LLM Deployment Methods

LLMOps is about running LLMs like real products with feedback loops, monitoring, and continuous improvement baked in 💯 This visual breaks it down into 14 steps that make LLMs production-ready and future-proof. 🔹 Steps 1-2: Collect Data + Clean & Organize Where does any good model start? With data. You begin by collecting diverse, relevant sources: chats, documents, logs, anything your model needs to learn from. Then comes the cleanup. Remove noise, standardize formats, and structure it so the model doesn’t get confused by junk. 🔹 Steps 3-4: Add Metadata + Version Your Dataset Now that your data is clean, give it context. Metadata tells you the source, intent, and type of each data point: this is key for traceability. Once that’s done, store everything in a versioned repository. Why? Because every future change needs a reference point. No versioning = no reproducibility. 🔹 Steps 5-6: Select Base Model + Fine-Tune Here’s where the model work begins. You choose a base model like GPT, Claude, or an open-source LLM depending on your task and compute budget. Then, you fine-tune it on your versioned dataset to adapt it to your specific domain, whether that’s law, health, support, or finance. 🔹 Steps 7-8: Validate Output + Register the Model Fine-tuning done? Cool, and now test it thoroughly. Run edge cases, evaluate with test prompts, and check if it aligns with expectations. Once it passes, register the model so it’s tracked, documented, and ready for deployment. This becomes your source of truth. 🔹 Steps 9-10: Deploy API + Monitor Usage The model is ready! You expose it via an API for apps or users to interact with. Then you monitor everything: requests, latency, failure cases, prompt patterns. This is where real-world insights start pouring in. 🔹 Steps 11-12: Collect Feedback + Store in User DB You gather feedback from users: explicit complaints, implicit behavior, corrections, and even prompt rephrasing. All of that goes into a structured user database. Why? Because this becomes the compass for your next update. 🔹 Steps 13-14: Decide on Updates + Monitor Continuously Here’s the big question: Is your model still doing well? Based on usage and feedback, you decide: continue as is or loop back and improve. And even if things seem fine, you never stop monitoring. Model performance can drift fast. 📚 Research and Curation Effort: 4 hours If you've found it helpful, please like and repost it to uplift your network ♻️ Follow me, Bhavishya Pandit, to stay ahead in Generative AI! ❤️ #llm #opensource #rag #meta #google #ibm #openai #gpt4 #ml #machinelearning #ai #artificialintelligence #datascience #python #genai #generativeai #huggingface #openai #linkedin #computervision

Vector search gave LLMs memory. Graph databases gave LLMs relationships. But neither could give LLMs real-time reasoning. That’s the next frontier. Because agents don't just need content — they need connected knowledge that they can reason over, instantly. And here’s where the traditional stack fails: Most graph databases still “walk” through data — one node, one edge, one hop at a time. Exactly like humans flipping pages in a directory. That works for analytics. It collapses for AI agents. The core idea: What if graphs stopped behaving like “maps”… and started behaving like “math”? That’s the FalkorDB breakthrough. Instead of hopping from node to node — FalkorDB converts the entire graph into a sparse matrix. Your data becomes a mathematical object. And once your graph is math — queries become math too. Not traversal. Not step-by-step. Just matrix computation using linear algebra. And math doesn’t walk. It computes. Which means: Real-time graph reasoning for agents. At scale. Why this changes the game for LLMs: Vector search tells you what is similar. Graphs tell you what is connected. But sparse matrix graphs tell you what is structurally meaningful — instantly. It’s the difference between finding a document… …and finding the truth inside a network of relationships. That's how agents will think. FalkorDB brings this into the real world: 🔹 Graphs as sparse matrices — zero traversal overhead 🔹 Linear algebra-powered queries — orders-of-magnitude faster 🔹 Redis-native, open-source, lightweight deployment 🔹 OpenCypher compatible — no need to learn a new language 🔹 Built specifically for LLM context, agent memory, and reasoning I tested it — queries that took seconds now feel like function calls. Agents that relied on retrieval now reason in real-time. The future isn't LLMs with bigger context windows. It’s LLMs with smarter knowledge structures. And frameworks like FalkorDB will power that shift. I’ve shared their GitHub link in the comments — explore it, run it, stress it. It feels like where Agent Memory is heading.

How to choose the best LLM for your use case 𝟭. 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 𝗔𝗴𝗮𝗶𝗻𝘀𝘁 𝗞𝗲𝘆 𝗧𝗮𝘀𝗸𝘀 - Start with task-based benchmarking: Choose a shortlist of LLMs and run tests specific to your use case (e.g., generate product descriptions, summarize long documents, or extract key insights). - Use open benchmark platforms like Hugging Face’s Evaluation or proprietary in-house benchmarks tailored to your data. 𝟮. 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿 𝗣𝗿𝗲-𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝘃𝘀. 𝗙𝗶𝗻𝗲-𝘁𝘂𝗻𝗲𝗱 𝗠𝗼𝗱𝗲𝗹𝘀 - If your use case requires specialized knowledge, consider models already fine-tuned for your industry (like healthcare or finance). - For more general tasks, evaluate popular pre-trained models (e.g., GPT-4, LLaMA, Mistral) to see if they perform well out-of-the-box. 𝟯. 𝗣𝗶𝗹𝗼𝘁 𝗦𝗲𝘃𝗲𝗿𝗮𝗹 𝗠𝗼𝗱𝗲𝗹𝘀 𝗶𝗻 𝗮 𝗦𝗮𝗻𝗱𝗯𝗼𝘅 - Set up a controlled environment and test models under real-world conditions. Look for how they handle edge cases and whether they require significant prompt engineering. - Pay attention to the ease of fine-tuning if customization is needed. 𝟰. 𝗔𝘀𝘀𝗲𝘀𝘀 𝗠𝗼𝗱𝗲𝗹 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 𝗮𝗻𝗱 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 - Check the support and community around each model. Open-source models like LLaMA have vibrant communities that offer quick help and resources. - Evaluate the ecosystem of tools (e.g., prompt optimization libraries, monitoring solutions, or integration plugins) that come with each model. 𝟱. 𝗣𝗹𝗮𝗻 𝗳𝗼𝗿 𝗟𝗼𝗻𝗴-𝘁𝗲𝗿𝗺 𝗠𝗮𝗶𝗻𝘁𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗖𝗼𝘀𝘁𝘀 - For enterprise use, factor in not just model performance but also long-term sustainability. This includes how often the model is updated, security patches, and total costs. - Consider if the LLM vendor provides good SLAs for managed services or if it’s better to host open-source models on your infrastructure to manage costs effectively. What tips do you have to share with all of us that worked well?

Everyone is quoting "95% of organizations are getting zero return" on GenAI. That's significant, but few seem to have read the paper for the deeper insights, both in study findings and the outlook needed to get far more than zero return. Here are a few, but read it to find your own... 💬“The dominant barrier to crossing the GenAI Divide is not integration or budget, it is organizational design.” 🔎Got it in one. Tacking GenAI onto old organizational structures doesn't work. You have to fundamentally redesign work and the organization. 💬“The core barrier to scaling is not infrastructure, regulation, or talent. It is learning." 🔎The reference here is primarily to the feedback systems to improve GenAI systems, but it applies just as much to organizational learning. 💬"Real gains come from replacing BPOs and external agencies, not cutting internal staff. Front-office tools get attention, but back-office tools deliver savings... Best-in-class organizations are generating measurable value… BPO elimination: $2–10M annually in customer service and document processing; Agency spend reduction: 30% decrease in external creative and content costs.” 🔎Don't aim to cut staff. Reduce external spend (watch out suppliers!). That gives real wins swiftly. 💬“Organizations that successfully cross the GenAI Divide approach AI procurement differently, they act like BPO clients, not SaaS customers. They demand deep customization, drive adoption from the front lines, and hold vendors accountable to business metrics.” 🔎AI providers need to be professional services firms with similar accountability. 💬"While only 40% of companies say they purchased an official LLM subscription, workers from over 90% of the companies we surveyed reported regular use of personal AI tools for work tasks. In fact, almost every single person used an LLM in some form for their work." 🔎These are not GenAI pilots that are defined and measured, these are people who are finding LLMs useful in their work. 💬"Sales and marketing functions captured approximately 70 percent of AI budget allocation across organizations... but back-office automation often yields better ROI." 🔎Many are choosing the wrong projects. Which is fine, as long as you learn. 💬“Agentic AI… embeds persistent memory and iterative learning by design… Unlike current systems that require full context each time, agentic systems maintain persistent memory, learn from interactions, and can autonomously orchestrate complex workflows.” 🔎This is not a surprising observation since this is the mission of MIT's Project NANDA: Architecting the Internet of AI Agents that produced the report. But it is true that the real value will come from not only new AI architectures, but the new organizational architectures that enable that. Go beyond the headline and read the paper for the real insights.

Few Lessons from Deploying and Using LLMs in Production Deploying LLMs can feel like hiring a hyperactive genius intern—they dazzle users while potentially draining your API budget. Here are some insights I’ve gathered: 1. “Cheap” is a Lie You Tell Yourself: Cloud costs per call may seem low, but the overall expense of an LLM-based system can skyrocket. Fixes: - Cache repetitive queries: Users ask the same thing at least 100x/day - Gatekeep: Use cheap classifiers (BERT) to filter “easy” requests. Let LLMs handle only the complex 10% and your current systems handle the remaining 90%. - Quantize your models: Shrink LLMs to run on cheaper hardware without massive accuracy drops - Asynchronously build your caches — Pre-generate common responses before they’re requested or gracefully fail the first time a query comes and cache for the next time. 2. Guard Against Model Hallucinations: Sometimes, models express answers with such confidence that distinguishing fact from fiction becomes challenging, even for human reviewers. Fixes: - Use RAG - Just a fancy way of saying to provide your model the knowledge it requires in the prompt itself by querying some database based on semantic matches with the query. - Guardrails: Validate outputs using regex or cross-encoders to establish a clear decision boundary between the query and the LLM’s response. 3. The best LLM is often a discriminative model: You don’t always need a full LLM. Consider knowledge distillation: use a large LLM to label your data and then train a smaller, discriminative model that performs similarly at a much lower cost. 4. It's not about the model, it is about the data on which it is trained: A smaller LLM might struggle with specialized domain data—that’s normal. Fine-tune your model on your specific data set by starting with parameter-efficient methods (like LoRA or Adapters) and using synthetic data generation to bootstrap training. 5. Prompts are the new Features: Prompts are the new features in your system. Version them, run A/B tests, and continuously refine using online experiments. Consider bandit algorithms to automatically promote the best-performing variants. What do you think? Have I missed anything? I’d love to hear your “I survived LLM prod” stories in the comments!

Most people evaluate LLMs by just benchmarks. But in production, the real question is- how well do they perform? When you’re running inference at scale, these are the 3 performance metrics that matter most: 1️⃣ Latency How fast does the model respond after receiving a prompt? There are two kinds to care about: → First-token latency: Time to start generating a response → End-to-end latency: Time to generate the full response Latency directly impacts UX for chat, speed for agentic workflows, and runtime cost for batch jobs. Even small delays add up fast at scale. 2️⃣ Context Window How much information can the model remember- both from the prompt and prior turns? This affects long-form summarization, RAG, and agent memory. Models range from: → GPT-3.5 / LLaMA 2: 4k–8k tokens → GPT-4 / Claude 2: 32k–200k tokens → GPT-OSS-120B: 131k tokens Larger context enables richer workflows but comes with tradeoffs: slower inference and higher compute cost. Use compression techniques like attention sink or sliding windows to get more out of your context window. 3️⃣ Throughput How many tokens or requests can the model handle per second? This is key when you’re serving thousands of requests or processing large document batches. Higher throughput = faster completion and lower cost. How to optimize based on your use case: → Real-time chat or tool use → prioritize low latency → Long documents or RAG → prioritize large context window → Agentic workflows → find a balance between latency and context → Async or high-volume processing → prioritize high throughput My 2 cents 🤌 → Choose in-region, lightweight models for lower latency → Use 32k+ context models only when necessary → Mix long-context models with fast first-token latency for agents → Optimize batch size and decoding strategy to maximize throughput Don’t just pick a model based on benchmarks. Pick the right tradeoffs for your workload. 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

Some challenges in building LLM-powered applications (including RAG systems) for large companies: 1. Hallucinations are very damaging to the brand. It only takes one for people to lose faith in the tool completely. Contrary to popular belief, RAG doesn't fix hallucinations. 2. Chunking a knowledge base is not straightforward. This leads to poor context retrieval, which leads to bad answers from a model powering a RAG system. 3. As information changes, you also need to change your chunks and embeddings. Depending on the complexity of the information, this can become a nightmare. 4. Models are black boxes. We only have access to modify their inputs (prompts), but it's hard to determine cause-effect when troubleshooting (e.g., Why is "Produce concise answers" working better than "Reply in short sentences"?) 5. Prompts are too brittle. Every new version of a model can cause your previous prompts to stop working. Unfortunately, you don't know why or how to fix them (see #4 above.) 6. It is not yet clear how to reliably evaluate production systems. 7. Costs and latency are still significant issues. The best models out there cost a lot of money and are very slow. Cheap and fast models have very limited applicability. 8. There are not enough qualified people to deal with these issues. I cannot highlight this problem enough. You may encounter one or more of these problems in a project at once. Depending on your requirements, some of these issues may be showstoppers (hallucinating direction instructions for a robot) or simple nuances (support agent hallucinating an incorrect product description.) There's still a lot of work to do until these systems mature to a point where they are viable for most use cases.

You need to check out the Agent Leaderboard on Hugging Face! One question that emerges in the midst of AI agents proliferation is “which LLMs actually delivers the most?” You’ve probably asked yourself this as well. That’s because LLMs are not one-size-fits-all. While models thrive in structured environments, others don’t handle the unpredictable real world of tool calling well. The team at Galileo🔭 evaluated 17 leading models in their ability to select, execute, and manage external tools, using 14 highly-curated datasets. Today, AI researchers, ML engineers, and technology leaders can leverage insights from Agent Leaderboard to build the best agentic workflows. Some key insights that you can already benefit from: - A model can rank well but still be inefficient at error handling, adaptability, or cost-effectiveness. Benchmarks matter, but qualitative performance gaps are real. - Some LLMs excel in multi-step workflows, while others dominate single-call efficiency. Picking the right model depends on whether you need precision, speed, or robustness. - While Mistral-Small-2501 leads OSS, closed-source models still dominate tool execution reliability. The gap is closing, but consistency remains a challenge. - Some of the most expensive models barely outperform their cheaper competitors. Model pricing is still opaque, and performance per dollar varies significantly. - Many models fail not in accuracy, but in how they handle missing parameters, ambiguous inputs, or tool misfires. These edge cases separate top-tier AI agents from unreliable ones. Consider the below guidance to get going quickly: 1- For high-stakes automation, choose models with robust error recovery over just high accuracy. 2- For long-context applications, look for LLMs with stable multi-turn consistency, not just a good first response. 3- For cost-sensitive deployments, benchmark price-to-performance ratios carefully. Some “premium” models may not be worth the cost. I expect this to evolve over time to highlight how models improve tool calling effectiveness for real world use case. Explore the Agent Leaderboard here: https://lnkd.in/dzxPMKrv #genai #agents #technology #artificialintelligence

One of the biggest debates in AI right now: Which LLM should you use? With AI evolving rapidly, picking the right LLM isn’t just a tech decision anymore—it’s a business strategy. The old one-size-fits-all approach? Gone. Most companies now use multiple models—some for general intelligence, others fine-tuned for specific tasks. But is there a "best" LLM? No. There’s only the right LLM for your needs. Let’s break it down. ⬇️ 🔹 Generalist vs. Specialist Need a broad, powerful AI? → GPT-4, Claude Opus, Gemini 1.5 Pro → Best for customer support, legal analysis, research, and creative tasks. Need deep domain expertise? → IBM Granite, Mistral models → Used in healthcare (medical records), finance (fraud detection), and manufacturing (predictive maintenance). ------------------------ 🔹 Big vs. Slim Large, high-performance models (GPT-4, Claude, Gemini) → Best for complex reasoning, but expensive & slower. Smaller, efficient models (Mistral 7B, LLaMA 3, RWWK) → Faster, cheaper, and ideal for real-time, edge, or cost-sensitive AI applications. 📌 Example: ✅ Building an AI-powered legal assistant? → GPT-4 for deep reasoning. ✅ Need AI for real-time chatbot responses? → Mistral 7B—fast, efficient, deployable on-premises. ------------------------ 🔹 Open vs. Closed Models Want full control & customization? → Open-source models (LLaMA 3, Mistral) provide transparency. Need cutting-edge AI out-of-the-box? → Closed models (GPT-4, Gemini) still lead in performance. 📌 Trend Alert: Many companies now use hybrid approaches—fine-tuning open models for cost efficiency while using closed models for specialized tasks. ------------------------ 💰 Cost vs. Performance: Key Considerations Budget-conscious? → Fine-tune open-source models (Mistral, LLaMA 3) for cost savings. Need state-of-the-art reasoning? → Proprietary models (GPT-4, Gemini) deliver superior accuracy. Deploying AI in production? → Go slim, go fast. ------------------------ 🚀 The Future of LLMs: What’s Next? 🔹 Multimodal AI (text, image, audio) → GPT-4V & Gemini 1.5 enable vision, speech, and multi-turn interactions. 🔹 On-device AI → LLaMA & Mistral are reshaping privacy-first, edge AI deployments. 🔹 Model-as-a-Service (MaaS) → More companies rent AI models via API rather than investing in infrastructure. ------------------------ 🔑 The Takeaway? There’s no universal “best” model—only the best fit for your specific use case: ✅ Need high-performance reasoning? → Go big. ✅ Deploying AI in production? → Go slim. ✅ Building industry-specific AI? → Fine-tune & optimize costs. ------------------------ Your Turn! 🔹 What’s your LLM strategy? 📊 Poll: When choosing an LLM, what matters most to you? 1️⃣ Accuracy 2️⃣ Cost 3️⃣ Speed 4️⃣ Customizability ------------------------ Drop your thoughts below—let’s discuss! 👇🚀 Sarveshwaran Rajagopal #AI #ArtificialIntelligence #MachineLearning #LLMs #LargeLanguageModels #GenerativeAI

When choosing to use an LLM via an API, you are not just selecting a model but an entire model-plus-infrastructure package. Sure, DeepSeek and Kimi k2 have attractive pricing, but what the price doesn't tell you are key metrics like TTFT, TPOT, RPS, usage limits, context limits, and others. Artificial Analysis has numbers on output speed vs price. You want to be either in that green quadrant (high speed, reasonable cost) or toward the center of the graph. Models like Gemini 2.5 Flash hit that sweet spot and o3, Gemini 2.5 Pro, GPT-4.1 are also good. The only other way to reduce costs and increase speed is to use caching or cache prompts (Zilliz) and reduce prompt size (ScaleDown, PromptOpti, Kong Inc.) A state-of-the-art model may be rendered unusable if served on a slow, inefficient platform, making performance benchmarking on the target provider's infrastructure an essential step in any evaluation.