The first time I explained what ZeroDrift does to a senior infrastructure engineer at a growing healthcare software company, he nodded along and then asked: "So it's just a middleware wrapper. How long does that add?" That framing is technically accurate in a narrow sense, but it skips past the parts that make this genuinely hard to get right. Adding latency is easy. Adding latency budgets that survive p99 production spikes is the actual challenge.
This post is about what real-time LLM output interception looks like when you are trying to be correct, fast, and non-lossy at the same time. I will focus on the architectural decisions rather than ZeroDrift's specific implementation, because the tradeoffs here are instructive regardless of which tooling you use.
The Basic Intercept Path
The simplest possible intercept architecture is synchronous. Your application sends a completion request to an LLM provider. The provider returns a response. Before your application delivers that response to the user, it passes the full text to an intercept service. The intercept service evaluates the response against the active policy ruleset and returns a decision: pass, block, or rewrite. Your application acts on the decision.
In this configuration, the intercept service is on the critical path. Every millisecond it takes to evaluate a response is added directly to the user's time-to-first-byte. For most enterprise LLM workflows, total response time already sits in the one-to-three second range for medium-length completions. An intercept service that adds 200ms at p50 is noticeable but probably acceptable. An intercept service that adds 200ms at p50 but 2,000ms at p99 is not.
The design challenge is building evaluation logic that is accurate enough to matter and fast enough to be invisible. These two requirements pull in opposite directions. More thorough evaluation takes more time. Faster evaluation is usually shallower.
Streaming Responses Create a Different Problem
The synchronous model assumes you wait for the full completion before checking it. That is fine for short responses, but modern LLM interfaces almost universally support server-sent event streaming, where tokens arrive incrementally as they are generated. Your application can forward those tokens to the user as they arrive, which produces the typing-cursor effect that users now expect from AI interfaces.
Streaming creates a tension with full-document policy evaluation. You cannot evaluate a sentence for a violation if you have not seen the sentence yet. There are three approaches teams take to resolve this, and each has real costs.
The first is buffer-and-release: buffer the full streaming response, evaluate it, then stream the result to the user in a way that simulates the original streaming behavior. The user experience is identical to non-streaming from the user's perspective, but the latency profile looks more like synchronous. You are adding roughly the full generation time before the user sees the first token.
The second is progressive evaluation: evaluate the response at sentence boundaries as they arrive in the buffer, and release tokens up to the most recently evaluated sentence. This preserves most of the streaming latency benefit while giving you near-real-time evaluation. The tradeoff is that violations appearing late in a long response may have already caused earlier clean tokens to reach the user. If a rewrite or block is triggered midway through, the user experience becomes complex to manage gracefully.
The third is speculative release: release tokens to the user as they arrive, evaluate asynchronously, and apply a correction event if a violation is detected. This is the lowest-latency option for clean responses, but it means a violation can briefly appear to the user before being replaced or removed. For most compliance use cases, this is disqualifying: the point of an intercept layer is to guarantee that violating content does not reach users, not to catch it after the fact.
For ZeroDrift, we settled on a variant of buffer-and-release with aggressive evaluation parallelism. We start evaluating sentences as soon as we have them and aim to have the full evaluation complete within 20-25ms of receiving the last token, targeting a total add-on latency under 30ms p99 for responses up to roughly 600 tokens.
Policy Evaluation at Speed
Getting policy evaluation fast enough to stay within a 30ms budget requires careful choices about what evaluation looks like at the engine level.
The naive implementation of a compliance check is: take the full response, pass it to a second LLM with a policy prompt, get a judgment. This approach produces excellent detection quality but terrible latency. A second LLM call, even to a fast small model, adds several hundred milliseconds at p50. That blows the budget by an order of magnitude.
Practical fast evaluation relies on a combination of deterministic rule matching and lightweight classifier passes rather than generative model calls. Deterministic rules handle categories that are structurally identifiable: presence of PII patterns, specific phrases on a disallowed list, numerical values in defined formats that the policy prohibits. These checks run in microseconds. Classifier-based checks handle categories that require semantic understanding: does this response constitute financial advice, does this paragraph make an unsupported factual claim, does this sentence contradict the product's safety documentation? These checks are heavier, but pre-trained classifiers operating on a fixed schema run in 8-15ms on current serving hardware, not hundreds of milliseconds.
The key is routing. Not every response needs every evaluation path. A response that contains no numbers does not need to run through the financial figures check. A response that passes all deterministic checks on categories with high precision can skip the heavier semantic passes. Routing evaluation work to only the paths that matter for each specific response keeps median latency very low while maintaining worst-case coverage.
Rewrite vs. Block: The Latency Asymmetry
Blocking a violating response is computationally cheap once you have the violation signal. You discard the response, log the event, and return a canned fallback. The total overhead from the intercept decision to the user seeing the fallback is minimal beyond the evaluation cost itself.
Rewriting is fundamentally different. A rewrite requires generating a new response that is compliant, useful, and coherent with the conversational context. If you use a generative model for rewriting, you have added another LLM call to the critical path. Even a fast small model adds latency that is visible to users on complex rewrites.
The approach that keeps rewrite latency manageable is constraining what rewrites actually do. A rewrite engine that can synthesize arbitrary new content is powerful but slow. A rewrite engine that applies template-based transformations to specific violation categories is much faster. If the policy says "do not quote specific balance figures," the rewrite template can replace any balance quote pattern with a standardized redirection. That transformation can happen in under 5ms. A generative rewrite of the same sentence takes 300-800ms.
This is the genuine tradeoff teams need to think through: template-based rewrites are fast and consistent, but they handle a narrower set of violation patterns and require policy teams to anticipate the rewrite need in advance. Generative rewrites handle novel violation patterns but carry meaningful latency cost. Most production systems we have worked with benefit from a tiered approach: template rewrites for high-frequency violation categories, with generative rewrite reserved for categories where template coverage is insufficient and the violation is high-severity enough to justify the latency.
What Gets Harder at Scale
The engineering challenges that look manageable at low throughput become interesting at production volumes. At 500,000 requests per month, which is ZeroDrift's Growth tier, you need to be confident your evaluation engine does not degrade under concurrent load. Latency budgets are per-request, not average across the fleet. A p99 that looks fine at 10 concurrent requests may fail at 100.
The audit logging requirement adds its own complexity. Every intercept event needs to be logged with sufficient fidelity that a compliance review can reconstruct exactly what the policy check saw, what decision was made, and what the user received. That means logging request metadata, the evaluated response text, the triggered rules, the action taken, and the final output, atomically, without that logging cost appearing in the user-facing latency. Getting the write path out of the critical path while maintaining write durability under failure conditions is a systems problem that does not get simpler as volume grows.
These are solvable problems. But they are different problems from "add a wrapper function to your LLM call." The teams that build intercept layers that survive their first year in production are the ones who treated the engineering as a real infrastructure project from the start.