We analyze one production AI workflow, benchmark cheaper alternatives and hand you a prioritized implementation plan. The goal is the lowest-cost configuration that still meets your required quality, latency, reliability and security thresholds.
An engagement today. A continuous optimization product in time.
Based in Helsinki. Serving Nordic B2B production AI teams.
Model prices are falling rapidly. But production AI systems are becoming more complex.
Adapterly analyzes the whole workload — not just the model layer.
Waste hides in six different places. The biggest savings almost always come from combining changes across layers. We're model-neutral — cheapest option that meets your quality bar, whether that's Claude, GPT, Gemini, Llama or a mix.
Use the cheapest model that meets the quality requirement — not the newest, not the biggest. Open-source models (Llama, Mistral, Qwen) often meet the bar at a fraction of the cost.
Send models only the information they actually need to answer the current question.
Find the expensive behavior hiding inside agent loops, retries and tool use.
Stop paying repeatedly for the same intelligence on the same inputs.
Not every problem needs an LLM. Identify the stable operations that can safely become code.
Optimize where and how AI workloads run once the logical stack is clean.
A five-step loop. Each step produces evidence the next step depends on. No optimization ships without verification.
The loop repeats — the model market changes weekly and the best fit for each task moves with it.
One end-to-end workload analyzed, plus where open-source substitutions typically save the most. All numbers illustrative — actual savings depend on your data.
When your data can't leave the EU (GDPR, sector rules, customer contracts), we host the open-source model on EU infrastructure — same optimization, no US round-trip. Frontier models like Claude and GPT can't offer this.
Complex reasoning, long-context synthesis, safety-critical outputs, and low-volume tasks (where hosting overhead outweighs API savings) usually still belong on a frontier model. The point isn't cheap — it's cheap enough at the required quality.
Two representative examples of what the analysis + implementation typically looks like. Numbers are illustrative — actual savings depend on your data and quality thresholds.
4,000 documents per day — invoices, safety reports, quality inspection forms — processed through a vision LLM. Customer requires EU data residency.
Blog drafts, LinkedIn posts, and multi-language email translations for a growing SaaS. All calls originally routed to Claude Sonnet.
Terrible fit for some organizations. Exceptional fit for others. We prefer to be clear about which you are before you talk to us.
A focused engagement that produces a defensible savings number and a prioritized plan — before you commit to anything larger.
Cost baseline · Optimization opportunities · Model comparison · Quality/cost analysis · Prioritized recommendations · Estimated annual savings.