Abhilash John Why LLMOps Tools Miss the Full Picture When It Comes to Tracking AI Costs
Why standard LLMOps tools fall short of comprehensive AI cost tracking, missing infrastructure, third-party tools, and agentic value chains.
Your engineering team has been using Langfuse for the past six months to monitor your AI application. The dashboard looks great, showing you token usage, latency metrics, and costs for every OpenAI API call. Everything seems under control. Then your CFO walks into your office with a question that stops you cold: “We spent $127,000 on AI last quarter, but your Langfuse dashboard only shows $43,000. Where did the other $84,000 go?”
This is the uncomfortable reality that many engineering leaders face today. LLMOps tools like Langfuse, Helicone, Portkey, and LangSmith have become essential for developer teams building AI applications. They provide excellent visibility into LLM API costs and performance. But when it comes to tracking the complete cost picture of running AI in production, they have significant blind spots that can leave finance teams frustrated and businesses unable to properly calculate margins.
Let me walk you through exactly why LLMOps tools fall short for comprehensive cost tracking, and more importantly, what that means for your business.
Understanding What LLMOps Tools Actually Track
Before we dive into the limitations, it helps to understand what LLMOps tools were designed to do and what they excel at. These platforms emerged to solve a specific set of problems that developers face when building applications on top of large language models.
Think about what happens when you make a simple API call to OpenAI’s GPT-4. You send a prompt, the model processes it and returns a response, and you get charged based on the number of tokens consumed in both the input and output. For a developer trying to build a reliable AI application, you need to track several things about this interaction. How long did the request take? Did it succeed or fail? How many tokens were used? What did the actual prompt and response look like? Are there patterns in failures or slow responses?
LLMOps tools were built specifically to answer these questions. They sit as a proxy or wrapper around your LLM API calls and capture detailed telemetry about every interaction. When you integrate a tool like Helicone into your code with a simple one line change, it starts logging every request you make to OpenAI, Anthropic, or whichever model provider you’re using. You can see the exact prompts, the responses, the token counts, the costs, and the latency for each call.
This kind of visibility is genuinely valuable for development teams. When a user reports that your AI feature is giving strange responses, you can trace back to that specific session and see exactly what prompts were sent and what the model returned. When your OpenAI bill suddenly doubles, you can identify which part of your application is making excessive API calls. If a particular prompt is consuming 5,000 tokens when it should only need 500, you can optimize it.
For what they were designed to do, which is help developers build better AI applications by providing detailed observability into LLM interactions, these tools work extremely well. The problem starts when organizations assume these tools can serve as complete cost management platforms for their entire AI spend. They cannot, and understanding why requires looking at what they don’t see.
The Fundamental Architecture Problem: Proxies Only See What Passes Through Them
The core limitation of most LLMOps tools stems from their architecture. They typically work as proxies that sit between your application and the LLM provider. Your code makes a request, it passes through the LLMOps tool which logs it, and then the tool forwards the request to OpenAI or Anthropic or whoever your provider is. When the response comes back, it flows through the tool again before reaching your application.
This proxy architecture has an inherent limitation. It can only track what actually passes through it. Think about it like having a security camera pointed at your front door. That camera gives you excellent visibility into everyone who enters and exits through the front door. But it tells you nothing about people using the side entrance, the back door, or climbing through windows.
In the world of AI applications, there are many “entrances” beyond the simple prompt to response flow that LLMOps proxies monitor. Let me give you a concrete example of how costs can slip through these gaps.
Imagine you’re building an AI customer support system. When a customer asks a question, your system does the following: it makes an initial call to GPT-4 to understand the intent, performs a vector database search to find relevant knowledge base articles, calls your internal order management API to check the customer’s order status, makes a tool call through an automation platform like n8n to update your CRM, and finally makes another GPT-4 call to generate the response.
Your LLMOps tool will capture those two GPT-4 calls perfectly. It will show you the token usage, the cost, the latency, everything you need to know about those interactions. But what about the other operations? The vector database query costs money. The compute time to run your order lookup API costs money. The n8n workflow execution costs money if you’re on a usage based plan. None of these costs flow through your LLMOps proxy, so none of them show up in your cost tracking.
This is what’s called the agentic workflow cost visibility problem, and it’s becoming increasingly common as AI applications get more sophisticated. Modern AI systems aren’t just making single API calls. They’re orchestrating complex multi step workflows that involve vector databases, tool calls to external APIs, internal compute for processing and transforming data, and coordination between multiple AI models. The LLMOps tool sees only the LLM portion of this workflow, which might represent 30% or 40% of the total cost to execute that workflow.
Missing the Infrastructure Layer Completely
Even if we solved the agentic workflow problem, there’s another huge category of costs that LLMOps tools don’t capture: your underlying cloud infrastructure. Let me explain why this matters so much.
When you’re running AI applications in production, you’re not just paying for API calls to model providers. You’re running services on AWS, Google Cloud, or Azure. You might have a vector database like Pinecone or Weaviate that’s charging you based on the number of vectors stored and searched. You have compute instances running your application code. You have storage costs for saving conversation histories and embeddings. You have data transfer and egress costs when moving data between services.
Your LLMOps platform knows nothing about any of this. It’s focused entirely on the LLM layer. This creates a massive blind spot when you try to understand your true cost to serve a customer or the real margins on your AI powered features.
Here’s a real world scenario that illustrates this gap. Let’s say you’re running an AI documentation assistant that helps users find information in your product docs. For every query, you’re embedding the user’s question using OpenAI’s embedding model at $0.0001 per 1,000 tokens, doing a vector search in your Pinecone database, retrieving relevant docs, and then using GPT-4 to generate an answer at roughly $0.03 per 1,000 tokens for input and $0.06 per 1,000 tokens for output.
Your LLMOps tool will show you the embedding costs and the GPT-4 costs. But it won’t show you that your Pinecone bill is $2,800 per month for storing 50 million embeddings and handling 2 million searches. It won’t show you the $1,200 per month you’re spending on the EC2 instances that run your API service. It won’t capture the $600 per month in S3 costs for storing conversation histories. It won’t track the $400 per month in data transfer fees.
When you add all these up, your total infrastructure cost for this feature might be $5,000 per month, but your LLMOps dashboard only shows $2,300. That’s a 54% gap between what you think you’re spending and what you’re actually spending. If you’re trying to calculate margins or price this feature appropriately, you’re making decisions based on incomplete data.
The fundamental issue here is that LLMOps tools were built by and for developers who care about optimizing LLM usage, not by finance teams who need to understand total cost of ownership. There’s nothing wrong with that focus, but it does mean these tools aren’t suited for comprehensive cost management.
The Third Party AI Tools That Never Touch Your LLMOps Infrastructure
Now let’s talk about an even more invisible category of AI costs: the third party AI powered tools your company uses. This is where the cost tracking problem becomes almost comical in how fragmented it gets.
Your company probably uses multiple AI tools as part of daily operations. Your sales team might use an AI SDR tool like Artisan or 11x for lead qualification. Your customer support team might use Intercom’s AI chatbot to handle tier one tickets. Your developers use Cursor or GitHub Copilot for coding assistance. Your HR team might use an AI recruiting tool to screen resumes. Your marketing team uses various AI content generation tools.
Every single one of these tools is consuming AI resources and costing your company money. Some charge you based on seats, some based on usage, some with hybrid models. But none of these costs flow through your LLMOps infrastructure because they’re completely separate applications. The LLM calls these tools make go directly from their infrastructure to model providers, not through your monitoring stack.
This creates a situation where your AI spend is scattered across dozens of line items in your expense management system, and there’s no unified view of total AI cost. When someone from finance asks “how much are we spending on AI?”, nobody can give an accurate answer without manually aggregating costs from OpenAI invoices, Anthropic bills, Pinecone subscriptions, Cursor seats, Intercom AI usage charges, cloud infrastructure costs, and a dozen other sources.
Let me give you a concrete example of how this plays out. Take a company like Perplexity, which is building an AI powered search product. Their engineering team might use their own LLMOps stack to track costs for their core product. But internally, their developers are also using Cursor for development, which is making its own API calls to Anthropic and OpenAI. Their customer support team uses Intercom’s AI features, which is running on Intercom’s infrastructure making its own model calls. Their sales team uses an AI tool for lead enrichment. Their finance team might even use an AI tool for processing invoices and expenses.
All of these are real AI costs that impact the company’s bottom line, but none of them show up in the LLMOps platform that tracks their product. The product costs might be $50,000 per month and well understood, but there’s another $30,000 per month in AI tool subscriptions scattered across different departments that nobody’s aggregating or optimizing.
This fragmentation is precisely why companies are struggling with AI cost management. The costs are real and growing rapidly, but the visibility is terrible. It’s like trying to manage your household budget when your spending is split across 20 different credit cards and bank accounts with no unified statement.
The Developer Centric Focus Misses Business Critical Questions
There’s another fundamental mismatch between what LLMOps tools provide and what businesses actually need, and it has to do with the questions each group cares about. This difference in perspective creates a gap that goes beyond just missing certain costs.
Developers using LLMOps tools care about questions like: Is this API call succeeding or failing? What’s the p95 latency? How can I optimize this prompt to use fewer tokens? Which model performs better for this task? Is caching working effectively? These are all important operational questions for building reliable AI applications.
But finance teams, product managers, and executives care about completely different questions: What’s our gross margin on each AI powered feature? How much does it cost us to serve a customer using our AI tools? Which customer segments are most profitable? How do our margins change as we scale usage? What’s our unit economics? Should we price this feature differently?
LLMOps tools simply weren’t designed to answer these business focused questions. They track technical metrics like tokens and inference time, but they don’t connect those metrics to business outcomes like revenue per feature or gross margin per customer. This disconnect means even when the LLMOps tool is accurately tracking all the costs it can see, it’s still not providing the insights that business stakeholders need to make strategic decisions.
Think about a product manager trying to decide whether to invest more in an AI summarization feature. The LLMOps dashboard can tell them that the feature consumed 50 million tokens last month at a cost of $2,400. But it can’t tell them that the feature was used by 8,000 customers, that those customers pay an average of $150 per month, that power users in the enterprise segment consume 10 times more tokens than SMB customers, or that the gross margin on this feature after accounting for all costs is 68% for enterprise customers but only 22% for SMB customers.
This kind of feature level profitability analysis is exactly what businesses need to make smart product and pricing decisions, but it requires connecting cost data to product analytics, customer segmentation, and revenue data. LLMOps tools don’t do this connection because it’s outside their scope. They’re focused on helping developers build better AI applications, not helping CFOs understand margins.
The result is that even companies with excellent LLMOps observability often have no idea whether their AI features are actually profitable. They know what the LLM API calls cost, but they don’t know the total cost to deliver value to customers, and they can’t calculate accurate gross margins. This is the kind of strategic blind spot that can lead to seriously flawed business decisions.
The Tool Call Execution Problem: When the Runtime Goes Invisible
Let me dive deeper into one particularly tricky technical limitation that illustrates how LLMOps proxies miss critical costs. This has to do with how modern AI agents actually work and where the execution happens.
When you’re building an AI agent using frameworks like LangChain or tools like n8n, the agent doesn’t just call an LLM and return a response. The agent might use tools, which means calling external APIs or running code to accomplish tasks. For example, an AI customer service agent might use a “check order status” tool that queries your order database, or a “send email” tool that actually sends an email through your email service.
Here’s the crucial technical detail: these tool calls are not executed by OpenAI or Anthropic. They’re executed by your runtime environment, like your Node.js server or your Python application. The flow works like this: your application calls the LLM, the LLM returns a response saying “I need to use the check order status tool with order ID 12345”, then your runtime executes that tool call using your own code, gets the result, and sends it back to the LLM for the next step.
From the perspective of an LLMOps proxy, it sees the initial LLM call and it sees subsequent LLM calls, but it has zero visibility into what happened during the tool execution phase. It doesn’t know that your runtime spent 2 seconds calling your order database and consuming compute resources. It doesn’t know that your tool call triggered a chain of operations in n8n or Zapier that each have their own costs. It can’t see any of the infrastructure cost associated with executing those tools.
This is exactly what makes agentic workflow cost tracking so difficult. The more sophisticated your AI system becomes, with agents using multiple tools and orchestrating complex multi step processes, the larger the gap grows between what your LLMOps tool tracks and what you’re actually spending to run these workflows.
I can give you a real example from the developer community that illustrates how dramatic this gap can be. A developer on Reddit shared that they spent $700 evaluating just 100 RAG question answer pairs using RAGAS for context precision evaluation. The actual LLM API costs were only about $150 of that total. The remaining $550 was consumed by the vector database searches, the embedding generations, the compute time for running evaluations, and the workflow orchestration costs. An LLMOps tool tracking only the LLM portion would have shown $150 and completely missed the other $550, making it look like the evaluation was 4.6 times cheaper than it actually was.
This kind of massive discrepancy is becoming more common as people build increasingly sophisticated AI applications. The simple “prompt in, response out” pattern that LLMOps tools handle well represents a shrinking fraction of total AI costs in production systems.
Why This Matters More Than You Think: The Margin Blind Spot
At this point, you might be thinking “okay, so LLMOps tools don’t track everything, but why does that really matter? Can’t we just add up the other costs separately?” This is a reasonable question, and the answer reveals why incomplete cost tracking is actually a strategic problem, not just an operational annoyance.
The core issue is that accurate cost tracking isn’t just about knowing how much you spent last month. It’s about understanding your unit economics and margins well enough to make smart business decisions. When you have significant blind spots in your cost data, you make decisions based on incomplete information, and those decisions can be seriously wrong.
Let me give you a realistic scenario. Suppose you’re a SaaS company that recently added an AI powered feature to help users analyze their data. You charge $50 per month extra for this AI feature. Your LLMOps dashboard shows that the average user costs you about $12 per month in LLM API calls. Based on this, you calculate that you have a healthy 76% gross margin on this feature. That looks great, so you decide to expand the feature and maybe even lower the price to drive adoption.
But remember all those costs the LLMOps tool doesn’t see? Let’s say the actual costs look like this: $12 in LLM API calls that your LLMOps tool tracks, $8 in vector database costs for storing and searching embeddings, $6 in compute costs for your API servers, $4 in data storage and transfer, and $3 in third party tool costs for workflow automation. The real total cost per user is $33, not $12.
Suddenly your gross margin isn’t 76%, it’s actually 34%. That’s still positive, but it’s less than half what you thought. At 34% margins, your strategy should be completely different. You probably shouldn’t lower the price to drive adoption because you don’t have enough cushion. You should be focused on optimizing costs before scaling. You might need to rethink your pricing model entirely.
This is the danger of making strategic decisions based on incomplete cost data. You think you’re optimizing for growth when you should be optimizing for efficiency. Or you might kill a feature that seems unprofitable based on partial data when it would actually have good margins if you could see the full picture and optimize accordingly.
The margin blind spot created by incomplete cost tracking affects every major business decision about your AI products. How should you price them? Which features should you invest in? Which customer segments are most profitable? Can you afford to offer a freemium tier? Should you build a new AI feature or focus on optimizing existing ones? All of these questions require accurate cost data, and LLMOps tools alone can’t provide that.
The Fragmentation Cascade: When Multiple Teams Use Different Tools
Let me show you how this problem compounds when you scale beyond a single team. Most mid market and enterprise companies have multiple teams building or using AI capabilities, and each team often ends up with their own tooling and tracking approach. This fragmentation creates a cascade of cost visibility problems.
Your data science team might be using Weights and Biases for their ML experiments, which has its own cost tracking for model training and evaluation. Your backend engineering team uses Langfuse for production API monitoring. Your product team uses LangSmith because it integrates well with their LangChain implementations. Your infrastructure team uses Datadog for overall cloud cost monitoring but that doesn’t capture detailed AI metrics. Meanwhile, different business units are using various third party AI tools that each have their own billing and usage dashboards.
The result is that nobody in your organization can answer the simple question: “What did we spend on AI this month across the entire company?” To get that answer, someone would need to log into a dozen different platforms, export data, normalize the formats, deal with different time zones and aggregation periods, and manually combine everything. Most companies simply don’t do this work because it’s too time consuming, so they operate with educated guesses about their total AI spend.
This fragmentation also means you can’t do meaningful cost attribution across your organization. You can’t easily answer questions like “which department is driving most of our AI costs?” or “which products are AI intensive versus AI light?” or “are our costs growing proportionally to our user base or faster?” The data exists somewhere across all these different tools, but there’s no unified view that makes it actionable.
Think about the impact this has on your finance team’s ability to forecast and budget. They need to predict AI costs for the coming quarter to set budgets, but they don’t have clean historical data because it’s scattered across multiple sources with different levels of granularity and accuracy. They can’t identify trends or patterns because nobody’s aggregating the data consistently. They end up either over budgeting to be safe, which ties up capital, or under budgeting and dealing with awkward conversations when teams exceed their allocations.
The fragmentation problem also makes it nearly impossible to implement company wide cost optimization initiatives. If you can’t see all your AI costs in one place and understand how they relate to business outcomes, how can you systematically identify and fix inefficiencies? You might optimize LLM costs in one team while missing huge wasteful spending in vector databases or workflow automation in another team.
What’s Actually Needed: A Different Approach to AI Cost Intelligence
Given all these limitations, what do companies actually need for proper AI cost management? The requirements are significantly different from what traditional LLMOps tools provide, and understanding these requirements helps clarify why the current generation of tools falls short.
First, you need comprehensive cross platform cost aggregation. This means pulling together costs from LLM providers, vector databases, cloud infrastructure, workflow automation platforms, and third party AI tools into a single unified view. No one can manage what they can’t see clearly, and AI costs are currently far too fragmented to see clearly.
Second, you need proper cost attribution that connects technical spend to business outcomes. It’s not enough to know you spent $50,000 on OpenAI last month. You need to know that $18,000 of that was for your document analysis feature used by enterprise customers at 73% gross margin, $22,000 was for your chatbot used by SMB customers at 31% gross margin, and $10,000 was for internal operations that don’t generate direct revenue. This kind of attribution requires tagging costs at the transaction level with metadata about which product feature, customer segment, and business process generated them.
Third, you need visibility into the full cost of agentic workflows, not just the LLM components. When a single customer interaction involves an LLM call, several vector searches, multiple tool executions, and workflow orchestration, you need systems that can trace and aggregate all those costs together as a single logical transaction. Most importantly, you need to be able to answer “what did it cost us to resolve this customer support ticket?” not “what did the LLM calls within this ticket cost?”
Fourth, you need the ability to connect cost data to revenue and customer data so you can calculate real margins. Finance teams need to see AI costs alongside the revenue those costs are generating, segmented by customer type, pricing tier, product feature, and other business dimensions that matter for decision making. This requires integration with your product analytics, billing systems, and customer data platforms.
Fifth, you need cost forecasting and predictive capabilities that account for the unique volatility of usage based pricing. Unlike traditional software where costs are relatively stable, AI costs can spike dramatically based on user behavior, new feature launches, or shifts in usage patterns. You need systems that can detect these changes early and help you model scenarios like “if we launch this new feature to 50% of users, what will it do to our costs?”
The point is that what’s needed goes well beyond traditional LLMOps capabilities. It’s not that LLMOps tools are bad at what they do. They’re excellent for their intended purpose of helping developers build and optimize AI applications. It’s that cost management for AI powered businesses requires a fundamentally different approach with different data sources, different integrations, and different focus areas.
The Window for Building This Is Closing Fast
One final point worth understanding is that the window of opportunity for building comprehensive AI cost management is narrowing. Multiple forces are converging that will make this problem either solved by existing platforms or much harder to solve as a standalone challenge.
Cloud providers like AWS, Google Cloud, and Azure are rapidly adding AI specific cost tracking to their native cost management tools. They already have comprehensive visibility into infrastructure spending, and they’re working to add better tracking for AI workloads. LLMOps platforms are racing to add more business focused features, budget controls, and integration capabilities. Traditional FinOps tools are building out AI specific modules. Even the LLM providers themselves are adding better cost management features to their platforms.
The question is whether any of these existing players can successfully bridge the gap between technical cost tracking and business cost intelligence. Each has inherent advantages and limitations based on where they sit in the stack. Cloud providers have great infrastructure visibility but limited sight into third party AI tools and SaaS applications. LLMOps tools have excellent detail on LLM usage but miss the broader cost picture. FinOps platforms have business context but struggle with the technical nuances of AI costs.
What’s most likely to emerge is a new category of tools purpose built for AI cost intelligence that sits at the intersection of all these existing categories. These tools would aggregate data from cloud providers, LLM APIs, observability platforms, and business systems to provide that unified view. They would focus specifically on the challenges unique to AI cost management like attribution for agentic workflows, connecting technical metrics to business outcomes, and forecasting usage based costs.
For companies grappling with AI cost management today, the practical reality is that current LLMOps tools are necessary but insufficient. You need them for developer observability and optimization, but you can’t rely on them alone for complete cost management. Until better solutions emerge, most companies are cobbling together partial solutions using data warehouses to aggregate costs from multiple sources, custom dashboards to visualize the combined data, and manual processes to connect costs to business metrics.
This is workable but inefficient, and it gets harder to sustain as AI usage scales across your organization. The limitation of LLMOps tools for cost tracking isn’t a trivial technical gap. It’s a strategic challenge that affects your ability to price products correctly, calculate margins accurately, forecast spending reliably, and make smart decisions about where to invest in AI capabilities. Understanding these limitations is the first step toward building or buying the cost intelligence capabilities your business actually needs.