One guards each LLM call in real time, the other evaluates the whole run's outcome, cost, and scope, so the two work at different layers.[1]
Lakera checks each prompt and response in real time for injection, jailbreaks, and data leakage. Prefactor watches the full run and puts a quality score on it, with drift detection and cost per agent. A call can pass every Lakera check while the run still misses its goal, so the two pair well.
| Decision factor | Lakera | Prefactor |
|---|---|---|
| Where it fits | Screening each prompt and response | Knowing the agent did its job across runs |
| Primary question | Is this prompt or response safe? | Did this agent produce the right outcome, at what cost? |
| Focus layer | A single LLM interaction | The agent that orchestrates many interactions |
| What you get | Real-time injection and leakage detection | A quality score per run, drift detection, and cost per agent |
| How it attaches | One API call per LLM interaction | Native SDK, core SDK, or OpenTelemetry ingest, no rebuild, no gateway in the request path |
| Use them together? | Guard prompts with Lakera | Evaluate the run with Prefactor |
Best for engineering teams that need fast, accurate screening of prompts and responses in customer-facing applications.
Best for teams running agents in production who need to know each one is doing its job, and prove it.
| Capability | Lakera | Prefactor |
|---|---|---|
| Securing LLM interactions | ||
| Prompt and response screening | ✓ | — |
| Real-time injection and jailbreak detection | ✓ | — |
| Adversarial red teaming | ✓ | — |
| Evaluating agents in production | ||
| Quality score per run | — | ✓ |
| Cost attributed per agent and version | — | ✓ |
| Drift detection against a baseline | — | ✓ |
| Hold or escalate a risky action before a user is affected | At the prompt level | At the agent level, on evaluation signals |
| Across your stack | ||
| Evaluates agents built on any framework | — | ✓ |
| One queryable record per agent | — | ✓ |
| Audit trail for a decision | — | ✓ |
We sell the layer this section describes. Read it with that in mind.
Lakera answers whether a single prompt or response is safe, in milliseconds. It does not answer whether the agent making those calls did its job across a whole run, at acceptable quality and cost.
Prefactor evaluates the run rather than the single call and tracks a quality score per agent across versions.
When behaviour shifts after a model update or a prompt edit, Prefactor flags the change.
When a score or a scope check crosses a threshold, Prefactor can hold or escalate the action before a user is affected.
Each decision keeps a record you can hand to a customer or an auditor, built from the traces you already emit through a native SDK or any OpenTelemetry source.
Reviewed against public product and documentation pages on March 19, 2026. If a vendor has changed a feature, product name, or positioning since then, send a correction and we will update it. Numbered source links in the page body point to the ordered sources below.
Book a demo and we will evaluate a live agent on a fleet like yours: quality per run, drift after a change, and cost per agent.
Prefactor helps teams observe, evaluate, and improve their AI agents in production, across every framework and provider.