Why “Just Use Open Source” Is the Most Expensive Sentence in AI
“It’s just a few APIs. We’ll use open source.”
You’ve probably heard this line said a few times if you lead engineering or tech at a bank. You have an incredible team creating agentic AI prototypes with LangGraph, LangChain or another open source framework.
Though the demo looks promising and the cost seems low, the prototype has evolved into a growing list of governance, audit, and model-risk questions, brittle services with low tolerance for stress, and a new obligation to maintain a system your team hadn’t planned on owning at scale.
Because of this, open source can become an expensive line item in your AI strategy.
In this piece, I explore how you can still get the upside without the compromise.
If you’re wrestling with whether to “just use open source” for your next agentic AI initiative, you’re not alone.
To explore how a multi‑agent AI platform designed specifically for your workflows can complement your existing open source work, submit the form:
Open Source Frameworks are Powerful but Aren’t a Platform
There are plenty of open source tools that can do amazing things for your team. LangChain and LanGraph, for example, can help you manage tools, context, and memory for LLMs, quickly iterate on new use cases for AI agents, and they can help orchestrate multi-agent workflows.
And though they can speed up the builds of prototypes and enable your teams for experimentation, they lack the critical foundation that’s required for regulated workloads in financial services. Open Source gets expensive because it doesn’t allow you to have a layer of built-in governance, end-to-end observability and lineage, and more
The Hidden Cost Layers of DIY Agentic AI
1. Platform Plumbing: Everything Around the Graph
Open source agent frameworks provide the bones of agent orchestration, but they miss the full system that's needed for full deployment.
In environments that require sign-off from risk, compliance, and are subject to regulatory scrutiny (think banking, markets, healthcare, insurance), building the appropriate environment management, state management, debugging, and human-in-the-loop controls becomes a massive undertaking.
2. Observability, Lineage, and Auditability
When it comes to these highly regulated environments, knowing something worked isn’t enough. In this instance, you also need to know why and how, ensuring you have complete traceability of each agent call, model response, 100% of data lineage, and consistent logs that satisfy all audits, internal and external. Though vendors now offer observability and tracing, trying to stitch these into a DIY stack adds more integration points, contracts, and headaches.
3. Security, Compliance, and Model Risk
In a consumer app, an agent hallucinating is an annoyance, but in a bank, the consequences are immense. Open source rarely ships with regulator-grade controls, which means the responsibility of having the correct access controls, data protection (PII handling, encryption, data residency, and cross‑border restrictions), and regulatory frameworks (emerging EU AI Act requirements, etc.)
4. Operational Burden and Technical Debt
Open source frameworks and LLM APIs iterate and change quickly. Because of this, what looked like clean architecture one month later can turn out to be a patchwork of compatibility shims just a few months later.
Some hidden costs that can arise include:
24/7 on‑call for agent failures in production
Regression testing across multiple models, tools, and agents when dependencies change
Version drift as teams fork frameworks or pin to older versions for stability
Key person risk, where only a handful of engineers understand how the system actually works
A lot of AI costs accrue after deployment, such as maintenance, refactoring, and unplanned re‑platforming. In a lean engineering organization, that opportunity cost is enormous.
5. The Cost of Delay
Finally, there is the hardest cost to quantify: time‑to‑value.
While your team is working on items like hardening orchestration, designing an internal observability solution, and negotiating log retention with compliance, your competitors are automating complex operations workflows and scaling personalised AI-driven client coverage.
Why This Matters More in Financial Services
So while open source might be the best option for a consumer product, for products that touch highly regulated environments like financial services, the risk is much greater. One single bad decision can create material losses and regulatory exposure, and when workflows touch money and markets, the risk is even greater. Governance, monitoring and explainability aren't just “nice to haves”; they are mandated requirements.
A Nuanced View: When DIY Makes Sense and When It Doesn’t
My goal here is not to dismiss open source, as some of the best innovation is happening there. But we do need to ask ourselves, where should we be using open source, and where do we need an enterprise platform?
DIY with Open Source Makes Sense When:
Agentic AI is core to your product and differentiation.
You have a dedicated internal platform team chartered to build and run AI infrastructure.
Your regulatory exposure is low, or your use cases are low‑risk and internal‑only.
You’re comfortable owning security, governance, and observability as long‑term commitments.
A Platform Approach Makes More Sense When:
You operate in a regulated domain like banking, insurance, healthcare, or capital markets.
You have aggressive timelines for time‑to‑value and limited capacity to build from scratch.
You need end‑to‑end governance, lineage, and auditability from day one.
You want internal teams to focus on business logic and use cases, not re‑implementing plumbing.
In practice, most large financial institutions end up with a hybrid approach: open source for experiments and edge cases, an enterprise platform as the production control plane.
How Artian AI Complements, Not Replaces, Open Source
At Artian, we see many teams already experimenting with LangGraph, LangChain, and other frameworks. That’s healthy and it signals a strong internal engineering culture.
Our view is this:
Keep experimenting with open source at the edge.
Let a financial‑grade multi‑agent platform handle production workloads where governance, scale, and resilience truly matter.
Artian is designed as that platform layer:
Governance‑first architecture: policy‑as‑code for which agents can do what, when, and on whose behalf.
Deep observability and lineage: full traces across agents, tools, and systems, with exportable evidence for internal audit and regulators.
Financial‑services‑ready integrations: connectors into core banking, trading, risk, and data platforms, so agents operate where your business already lives.
Operational reliability: SLAs, support, and a roadmap aligned with the realities of large, regulated enterprises.
Your engineers still get to choose the best‑fit frameworks and models. Artian becomes the orchestrator, guardrail, and audit trail that lets those choices scale safely.
