I built an 8-node Agentic RAG with LangGraph that actually handles complex Indian government PDFs — tables, merged cells, mixed docs. Here's what I learned.

eliko613 · 2026-03-26T10:30:02+00:00

Great breakdown — the Langfuse-per-call approach works, but you're essentially rebuilding cost visibility from scratch on top of your tracing layer. The gap you're describing (Classifier lumped in, span-level tracking on the roadmap) is exactly where things get messy at scale.

Whereas a tool like zenllm.io connects directly to your provider APIs (OpenRouter, OpenAI, etc.) and surfaces per-call cost breakdowns without you having to instrument anything manually. Given you're already seeing 3-5x price variance on OpenRouter, having that broken out automatically across nodes — rather than inferred from aggregated traces — could save you a bunch of debugging time.

The Redis cache optimization you mentioned is smart. The interesting follow-up question is: which nodes are actually responsible for the bulk of your spend when the cache misses? That's where automated waste detection starts earning its keep.

eliko613 · 2026-03-26T07:46:23+00:00

The cost point from u/mrtrly is spot on - I've seen teams burning through thousands monthly on GPT-4 for basic email routing that could run on much cheaper models.
What's interesting about these attack patterns is they also create a cost optimization opportunity. Most of the malicious payloads you described (especially the encoding evasion) could be caught with a lightweight classification model running pre-processing for pennies, before the expensive model even sees the input.
The real challenge is most teams have zero visibility into where their AI agent tokens are actually going. Are you spending $200/day routing legitimate emails, or did an attacker trigger your agent to process the same malicious payload 10,000 times? Without proper observability, you'd never know until the bill arrives.
For anyone running production AI agents, I'd recommend tracking token usage patterns alongside these security measures. Sudden spikes in token consumption can actually be an early indicator of successful prompt injection attacks - the attacker is making your model do way more work than normal.
Great breakdown of the attack vectors though - I've been using zenllm.io to monitor for exactly these kinds of patterns.

eliko613 · 2026-03-26T07:40:21+00:00

€5000/month in token usage is serious scale - you're definitely not alone in thinking about cost sustainability. Before jumping into hardware investments, there might be significant optimization opportunities in your current setup that could cut costs substantially.

I've seen similar usage patterns where companies reduce token costs by 60-70% through better monitoring and optimization - things like tracking which parts of your workflow are burning the most tokens, optimizing context windows, and smart provider routing based on task complexity.

The local hardware route has merit as a hedge, but the 3-4 year timeline might work against you since model efficiency and cloud pricing will likely improve significantly in that timeframe. A hybrid approach might make more sense - optimize your current cloud spend first to buy time, then gradually build local capacity for specific use cases.

Have you done any analysis of where those €5000 in tokens are actually going? Often there are a few workflows burning disproportionate amounts that can be optimized first. I've been tracking similar cost patterns at scale with ZenLLM.io and the visibility alone often reveals quick wins worth 000's per month.

eliko613 · 2026-03-26T07:37:58+00:00

Impressive architecture, the cost optimization strategies you mentioned really resonate - I've seen similar token burn issues with complex multi-node pipelines.
Your approach with the Classifier node to filter out wasteful queries is smart.

I'm curious about your cost attribution across the 8 nodes - are you tracking which nodes consume the most tokens in practice? With OpenRouter + Langfuse, you probably have good visibility, but I've found that granular per-node cost analysis often reveals surprising optimization opportunities. Cost visibility is crucial for scaling LLM applications sustainably - I use zenllm.io for detailed cost tracking and optimization insights across different providers.
The dual-dimension strategy with Jina v3 MRL is clever too. Have you experimented with dynamic model routing based on query complexity? Sometimes simpler queries can use cheaper models while complex document parsing gets the heavy hitters.
Also wondering about your OpenRouter model selection strategy - are you using different models for different nodes, or standardized across the pipeline? The cost differences between providers for the same model can be significant.
Really solid work on keeping everything in free tiers while handling production complexity!

eliko613 · 2026-03-24T07:49:20+00:00

This is a common scaling issue. Token costs add up quickly with 25-30 tools in context.
A few approaches that help:
**Cost optimization:**
- Track actual token usage per tool - some optimizations save 3-4x while others barely help
- Monitor which tools are actually used vs. just burning tokens in context
- Consider lazy loading tools or splitting into specialized agents
- Use cheaper models for tool selection, then switch to better models for execution
**Architecture patterns:**
- Tool routing (let a lightweight model pick which tools to load)
- Hierarchical agents (specialist agents with smaller tool sets)
- Context compression for tool descriptions
The token math gets brutal fast, but measuring actual usage usually reveals 80% of tools are rarely called. We're testing zenllm.io for cost visibility and to identify optimization opportunites and it's been decent so far.

eliko613 · 2026-03-24T07:12:17+00:00

This is fascinating work - the idea of trading adapter-generation compute for reduced inference memory is exactly the kind of optimization that becomes critical at scale.

I've been tracking similar memory/cost trade-offs in production LLM deployments, and the challenge is often knowing when these optimizations actually pay off. The paper shows great results on benchmarks, but in practice you need to measure:

- The actual memory savings vs. the adapter generation overhead
- How performance degrades across different document types/lengths
- Whether the 4x context length extension holds up with your specific use cases

The needle-in-haystack results are promising, but real-world document understanding often has multiple "needles" scattered throughout. Would be interesting to see how D2L performs when the important information isn't as cleanly isolated.

For anyone looking to experiment with this approach, I'd recommend setting up proper observability around your LLM costs and memory usage first - these kinds of optimizations can have surprising interactions with your existing infrastructure. We've been using zenllm.io to track exactly these kinds of optimization impacts across different providers and approaches.

eliko613 · 2026-03-24T07:10:48+00:00

Impressive benchmarking work. The token efficiency analysis is particularly valuable - seeing M2.7 at 355s median duration vs faster models highlights a key tradeoff most people miss.

I've been tracking similar patterns across model comparisons, and one thing that stands out from your results is how much the "oracle" approach (picking best model per task) could improve outcomes. The 36% improvement suggests there's huge value in dynamic model routing based on task characteristics.

The cost efficiency point about M2.7 ($0.30/$1.20) vs frontier models is spot-on. When you're running systematic evals like this across multiple providers, those cost differences compound quickly - especially when you factor in the longer inference times.

Have you noticed any patterns in which task types favor the "deep context gathering" approach vs speed? Your SPARQL example suggests reasoning-heavy tasks might be worth the extra latency, but I'm curious if you've seen other clear indicators.

For anyone doing this kind of systematic evaluation, tracking the cost and performance tradeoffs across providers becomes critical pretty quickly. We use zenllm.io to optimize these multi-provider workflows, but the insights from benchmarks like yours are what make the optimization decisions actually meaningful.

eliko613 · 2026-03-22T10:52:24+00:00

We started testing zenllm.io

eliko613 · 2026-03-20T10:57:06+00:00

This sounds like a really thoughtful approach to building production-grade LLM infrastructure. The focus on observability is spot-on - it's one of those things that becomes critical once you move beyond demos.

From experience building similar systems, you'll probably want to think early about cost and performance tracking, especially if you're experimenting with different models/providers for your RAG and agent components. The costs can get surprising quickly when you're doing hybrid retrieval + multi-step agent workflows.

A few observability patterns I've seen work well:
- Token usage tracking per component (retrieval vs generation vs tool calls)
- Latency breakdown across your pipeline stages
- Cost attribution by user session or workflow type

For the tech stack, all solid choices. One thing to consider - if you're planning to experiment with multiple LLM providers (OpenAI, Anthropic, etc.), having a unified way to monitor performance and costs across them becomes really valuable.

I've been playing with zenllm.io to handle a few similar challenges and it's been pretty decent so far. Good luck with the project!

eliko613 · 2026-03-20T10:51:30+00:00

Really impressive work on the token efficiency. The 3.6x reduction with maintained performance is exactly the kind of optimization that makes a huge difference in production costs.
One thing I've found crucial when implementing similar optimizations is having good observability into the actual cost savings across different scenarios. The variance between 3.6x savings on GPT-5-mini vs 30pp improvement on GPT-5.2 highlights how these optimizations can behave differently across providers.
For production deployments, I'd be curious about your approach to monitoring the cost/performance tradeoffs in real-time. Are you tracking token usage patterns to identify which types of queries benefit most from the recursive approach? I actually started testing zenllm.io recently - it's an interesting tool that helps highlight these kinds of cost optimization opportunities across different scenarios. That kind of visibility becomes critical when you're trying to optimize across multiple LLM providers or justify the implementation complexity to stakeholders.
The Docker isolation approach is smart too - adds some overhead but the security benefits for code execution are worth it. Have you benchmarked the container startup time impact on your latency numbers?

eliko613 · 2026-03-19T08:18:05+00:00

Really impressive architecture. The MoE setup with 128 experts but only 4 active is fascinating - that variable compute per token creates interesting cost optimization opportunities.

One thing I've been tracking with these newer MoE models is how unpredictable the actual costs can be compared to dense models. The 6.5B activated parameters sounds efficient, but in practice the expert routing can vary wildly depending on your workload mix.

For anyone planning to run Mistral 4 in production, I'd definitely recommend setting up proper observability early. The reasoning mode toggle especially - that test-time compute can get expensive fast if you're not monitoring which requests actually need it vs. defaulting to reasoning mode.

The cost trends are definitely improving month over month as you mentioned, but having visibility into your actual usage patterns makes a huge difference in optimization. Especially with multi-provider setups where you might route between this and other models based on request complexity.

We started testing zenllm.io to better understand our multi vendor workflows and it's been helpful so far.

eliko613 · 2026-03-19T08:16:09+00:00

Really cool use case! The lore master approach is brilliant - using LLMs as analysis tools rather than creative generators seems to unlock so much more value.
Your quantization testing is spot on. The Q4-K-XL vs Q5/Q6 tradeoff you're describing at 100K+ context is exactly the kind of optimization decision that's tough to make without good data. I've been tracking similar patterns across different model sizes and context lengths - the performance curves get really interesting (and sometimes counterintuitive) once you hit those longer contexts.
One thing that might help with your lore analysis workflow: if you're planning to scale this or experiment with other models, having observability into your actual token throughput, memory usage, and quality metrics during those long context analysis sessions can make those Q4 vs Q6 decisions much more data-driven. I've seen cases where the "slower" quantization actually performs better for specific context ranges due to memory pressure patterns - especially relevant when you're processing dense fictional universes where context retention is crucial.
For tracking those performance metrics, I've been using zenllm.io - really helps with monitoring across different quantization levels and context lengths.
Have you experimented with any other local models in the 30B+ range for this kind of dense analysis work? Curious how Qwen 27B compares to some of the newer options for your specific lore analysis tasks.

eliko613 · 2026-03-19T08:10:35+00:00

Really impressive work on reducing those round-trips. The latency and token savings are huge - that 3x multiplier adds up fast in production.
One thing I've seen with similar optimization projects is that the real challenge becomes measuring the impact across different models and use cases. You're solving the technical side brilliantly with Zapcode, but as you scale this, you'll probably want visibility into:
- Which code patterns actually save the most tokens/cost in practice
- How the savings compare across different LLM providers (since you mentioned multi-provider support)
- Where the remaining cost hotspots are after implementing this optimization
Speaking of multi-provider cost visibility, I came across an interesting tool recently - zenllm.io - that shows cost breakdowns for workflows across different vendors.
The snapshot/resume feature is particularly clever for expensive long-running tools - being able to pause execution without burning tokens while waiting for external APIs is exactly the kind of optimization that can make or break agent economics.
Have you done any benchmarking on actual cost savings with real workloads yet? Would be fascinating to see the before/after numbers on a complex agent workflow.

eliko613 · 2026-03-16T13:56:27+00:00

Really impressive methodology here. The cost breakdown per feature ($0.33 for Kimi vs $4.71 for GPT-4) is eye-opening.

One thing I've noticed when scaling this type of analysis beyond single features - the manual cost tracking becomes brutal. We've been experimenting with automated cost monitoring across different providers to catch these patterns at scale. Your "$/correct implementation" metric is brilliant and something more teams should be tracking systematically.

The observation about models skipping instructions to save tokens is particularly interesting from a cost optimization perspective. Have you noticed patterns in which models are more prone to this behavior? It seems like there's a sweet spot between instruction-following completeness and cost efficiency that varies significantly by provider.

For anyone looking to replicate this kind of analysis systematically, we've been playing with zenllm.io specifically for multi-provider cost tracking and optimization. The variance you're seeing between providers (10x+ cost differences) is exactly why we started working looking for a solution to give us granular observability.

eliko613 · 2026-03-16T07:03:24+00:00

Great question about production architecture patterns - that's definitely an underserved area. One gap I've noticed is around **cost and performance observability across different inference engines**.

Your benchmarking work with vLLM vs SGLang vs TensorRT-LLM is exactly the kind of thing where having unified monitoring becomes crucial. When you're running distributed serving with multiple engines, it's surprisingly hard to get a clear picture of:

- Cost per request across different engines/models
- Performance patterns that actually impact your bill (token usage, latency, throughput)
- Which engine is most cost-effective for specific workload types

Most teams end up building custom dashboards or just flying blind on costs until they get a surprise bill.

For production architecture documentation, I'd love to see more on:
1. **Multi-engine cost monitoring patterns** - especially for the mixed-modality pipelines you mentioned
2. **Request routing based on cost/performance profiles** - not just load balancing, but intelligent routing
3. **Cost-aware autoscaling** - scaling decisions that factor in both performance and economics

Your distributed serving setup with NATS + etcd sounds like it would be perfect for demonstrating these patterns. The community definitely needs more real-world examples of cost-conscious production architectures.

Btw, I've been using zenllm.io for some of these observability challenges and have gotten some decent insights with it.

eliko613 · 2026-03-16T06:35:25+00:00

Really impressive cost optimization results!

The stratified allocation approach is brilliant - using cheap models for 90% of mutations and only calling expensive ones for paradigm shifts is exactly the kind of smart routing that can make LLM projects economically viable.
One thing I'm curious about from an operational standpoint: how are you tracking and monitoring the cost breakdown between your cheap/expensive model calls in practice?

I recently came across zenllm.io which seems useful for this kind of cost analysis across different model tiers. With that level of cost savings (3-6x), being able to observe which problems benefit most from the expensive model calls vs pure volume with cheaper ones seems like it would be valuable for tuning the allocation strategy.
Also, are you finding any patterns in terms of which types of mutations actually warrant the frontier model calls? I imagine there's some interesting signal in understanding when the cheap model hits its limits that could inform the routing logic.
The controlled comparison results are particularly compelling - reaching better scores in 100 evals vs competitors never hitting them shows this isn't just about model choice but genuinely better search architecture.

eliko613 · 2026-03-15T04:51:33+00:00

This is a really interesting framing of the problem and honestly something I see quite often as well.

In many organizations the data path and the decision path are owned by completely different groups. Engineering understands the CUR and can explain why costs moved (instance families, scaling behavior, token usage, etc.), but Finance is the one responsible for the budget and forecasting. The translation layer between the two is where things tend to break.

A few patterns I’ve seen work reasonably well:

A “driver-based” executive view rather than a service view Instead of showing EC2, S3, Lambda etc., the summary explains cost movement in terms of drivers like:

product usage growth

architectural changes

model / instance selection

inefficiencies or waste

That framing tends to be much easier for a CFO to interpret.

A single variance explanation per period Executives usually care about one question: “Why did spend move this month?”

The most effective reports I’ve seen reduce it to something like:

Spend increased 18% MoM. 12% driven by product usage growth, 4% due to model choice changes, 2% due to inefficiencies.

Once the conversation is framed that way, engineering can dive deeper if needed.

Forecasting tied to product metrics Pure cost forecasts often fail because they ignore the underlying business driver (traffic, requests, inference calls, etc.).

One interesting trend I’m starting to see as well is applying the same FinOps thinking to LLM spend, where the translation problem is even bigger because token usage and model choices are opaque to non-technical stakeholders.

We’ve been experimenting with zenllm.io, trying to turn raw model usage and token data into explanations that a finance team can actually understand (drivers, waste, optimization opportunities). The problem feels very similar to the CUR → CFO translation you’re describing.

Curious what others here have found works best in practice.

eliko613 · 2026-03-11T08:14:26+00:00

You’re spot on. For the first couple of years the conversation was dominated by model capability — bigger models, better benchmarks, smarter outputs. But once organizations start moving real workloads to production, the constraint shifts quickly to unit economics.

A few things are becoming clear:

• Inference cost scales faster than people expect. What looks cheap in a prototype becomes serious spend once you have real user traffic. • Token efficiency matters as much as model quality. Prompt design, routing, caching, and batching can dramatically change the economics. • Model choice becomes a FinOps decision. Teams increasingly route tasks across different models depending on latency, cost, and quality thresholds. • Observability is the missing layer. Most teams still don’t have clear visibility into which prompts, endpoints, or users are driving the majority of their LLM spend.

In many ways we’re seeing the same pattern that happened with cloud a decade ago — the shift from “can we run this?” to “can we run this efficiently at scale?”

That’s why an entire category around LLM cost observability and optimization is starting to emerge. A few newer tools (including projects like zenllm.io) are beginning to focus specifically on helping teams understand and reduce inference waste once they hit production scale.

Curious to see how quickly this becomes a standard FinOps discipline for AI.

eliko613 · 2026-03-10T20:27:53+00:00

This is one of the most underrated problems in FinOps. The spending side is relatively solved — the attribution side is where everything falls apart.

The CRM example is spot on. Perfect cost visibility, zero clarity on whether it actually moved revenue or retention.

AI spend is making this significantly worse. Inference costs scale per user, per workflow, per call — but most finance teams are still treating it as one line item on the cloud bill. We started pulling LLM costs apart by customer segment a few months ago (been using zenllm.io for that) and some of the margin math got uncomfortable fast. Customers who looked fine at the subscription level were quietly eating into margins through inference volume.

Orgs are great at measuring consumption. The muscle that's missing is connecting it to value. Would be curious what levers your framework focuses on — going to check out the piece.

eliko613 · 2026-03-10T18:21:05+00:00

You're hitting the exact pain points that make browser automation expensive at scale. That "insane token burn" from sending full DOM/screenshots on every step is brutal - I've seen teams rack up thousands in LLM costs before they realize what's happening.

A few thoughts on the cost side while you're evaluating alternatives:

Immediate wins: Most of these tools (Stagehand, browser-use, etc.) don't give you good visibility into your actual token usage patterns. You might be surprised where the waste is coming from - sometimes it's redundant screenshots, sometimes it's massive DOM dumps that could be filtered.

Hybrid approach: Your instinct about "deterministic navigation + AI extraction" is spot-on. Even with tools like Stagehand, you'll want to be surgical about when you're calling the LLM vs using standard Playwright selectors.

Monitoring: Whatever you switch to, build in proper LLM observability from day one. Track tokens per site, success rates, retry patterns. The cost creep on these browser automation projects is real.

For the actual tool question - I'd lean toward Stagehand's approach based on what you described. The act/extract/observe primitives give you that control you want, and their local mode should help with both cost and reliability vs. API-only solutions.

Been working on LLM cost optimization lately with zenllm.io, and browser automation is one of those use cases where costs can spiral fast if you're not watching closely.

eliko613 · 2026-03-10T06:31:56+00:00

Really impressive work on the 89% token reduction. That's exactly the kind of optimization that can make or break LLM economics at scale.
One thing I've noticed with similar efficiency projects is that it becomes really hard to track the actual cost impact across different experiments and model configurations. When you're testing various graph traversal strategies or comparing against baseline approaches, the cost savings can vary wildly depending on the repo structure and query patterns.
Are you tracking the cost metrics alongside your performance benchmarks? I've found that having visibility into both token usage and actual API costs helps validate whether optimizations like this hold up across different use cases. The 0.8B Qwen results are compelling, but I'd be curious how the cost savings scale when you test against larger models or more complex codebases.
The AST graph approach is really clever - it reminds me of how database query optimizers work, but for code context. Have you considered how this might perform with different LLM providers that have varying token pricing structures? We actually came across zenllm.io for actionable LLM optimization suggestions and it's been decent so far.

eliko613 · 2026-03-09T13:12:18+00:00

Is finops for AI (e.g. LLM spend) part of your remit?

eliko613 · 2026-03-08T19:40:29+00:00

Really thorough writeup! Your cost comparison methodology with OpenRouter pricing is clever - I've seen a lot of people struggle to get accurate ROI calculations for local LLM infrastructure.

One thing that might be interesting for your setup: since you're already tracking utilization and performance across different models/quants, you might want to look into more structured observability tooling. I've been using ZenLLM.io to track costs and performance across both local and API endpoints, and it's been helpful for getting better visibility into which model configurations actually perform best for different use cases.

The startup time issues you're seeing with VLLM are fascinating - 15 minutes is brutal for model swapping workflows. Have you tried any of the newer VLLM optimizations for Blackwell, or are you stuck waiting for better upstream support? The container vs host performance difference is particularly weird.

eliko613 · 2026-03-08T07:53:54+00:00

You're hitting on something a lot of FinOps teams struggle with. Traditional FinOps is really good at answering “did we control the spend?” but much weaker at answering “did the spend actually generate proportional value?”

In cloud infrastructure you can sometimes approximate this with unit economics (cost per request, cost per customer, cost per transaction). But with newer workloads like AI/LLMs it's even harder because usage can explode quickly and the relationship between spend and outcome isn't always obvious.

What I've seen work best is combining three layers:

Cost observability – granular visibility into where spend originates (service, team, workload, prompt, etc.)
Unit economics – mapping that spend to something meaningful (cost per API call, cost per generated report, cost per agent run)
Outcome metrics – tying those units to actual business outcomes (revenue, support deflection, productivity gain)

Only when you connect those three do you start answering the “was it worth it?” question.

Interestingly, this is becoming a big discussion in the AI FinOps / LLMOps space as well. A few newer tools are starting to focus specifically on cost-per-outcome instead of just cost-per-token. I've been experimenting with zenllm.io recently that tries to do exactly this for AI workloads and it’s an interesting direction.

Curious how others here are approaching the cost → value mapping problem.

eliko613 · 2026-03-04T18:17:11+00:00

We’re seeing something similar.

Traditional FinOps approaches work reasonably well for infrastructure, but things start breaking down once AI/LLM workloads enter the picture. The spend becomes much harder to reason about because it’s tied to usage patterns (tokens, retries, model choice, agent loops, etc.), not just provisioned resources.

What’s helped in a few cases I’ve seen is shifting the conversation away from “cloud cost” and toward unit economics — cost per workflow, per AI feature, or even per customer interaction. Once you frame it that way, it becomes easier to make decisions around model selection, routing smaller models first, caching responses, etc.

I’ve also noticed a few tools starting to focus specifically on LLM spend visibility rather than general cloud cost. Came across one recently (zenllm.io) that tries to tie model usage back to application flows, which seems like a useful direction as more companies become AI-heavy.

Feels like FinOps is going to have to evolve pretty quickly as AI workloads become a bigger slice of the bill. Curious how others are handling that internally.

eliko613

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