Build vs buy · Honest comparison
When PrimeSwarm is the right choice
We are not going to tell you PrimeSwarm is always the answer. It isn't. Here is when each approach makes sense — including when ours doesn't.
Do nothing
Rely on the LLM provider's built-in safety filters
When it makes sense
Your risk tolerance is high, your use case is low-stakes, and you trust the model provider to handle safety.
Pros
- Zero cost
- Zero integration work
- Works immediately
Cons
- No audit trail — you cannot reconstruct what happened
- No HITL escalation — the model decides alone
- No provenance — outputs are not traceable to sources
- Provider can change behavior without notice
- Fails open — if the filter breaks, the output goes through
Build your own
Python guardrails in your application layer
When it makes sense
You have a dedicated ML safety team, you need full control, and you can maintain the guardrails as models and attacks evolve.
Pros
- Full control
- No vendor dependency
- Can be exactly tailored to your use case
Cons
- 6-12 months to build a production-grade gate
- Ongoing maintenance — new attack patterns require constant updates
- No deterministic receipts — your guardrails are code, not proofs
- No independent verification — you are marking your own homework
- Hard to prove compliance to regulators — 'we built it ourselves' is not an audit answer
Open-source framework
Guardrails AI, NeMo Guardrails, or similar
When it makes sense
You want a starting point, you have engineers who can extend it, and your compliance requirements are moderate.
Pros
- Free to start
- Community-maintained
- Good for prototyping
Cons
- Not deterministic — guardrails are probabilistic, not mathematical
- No built-in HITL escalation
- No receipt store — you build your own audit trail
- No independent verification — the framework tests itself
- No governance DSL — policies are Python code, not verifiable scripts
- Scaling to production is your problem
PrimeSwarm
OursGovernance gate + receipts + HITL + DGV verification
When it makes sense
You are in a regulated industry, you need deterministic proof of governance, and you want it deployed in weeks not quarters.
Pros
- Deterministic receipts — every decision is a tamper-evident SHA-256 receipt
- HITL escalation built in — high-stakes decisions route to a human
- Live enforcement gate — authority checked at proposal time (T₀) and re-verified at execution time (T₁), with signed Ed25519 receipts
- Fail-closed by default — if the revocation store is unreachable, the gate denies instead of assuming
- Signed revocation gossip — revocations propagate to disconnected peer nodes (~60 ms measured)
- DGV verification — 89 test cards independently verify the gate works
- ONLY Lang DSL — policies are scripts, not code. They can be inspected, tested, and replayed
- Deployed in weeks — Helm chart, your VPC, your keys
- Model-agnostic — works with any LLM provider
- Open-source verifier — you can inspect and run the test cards without buying anything
Cons
- Commercial — Guard starts at $14,800/year
- Not open-source (the runtime) — you depend on Only Institute for updates
- New vendor — limited track record vs established players
Feature comparison
| Feature | Do nothing | Build your own | Open-source framework | PrimeSwarm |
|---|---|---|---|---|
| Deterministic receipts (SHA-256) | ||||
| HITL escalation | DIY | DIY | ||
| Independent verification (DGV test cards) | ||||
| Governance DSL (ONLY Lang) | ||||
| Authority re-checked at execution time (T₀→T₁) | DIY | |||
| Fail-closed when the authority store is unreachable | DIY | |||
| Signed, versioned policies with rollback | DIY | |||
| Signed agent-to-agent envelopes (replay-protected) | DIY | |||
| Revocation gossip to disconnected nodes | ||||
| Verified human approvals before execution | DIY | DIY | ||
| Audit trail export to SIEM | DIY | DIY | ||
| Provenance enforcement | DIY | |||
| Model-agnostic | ||||
| Deploy in weeks | Maybe | |||
| Open-source verifier | ||||
| Regulator-ready evidence | ||||
| Ongoing maintenance included | Community | |||
| Cost | $0 | 6-12mo eng | $0 | from $14.8k/yr |
The honest bottom line
If you are building a chatbot for internal use and the worst case is a wrong answer — do nothing. The model provider's filters are probably fine. You do not need us.
If you have a dedicated ML safety team and 6-12 months — build your own. You will get exactly what you need and you will own it. Just remember: you are also signing up to maintain it forever, and "we built it ourselves" is not a compelling answer to a regulator.
If you are prototyping and want a starting point — use an open-source framework. Guardrails AI and NeMo Guardrails are good starting points. They are not production governance, but they will help you understand the problem.
If you are in a regulated industry and need to prove governance to a regulator, an auditor, or a malpractice insurer — that is when PrimeSwarm is the right choice. The receipts are the product. The DGV test cards are the proof. The ONLY Lang scripts are the policy. If you need deterministic, verifiable, auditable governance deployed in weeks, this is it.