// Production / Agentic Workflows & RAG
Cut your LLM bill 10x.
Most teams optimize by vibes - swap a model, hope quality holds, ship. We do it backwards. Build the eval first. Then grind smaller models, different providers, structured outputs, caching, parallelism, fine-tuned specialists. Every change ships with a measured quality, latency and costs delta.
// What we see
Cost and latency wins ship with quality regressions you don't see.
01
Every prompt change is a cost guess
The team trims a prompt, swaps a model, or refactors a step. Cost goes down on the dashboard. Whether quality moved with it - nobody knows. The next regression shows up in a customer ticket.
02
Smaller-model swaps fail silently
Haiku replaces Sonnet on a step. The five test cases the team checked still pass. The 8% of conversations that depended on long-range coherence quietly degrade. By the time someone notices, the cheaper model is in production.
03
Provider switching is theater without evals
Three providers, three pricing tiers, three personalities. Without an eval on your data, the comparison is whichever one looked good in a dashboard demo. With evals, the right answer often isn't the obvious one.
// What we do
Three layers that turn guesswork into engineering.
Optimization isn't a single trick. It's a measurement layer, a substitution layer, and the long-tail of fine-tuned specialists. We wire all three so the team can keep grinding after we leave.
Eval baseline + cost/latency observability
Before a single optimization, we wire the eval suite against your current system and per-step cost/latency tracking. Every change after this measures itself against the baseline. No move ships without a delta.
- Eval suite mined from production traces, not happy paths
- Per-step cost, p50/p90/p99 latency via Langfuse / OTel
- LLM-as-judge graders calibrated against a human-graded sample
- Critical-path analysis - which steps actually eat the budget
Model & provider grinding
With evals in place, the substitution work becomes empirical. Smaller models, different providers, structured outputs, caching, parallelism. Every candidate change benchmarked, kept or reverted on the eval delta.
- Model cascades - Haiku / Gemini Flash / Llama-3.1 8B for 80% of calls, frontier for the rest
- Provider sweeps - Anthropic, OpenAI, Gemini, self-hosted vLLM benchmarked on your data
- Structured outputs to eliminate parser-retry tax
- Prompt caching, embedding caches, semantic caching, parallel tools
Fine-tuned specialists where the math says yes
When the eval shows a step that a 7B fine-tune could nail at 1/100th the cost of an API call, we ship that fine-tune. LoRA adapters routed by request type, served on your inference layer, evaluated continuously.
- LoRA / QLoRA fine-tunes on Llama, Qwen, Mistral
- Synthetic data generation distilled from your frontier-model traces
- Multi-adapter routing on a single base model deployment
- Continuous eval so the specialist stays good as data drifts
// Method fit
Optimization isn't the right move for every system.
skip it if
The LLM bill is small and latency is fine
If you're spending $200/month on LLMs and p50 is acceptable, the engagement cost is bigger than the savings.
To give you idea - we would aim for max of 3 month ROI. Planning ROI for 2 years makes no sense when models fundamently change every quarter.
You're still pre-PMF and the workload keeps shifting
Optimizing a system that's getting rewritten next sprint is wasted work. Ship the thing, find product fit, then optimize what stabilizes. Premature optimization is the same kind of trap in LLM systems as anywhere else.
The task has no quality ceiling
Open-ended generation - articles, complex problem solving, coding. Honest take from our engagements: with today's models, you're better off investing in quality there, not cutting cost. Otherwise your competitors will and users will flock to them.
use it if
Two patterns we mostly take. First: you're already at scale. $1k+/day on LLM inference, happy enough with output quality, looking to cut the bill without breaking what works. The savings often cover the engagement inside the first month.
Second: you're not at that scale yet, but the unit economics block you from getting there. The product would be viable at $0.005 per request and your current cost is $0.40. Closing that gap turns a non-viable product into a shippable one. Same playbook, different ROI math.
Either way: eval suite week one. Provider grinding, caching, cascades, parallelism. Fine-tuned specialists where the math justifies them. Hand off the eval gates so the team keeps grinding after we leave.
// How we work
Wire evals first. Grind providers next. Fine-tune the long tail.
Every engagement starts the same way: make the cost, latency, and quality of every step legible. Once we can see what's happening, the optimization work becomes empirical - try, measure, keep or revert.
01
Wire evals + cost/latency observability
Eval suite built from production traces. Per-step cost and p50/p90/p99 latency tracking via Langfuse, LangSmith, or OTel direct. By end of week one we have the baseline number we'll measure every change against.
02
Grind models, providers, and orchestration
Model cascades. Provider sweeps. Structured outputs. Prompt caching. Parallel tool execution. Each candidate benchmarked on the eval suite. Kept if quality holds, reverted if it doesn't. Most engagements land 60–85% cost and 50–80% latency reductions in this phase alone.
03
Fine-tune specialists, hand off the eval gates
Where the eval shows a step a 7B LoRA could handle at a fraction of the cost, we train and deploy it. Then we hand off: the eval suite runs in CI, the cost dashboards keep tracking, the team keeps grinding without us.
// Expert insight
“Most teams chase latency by trying a few models and shipping the smallest one that works. You should absolutely do that - but it's the floor, not the ceiling. Fine-tuning a smaller model on a high-volume step often cuts cost 90–99% with no measurable quality loss. Or we redesign the task and the user gets 300ms latency inted of 3 minutes.”
Michał Pogoda-Rosikoń
Co-founder @ bards.ai
// Why bards.ai
Why us, instead of a prompt engineer from Upwork.
A prompt engineer can rewrite your prompts. They can't build the eval suite that proves the rewrite worked, run the provider sweep that finds the cheaper option, or fine-tune the specialist that makes the cost go away entirely.
Eval-first, always
Every optimization ships with a measured delta on the same eval suite. No silent regressions to land a cost win.
1B+ tokens/day in production
We've operated LLM platforms at the scale of a top-10 SEO product. Cost and latency optimization is muscle memory.
Sub-5s agents on real workloads
Brand24's agent hits sub-5s median across 13 sources - through engineering, not provider switching.
16+ open-source models on Hugging Face
80K+ monthly downloads. We know which open-weight model can replace which API call - and which can't.
Fine-tuning is in-house
LoRA, QLoRA, DPO, GRPO. We don't subcontract the long-tail wins - we deliver them ourselves.
Senior team, no juniors
Every engineer has shipped LLMs to production at scale. We don't bring ramp-up time to your optimization sprint.
// FAQ
Common questions about LLM cost & latency optimization
You can. You'll just regress quality and not know it. Every team that skips the eval step ends up arguing about whether output got worse - without data - three weeks later. The eval is a one-week investment that makes the entire rest of the engagement empirical instead of subjective.
You probably do - you just haven't mined your trace store yet. Production traces from the last 30 days, plus customer complaints and support tickets, give us a starting golden set within days. We grow it as new failure modes surface during the engagement.
60–85% cost reduction and 50–80% latency reduction at p50, with quality regressions below the noise floor of the eval suite. Specifics depend on starting state - agents already using prompt caching and small-model routing get smaller wins, agents on a single frontier model for everything get bigger ones. We'll tell you the number after week one based on your traces.
Multi-step planning, ambiguous tool-selection, and tasks requiring long-range coherence are where small models fall off - sometimes silently. We benchmark per-step on your data and only promote a small model where the eval gap is within tolerance. For routing and structured-output steps, small models often match frontier quality at 5–20% of the cost.
When the eval shows a high-volume step where a 7B LoRA fine-tune lands within tolerance of the API output. The math is cost-per-1M-tokens × volume vs. our engagement plus inference hosting. For repetitive tool-calling, structured extraction, or domain-specific summarization at scale, fine-tunes usually pay back in weeks, not quarters.
Side-by-side runs on the same eval suite, same inputs, same temperature controls. We measure quality, cost per 1M tokens, p50/p90/p99 latency, structured-output reliability, and rate-limit behavior. The winner is per-step, not per-provider - most production agents end up using two or three providers with traffic routed by what each one is best at.
No. Optimization layers cleanly on top of LangGraph, Pydantic-AI, LlamaIndex, OpenAI Assistants, and most custom Python orchestration. We instrument first, find the actual bottleneck, and patch incrementally. Full rewrites happen only when the orchestration framework itself is the bottleneck - which is rare.
// Let's ship it
Cut your LLM cost and latency - without breaking what already works.
Tell us your current $/run, p50, and quality bar. We'll come back with a profiling plan and a target number - usually within a business day.
Michał Pogoda-Rosikoń
Co-founder @ bards.ai