Our Open-Source RegTech Stack: From €5,400/mo to €800/mo (80% Cost Reduction)

Kyonis — Sun, 15 Mar 2026 23:24:10 +0000

We built Kyonis, an agent-native KYC/AML compliance API. Before writing a single line of code, we made a critical decision: 100% open-source stack.

The result? An 80% reduction in infrastructure costs compared to a proprietary stack. Here's exactly what we use and why.

The Cost Comparison

Component	Proprietary	Open-Source	Savings
Cloud hosting	€3,000/mo (AWS)	€400-800/mo (Scaleway)	75%
LLM/AI	€1,500/mo	€200-400/mo (local + API)	80%
Monitoring	€500/mo (Datadog)	€0 (Grafana)	100%
Cache	€100/mo (Redis Cloud)	€0 (Valkey)	100%
Search	€200/mo (Elastic)	€0 (PostgreSQL pg_trgm)	100%
API Gateway	€100/mo (Kong)	€0 (Traefik)	100%
Total	€5,400/mo	€800-1,200/mo	~80%

That's €75,000 saved over 18 months.

Our Stack

Backend: FastAPI + Go

The API layer uses FastAPI (Python) for its async capabilities and auto-generated OpenAPI docs. But for sanctions screening — where we need sub-500ms response times across 100K+ entries — we use a Go microservice with in-memory fuzzy matching.

Why not all Go? Because Python has the best AI/ML ecosystem (spaCy, HuggingFace, sentence-transformers). Why not all Python? Because Go gives us 10x throughput on the hot path.

Database: PostgreSQL Does Everything

No Elasticsearch. No dedicated search engine. PostgreSQL 16 with:

pg_trgm for fuzzy name matching (sanctions screening)
tsvector for full-text search
GIN indexes for fast trigram lookups
Partitioned tables for the audit trail (7-year retention)

One database instead of three. Simpler ops, fewer things that break at 3am.

Cache: Valkey (not Redis)

Redis changed its license. Valkey is the Linux Foundation fork — 100% compatible, truly open-source. Drop-in replacement, zero migration effort.

Message Queue: NATS (not Kafka)

Kafka requires ZooKeeper, JVM tuning, and a PhD in distributed systems. NATS is a single binary, 10MB RAM, and handles everything we need. JetStream gives us persistence when required.

AI: Hybrid Strategy (80% Local, 20% Cloud)

This is where it gets interesting:

Level 1 — Ollama (Mistral 7B): Text extraction, classification, summarization. Cost: ~€0.
Level 2 — spaCy + HuggingFace: NER for names, companies, addresses in KYC documents. Cost: €0.
Level 3 — Claude API (20% of requests): Complex risk profiles, adverse media analysis, regulatory reports. Cost: ~€200-400/mo.
Level 4 — Deterministic rules engine: Hard-coded regulatory thresholds, auditable scoring. Cost: €0.

80% of requests never hit a paid API. The local models handle extraction and classification. Claude only steps in for complex reasoning.

Monitoring: Grafana Stack

Grafana + Prometheus + Loki replaces Datadog. Same dashboards, same alerts, zero monthly bill.

Infrastructure: Scaleway Kapsule

We chose Scaleway over AWS:

Kubernetes managed (Kapsule) — no cluster management overhead
Paris datacenter — GDPR native, data stays in France
3-5x cheaper than equivalent AWS setup
Startup program with up to €36K credits

Data: Open Data First

12 data sources, all free:

Sanctions: OFAC, EU, UN, HMT, DFAT (government APIs, free)
Company registries: API Sirene (France), Companies House (UK), SEC EDGAR (US) — all free
Adverse media: GDELT Project (free, updated every 15 minutes)
Offshore leaks: ICIJ database (Panama/Pandora Papers, free)
UBO: OpenOwnership registry (free)

Total data cost at launch: €0/month. We add OpenSanctions (€500/mo) when we hit 50+ paying customers.

The Result

An API that:

Screens against global sanctions in <500ms
Runs full KYC verification in <3 seconds
Includes explainable reasoning in every response
Is discoverable by AI agents via MCP
Costs €95/month to run (not €5,400)

Try It

Free Sandbox plan: 500 verifications/month, no credit card.

🌐 kyonis.com
📖 Documentation
🔌 API
🤖 MCP Server for Claude Desktop
💻 GitHub

If you're building a fintech or compliance tool, give it a spin and let me know what you think.

Build a KYC Agent with LangChain + Kyonis in 50 Lines of Python

Kyonis — Sun, 15 Mar 2026 23:12:25 +0000

Banks spend $270B/year on compliance. 95%+ of sanctions screening results are false positives. And most compliance APIs aren't designed for AI agents.

We built Kyonis to fix this — an agent-native KYC/AML API with sanctions screening in <500ms, full explainability, and a free tier.

Here's how to build a compliance agent in 50 lines of Python.

Install

pip install kyonis langchain-anthropic

Get your free API key (500 verifications/month): app.kyonis.com/register

The Agent

from langchain_anthropic import ChatAnthropic
from kyonis.langchain_tool import get_kyonis_tools
from langchain.agents import initialize_agent, AgentType

llm = ChatAnthropic(model="claude-sonnet-4-20250514")
tools = get_kyonis_tools(api_key="sk_sandbox_...")

agent = initialize_agent(tools, llm, agent=AgentType.OPENAI_FUNCTIONS)

result = agent.run(
"Screen these people against sanctions lists: "
"1. Jean Dupont (France) "
"2. Mohammed Al-Rashid (Iraq) "
"Give me the risk level for each."
)
print(result)

What Happens Under the Hood

LangChain agent receives the request
Agent calls kyonis_aml_screen for each person
Kyonis screens against OFAC, EU, UN, HMT sanctions (<500ms)
Each result includes a reasoning field explaining the decision
Agent compiles a human-readable summary

The Response

Every Kyonis response includes explainability:

{
"match": true,
"score": 0.92,
"reasoning": "Matched against OFAC SDN entry using fuzzy matching (Jaro-Winkler: 0.92). Country match confirmed.",
"sources": ["OFAC", "EU_SANCTIONS"],
"latency_ms": 417
}

Why Agent-Native Matters

Traditional compliance APIs return raw data. Kyonis returns reasoned decisions that AI agents can act on directly:

reasoning field → agent understands why
Structured scores → agent can make decisions
MCP server → Claude discovers Kyonis automatically

Try It

Free tier: 500 verifications/month
No credit card required
MCP server for Claude Desktop
SDK for Python & TypeScript

👉 kyonis.com
👉 GitHub
👉 API Docs

DEV Community: Kyonis

Our Open-Source RegTech Stack: From €5,400/mo to €800/mo (80% Cost Reduction)

The Cost Comparison

Our Stack

Backend: FastAPI + Go

Database: PostgreSQL Does Everything

Cache: Valkey (not Redis)

Message Queue: NATS (not Kafka)

AI: Hybrid Strategy (80% Local, 20% Cloud)

Monitoring: Grafana Stack

Infrastructure: Scaleway Kapsule

Data: Open Data First

The Result

Try It

Build a KYC Agent with LangChain + Kyonis in 50 Lines of Python

Install

The Agent

What Happens Under the Hood

The Response

Why Agent-Native Matters

Try It