). Why use GPT-4 for simple classification when GPT-3.5 will do? Smart routing ensures you're not overpaying for intelligence you don't need. Step 4: Self-Hosting I only suggest self-hosting for teams at scale because of the complexities involved. This requires more technical investment upfront but pays dividends for high-volume applications. The key is tackling these layers systematically. Most teams jump straight to self-hosting or model switching, but the real savings come from optimizing throughput and token efficiency first. What's your experience with AI cost optimization?

As companies look to scale their GenAI initiatives, a significant hurdle is emerging: the cost of scaling the infrastructure, particularly in managing tokens for paid Large Language Models (LLMs) and the surrounding infrastructure. Here's what companies need to know: a) Token-based pricing, the standard for most LLM providers, presents a significant cost management challenge due to the wide cost variations between models. For instance, GPT-4 can be ten times more expensive than GPT-3.5-turbo. b) Infrastructure costs go beyond just the LLM fees. For every $1 spent on developing a model, companies may need to pay $100 to $1,000 on infrastructure to run it effectively. c) Run costs typically exceed build costs for GenAI applications, with model usage and labor being the most significant drivers. Optimizing costs is an ongoing process, and the following best practices would help reduce the costs significantly: a) Techniques, like preloading embeddings, can reduce query costs from a dollar to less than a penny. b) Optimizing prompts to reduce token usage c) Using task-specific, smaller models where appropriate d) Implementing caching and batching of requests e) Utilizing model quantization and distillation techniques f) A flexible API system can help avoid vendor lock-in and allow quick adaptation as technology evolves. Investments in GenAI should be tied to ROI. Not all AI interactions need the same level of responsiveness (and cost). Leaders must focus on sustainable, cost-effective scaling strategies as we transition from GenAI's 'honeymoon phase'. The key is to balance innovation and financial prudence, ensuring long-term success in the AI-driven future. #GenerativeAI #AIScaling #TechLeadership #InnovationCosts #GenAI

This simple optimization cut agent response time by 54% and reduced tokens by 95%. ���� Here's what I have learned after building and deploying agents. Many developers reach for tools or MCP servers by default. Without considering if there's a simpler way. One of the first questions I ask when building an agent is this: - Does your agent need to look up this information, or can you just provide it? For example: Getting Current Date/Time ❌ Approach 1: Give agent a current_time tool - 2 LLM calls (agent decides → invokes tool → processes result) - 4.78 seconds - 1,734 tokens - Agent has to reason about using the tool ✅ Approach 2: Include date/time in system prompt - 1 LLM call (information already there) - 2.18 seconds - 94 tokens - Instant access The Impact at Scale: - 54% faster (2.18s vs 4.78s) - 95% fewer tokens (94 vs 1,734) - Better UX (no extra latency) - Lower cost per interaction Imagine this at scale for 1M agent calls/month: - Tool approach: ~1.7B tokens - Context approach: ~94M tokens - Savings: $hundreds to $thousands (depending on your model) I can't stress this enough, not everything needs to be a tool. Let alone an MCP server. This technique shown is called dynamic context injection. This is where you update the agent's context with live data instead of making it use tools to fetch data you already have. You can inject this via system prompt, user prompt or even during the agent event loop. This is just one of the many topics I intend to cover. So if you have questions ask below or have a comment drop it below. 👇🏾 #AIAgents #ProductionAI #CostOptimization #StrandsAgents #AWSreInvent #LLMOptimization

My favorite product - Shazam (acquired by Apple in 2018) Shazam is an example of great product management in action, ensuring sustained product excellence at scale. At its core, Shazam solves one clearly defined job-to-be-done: identify a song playing in the environment under imperfect conditions, quickly and reliably. From the user’s point of view, the experience is almost effortless. Open the app and it’s already listening. In many cases, there isn’t even a click required to start receiving value. The UI is intentionally sparse: 1. One screen 2. One primary state 3. No onboarding or configuration 4. No decisions to make This level of simplicity maintained over time is not accidental—it’s the outcome of strong product judgment. What the user doesn’t see is the depth of engineering investment behind that experience: 1. Continuous audio capture and preprocessing 2. Robust signal normalization across devices and environments 3. Large-scale, low-latency pattern matching 4. High-confidence identification from partial and noisy inputs All of this complexity is deliberately absorbed by the backend so the frontend can remain obvious. This is where product management shows up clearly. The product architecture is optimized around a single primary metric: time-to-correct-identification. Every product decision—UI, flow, feature inclusion—serves that metric. The team made conscious trade-offs to: 1. Minimize interaction cost 2. Protect time-to-value 3. Avoid feature expansion that dilutes the core outcome 4. Invest disproportionately in infrastructure rather than surface-level features Great PM work is often invisible. It shows up as restraint, clarity, and consistency—not as more buttons or more screens. Shazam demonstrates a principle that holds at scale: When user intent is simple, the product experience should be simpler—no matter how complex the system underneath. That alignment between user experience, engineering depth, and strategic focus is why Shazam remains a reference point for product excellence—and a prime example of product management done right.

Meta, Google, and Amazon - "Don't scale compute; scale interaction" Run smarter, smaller agents at 1/10th the cost; Here's how... I spent some hours digging into the new Agentic Reasoning Survey by researchers from UIUC, Meta, Amazon, and Google DeepMind. They findings are quite interesting, especially challanging the fact, "How can a smaller agent be smarter?" 📌 Quick answers first, then the deep dive: 1. We're moving from "static" generation to "agentic" interaction. 2. We're shifting from "Post-training" (hardcoded weights) to "In-context Reasoning" (scaling test-time logic). 3. We're using specialized "Multi-Agent" teams instead of one massive, expensive brain. The paper outlines that we can do this by 3 ways: 1\ Foundational Layer: Inceasing reasoning via Core single-agent capabilities like Planning, Tool Use, and Search. 2\ Self-Evolving Layer: Inceasing reasoning via Agents that refine themselves through Feedback and Memory. They learn from mistakes *without* retraining. 3\ Collective Layer: Inceasing reasoning via Multi-agent collaboration where roles like "Managers" and "Workers" coordinate to solve long-horizon tasks. 📌 The numbers are what really caught my eye: ↳ 3.5x more compute-efficient: 8B models are now reaching competitive performance with 32B models by using these agentic loops. ↳ 30-fold token reduction: Using "Semantic Structured Compression" to reduce inference-time consumption. ↳ 56.9% Pass Rates: Achieved with only 180 training queries, nearly 3x better than traditional GPT-5 baselines. The point they made? Better interaction = more affordable AI. and I beleive it too! You aren't just buying compute anymore; you're building systems that *think* before they act. P.S. Check the comments for full research 👇 📌 If you want to understand AI agent concepts deeper, my free newsletter breaks down everything you need to know: https://lnkd.in/gg8rNvCq Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents

Most leaders ask: “If AI costs money, where exactly do we save costs?” The answer becomes clear only when you map AI step by step across the customer journey. Here’s how cost-to-serve reduces systematically, not magically This is what I have learnt and realised. 1️⃣ First Contact (Call center, chat, web, inbound leads) AI implemented: • AI agents handle FAQs & intent detection • Smart routing to correct team • Call summarization for agents Why cost reduces (important): 👉 Because humans only handle qualified, contextual conversations—not repetitive ones. Typical cost saving: 25–40% at Level-1 support 2️⃣ Lead Capture & Data Cleaning (CRM stage) AI implemented: • Duplicate detection • Auto-enrichment & validation • Bounce prediction before campaigns • Junk deletion Why cost reduces: 👉 Because automation stops failing, campaigns stop wasting money, and teams stop fixing data manually. Typical cost saving: 20–30% reduction in wasted campaign & ops effort 3️⃣ Journey Automation & Nurturing AI implemented: • AI-driven segmentation • Next-best-action recommendations • Dynamic journey optimization Why cost reduces: 👉 Because messages go to the right customer at the right time—reducing rework and drop-offs. Typical cost saving: 15–25% lower cost per conversion 4️⃣ Sales Handoff & Deal Management AI implemented: • Deal risk prediction • Auto-generated summaries • Priority-based follow-ups • Forecast accuracy improvement Why cost reduces: 👉 Because sales time is spent only on deals that can close, not chasing dead ends. Typical cost saving: 10–20% reduction in sales effort per deal 5️⃣ Post-Purchase & Onboarding AI implemented: • Automated demand notes & reminders • Smart onboarding journeys • Proactive issue detection • Customer history summaries for agents Why cost reduces: 👉 Because customers don’t need to call support to understand what’s already happening. Typical cost saving: 30–50% reduction in onboarding & support tickets 6️⃣ Retention & Loyalty AI implemented: • Churn prediction • Personalized retention nudges • Loyalty journey automation Why cost reduces: 👉 Because retaining a customer costs far less than replacing one—and AI acts before churn happens. Typical cost saving: 5–15% reduction in churn-related revenue loss Final insight: AI is expensive when added as a tool. AI is profitable when added as a journey layer. Cost savings don’t come from automation alone. They come from preventing friction before it creates work. That’s how AI pays for itself. #ai #automation #marketing #journey #cx #CustomerExperience #AIinbusiness #customerjourney #martech #revenueoperations #DigitalTransformation

If you have a working product/service that relies on language models in the backend, there are a few very easy ways to reduce costs without sacrificing quality: (a) Creating a small custom router to decide which language model to use (you can rely on cheaper models for simpler responses); (b) Shortening a large chunk of the conversational history as your chat progresses;  (c) Caching your outputs. There are, of course, many more. Having worked with a few companies whose business model is based on having high LLM output quality while minimizing associated costs, I’m surprised that these strategies are not yet standard.