COMPARE
The discovery of LLMs.
The trust of rules engines.
Your SMEs already understand 80% of the matching work in your data backlog. Coherany lets them run it themselves. An LLM coach helps them define each pattern; the deterministic engine runs every decision in production, audit-ready by default.
Every approach has trade-offs. Here's where each one breaks.
FULL COMPARISON
How every approach stacks up.
A full feature matrix across the five most common alternatives. Scroll right on mobile.
| Feature | Coherany | LLM APIs | ML Platforms | Custom Build | Rules Engines |
|---|---|---|---|---|---|
| Auto pattern discovery | Varies | Varies | |||
| Human approval workflow | Varies | ||||
| Instant matching | Varies | Varies | |||
| Full audit trail | Varies | Varies | Varies | ||
| No ML expertise needed | |||||
| Same input, same answer, every time | |||||
| Cloud + on-prem deployment options | Varies | ||||
| Predictable pricing | Varies | N/A | |||
| Explainability | Varies | Varies |
vs. RAG
RAG generates. PAR matches.
Zero LLM calls in the production path. Every decision has a named approver and a complete audit trail. Defensible to regulators by design.
PAR shares 60% of its technical stack with RAG. The same embeddings, vector indexes, and retrieval primitives. The difference is what happens after retrieval: RAG feeds context to an LLM that generates new text. PAR matches against patterns your compliance team already approved.
RAG Pipeline
- LLM generates new text on every call
- Hallucination risk — answers not grounded in retrieved content
- Non-deterministic — same query, different answer each time
- No native audit trail — LLM reasoning is not reproducible
- Per-token cost scaling — $62K/month at 10M records
- No human gate before retrieval reaches production
PAR Pipeline
- Matches human-approved insights — no generation step
- Zero hallucination — returns the approved insight directly
- Deterministic — same input, same result, every time
- Complete audit trail — every match traceable to a named approver
- Flat-rate matching — $6K/month at 10M records
- Human-in-the-loop — nothing reaches production without sign-off
| Dimension | RAG | PAR |
|---|---|---|
| Post-retrieval | LLM generates | Pattern match |
| Determinism | No | Yes |
| Hallucination risk | Present | Zero |
| Audit trail | Typically none | Every decision |
| Human oversight | None before output | Required |
| Throughput | 50–500 req/sec | 400K decisions/sec |
| Cost at 10M records | ~$62,000/mo | $6,000/mo |
"RAG generates. PAR matches."
Same retrieval foundation. Different guarantee.
vs. LLM APIs
Your LLM bill scales linearly. Ours barely moves.
LLM APIs charge per token, per call. At scale, that becomes tens of thousands per month. And the outputs can still hallucinate, vary, and break audits.
Coherany puts the LLM where it belongs: helping your SMEs define each pattern and explaining results. Production matching runs through the deterministic engine, with zero LLM calls in the decision path. Every decision has a named approver and a complete audit trail. Defensible to regulators by design.
| Monthly Volume | LLM API Cost | Coherany | Savings |
|---|---|---|---|
| 100K records/mo | ~$4,800 | $2,000 (Growth) | 58% |
| 500K records/mo | ~$8,000 | $2,000 (Growth) | 75% |
| 1M records/mo | ~$12,500 | $2,000 (Growth) | 84% |
| 5M records/mo | ~$31,000 | $6,000 (Scale) | 81% |
| 10M records/mo | ~$62,000 | $6,000 (Scale) | 90% |
less at 10M records/month
$6,000 vs $62,000. The gap widens with every record.
Beyond cost
- No hallucination risk. Deterministic outputs.
- Same input, same output, every time
- Complete audit trail for every decision
- No prompt engineering required
- 12ms latency, not 1-3 second API round-trips
- No rate limits at scale
vs. ML Platforms
Days to production. Not months.
Your SMEs already understand 80% of the matching work. Coherany lets them run it themselves: an LLM coach helps them define each pattern, a deterministic engine runs every decision in production, audit-ready by default. Your data scientists sponsor and review. Your SMEs operate. The 20% that actually needs a data scientist stays with them.
Labeled training data required
You need thousands of manually labeled examples before training can begin. That alone takes months.
No labeled data needed
Coherany discovers patterns in raw text. No annotation pipeline, no labeling team, no cold-start problem.
Data science team required for everything
ML platforms assume every matching job needs a data scientist. The repetitive 80% piles up in their backlog while higher-value work waits.
DS sponsors and reviews. SMEs operate.
Your data scientists sponsor and review. Your SMEs define patterns directly with an LLM coach and operate the 80%. The 20% that actually needs a data scientist stays with them.
Ongoing model maintenance
Models drift. Someone needs to monitor, retrain, and redeploy on a recurring basis indefinitely.
No model maintenance
New patterns are discovered and approved continuously. No retraining. No redeployment. Your team focuses on decisions.
Black-box predictions
Confidence scores don’t explain why a model classified something. Audit teams and regulators want lineage.
Built-in human oversight
Every match traces back to an approved pattern. Humans approve patterns before they fire. Full explainability built in.
Periodic retraining cycles
When your data changes, you retrain. That’s a weeks-long cycle every time your business shifts.
No retraining required
New patterns are discovered continuously. Approve or reject — no retraining, no model deployment, no downtime.
vs. Custom Build
85% of AI Projects Never Reach Production
Your SMEs already understand 80% of the matching work in your data backlog. Coherany lets them run it themselves, instead of waiting in a data science queue. Your data scientists sponsor and review. The 20% that actually needs a data scientist stays with them.
Building in-house feels like maximum control. In practice, it's maximum risk: budget overruns, timeline slippage, and key-person dependency that never goes away.
of AI and machine learning projects fail to deliver.
— Gartner Research
Custom Build Reality
- $150K–$500K in engineering time to reach production
- 6–12 months before first real classification runs
- Dedicated engineer(s) for ongoing maintenance
- Build your own pattern discovery system
- Build your own search and matching infrastructure
- Build your own audit trail and approval workflow
- Key-person dependency — leave risk always present
Coherany
- $10K pilot, 6 weeks to real results
- Credited to annual. The pilot pays for itself.
- Platform updates included — no maintenance burden
- Automatic pattern discovery, built-in and production-hardened
- 400K decisions/sec production matching, already integrated
- Full decision lineage and approval workflow included
- Documented API, 9,600+ tests, 242 endpoints. No key-person dependency.
Everything is already built. 9,600+ tests. 242 API endpoints. 143 Studio nodes. Ready to deploy in days.
vs. Rules Engines
Discover patterns you didn't know existed
Rules engines are reliable for what you already know. The problem is what you don't know. Coherany finds those patterns. Rules engines can't.
Both run deterministically in production. The difference is how the patterns get there. With a rules engine, every rule is hand-coded by an analyst or engineer who already knew the rule. Coherany lets your SMEs define the patterns themselves with an LLM coach, sign each one off, then runs every decision through the deterministic engine. Every decision has a named approver and a complete audit trail. Defensible to regulators by design.
The Fundamental Limitation
- —Every rule must be written by a human expert
- —Only catches patterns someone already thought of
- —Gaps widen as data volume and variety grow
- —Maintenance burden grows with every new rule
- —Pattern changes require engineering involvement
Coherany Discovers Automatically
- AI discovers patterns automatically from raw text
- Surfaces patterns that no one thought to look for
- Coverage scales with data, not with headcount
- Human experts approve — platform handles discovery
- New patterns emerge continuously, no code required
- Deterministic matching once patterns are approved
"Rules engines match what you expect. Coherany discovers what you don't, then runs it audit-ready."
Coherany is not for everyone.
We'd rather tell you now than after a pilot.
- Volume under 100K records/month. The economics don't justify the platform fee
- Generative AI use cases (content creation, summarization). Coherany matches; it doesn't generate
- No DevOps capability for on-prem. Cloud deployment is fully managed, but on-prem requires a Kubernetes-capable team.
- Non-relational database requirement. The platform uses a relational database architecture
See it on your data.
Run a proof-of-concept. See what surfaces and what it saves.
$10K pilot. 6 weeks. Credited to annual.