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Your LLM Gateway Routes. But Does It Verify?

#llm #ai #reliability #opensource

Your LLM Gateway Routes. But Does It Verify?

LiteLLM, Portkey, TensorZero — they're all gateways. Here's why "routing" isn't enough for production AI.

The $40,000 Question Nobody's Asking

Your LLM gateway routes requests to 100+ providers. It handles retries, fallbacks, load balancing, and cost tracking. It's fast, reliable, and your engineering team loves it.

But here's what it doesn't do: it doesn't check if the LLM's output is actually correct.

Think about that. You're routing millions of dollars in LLM API calls through a system that treats every response as valid — whether the model hallucinated a fake legal citation, returned JSON in the wrong schema, silently degraded in quality over the past week, or started answering in the wrong language.

The gateway switches providers. But it doesn't verify the output.

Failover switches. Correctover verifies.

The Five LLM Gateways (And What They Don't Do)

Let me be fair — the major players in this space are excellent tools. I use them. But they solve a different problem than what production AI teams actually need.

LiteLLM (51K+ ★)

What it does: Unified API for 100+ LLM providers. Great routing, fallback logic, cost tracking.

What it doesn't do: Semantic output validation. LiteLLM will happily retry a "failed" request — but if the response looks correct syntactically while being semantically wrong, LiteLLM passes it through.

Portkey (12K+ ★)

What it does: Enterprise AI Gateway with observability, guardrails, and governance. Strong on security and compliance.

What it doesn't do: Real-time semantic equivalence checking. Portkey's guardrails focus on input/output safety (PII, toxicity) rather than verifying that the LLM's answer actually matches what was asked.

TensorZero (11K+ ★)

What it does: Full LLMOps platform — gateway, observability, evaluation, optimization, A/B testing. Very impressive.

What it doesn't do: Inline, real-time output verification. TensorZero's evaluation is batch/offline. It tells you after the fact that quality degraded. Correctover catches it before the user ever sees the bad output.

Kong AI Gateway / Maxim Bifrost

What they do: Enterprise-grade API gateway with AI-native features. Fast, scalable, production-proven.

What they don't do: Any form of semantic validation. These are infrastructure-level tools — they optimize for latency and throughput, not output correctness.

What They All Have in Common

None of these tools answer the question: "Is this LLM output actually correct for this specific request?"

They route. They observe. They configure. They optimize.

They don't verify.

What "Verified Failover" Actually Means

Here's a scenario every production AI team has experienced:

  1. Your app sends a request to GPT-4o via your gateway
  2. GPT-4o returns a response — HTTP 200, valid JSON, looks fine
  3. But the answer is wrong. The LLM hallucinated a statistic. It answered in French instead of English. It returned a schema that's technically valid JSON but doesn't match your contract.
  4. Your gateway marks this as "success" and passes it to the user.

This is a silent failure — and it's 10x more dangerous than a 500 error.

With Correctover, the flow looks different:

  1. Your app sends a request
  2. The LLM returns a response
  3. Correctover validates the output across 6 dimensions:
    • Schema contract compliance
    • Semantic equivalence to expected patterns
    • Language/locale correctness
    • Factual consistency
    • Structural integrity
    • Quality threshold checks
  4. If validation fails → automatic self-healing (retry with corrected prompt, fallback provider, or graceful degradation)
  5. If all providers fail → circuit breaker with detailed diagnostics

The key insight: a failed validation is treated the same as a failed API call. Both trigger the self-healing engine. Both get retried. Both get logged.

This is not "failover." This is verified failover.

The Drift Detection Problem

Here's another silent killer that no gateway addresses:

Your LLM provider updates their model. Suddenly, output quality drops 15%. Nobody notices for weeks because there's no error — the responses just... aren't as good. The JSON is still valid. The HTTP status is still 200. But your user satisfaction is silently cratering.

Correctover's drift detection engine continuously monitors output quality against baseline metrics. When it detects statistical degradation — even without hard failures — it alerts you and can automatically switch to a backup provider or model version.

No other gateway does this. Because no other gateway is looking at what the LLM actually said.

The Numbers

From our benchmark data (430 fault types, 20K test runs):

  • 43% of silent failures are caught and self-healed within 1 second
  • Semantic validation catches 6x more production issues than HTTP status checks alone
  • Drift detection identifies quality degradation an average of 4.2 days before manual discovery

These aren't theoretical. This is what happens when you verify outputs instead of just routing requests.

The Stack You Actually Need

This isn't "replace your gateway." Your LiteLLM/Portkey/TensorZero is doing its job well.

The missing layer is reliability engineering on top of your gateway:

Your App → Correctover SDK → Your Gateway (LiteLLM/Portkey/etc.) → LLM Providers
                ↓
         [6-dimension validation]
         [Self-healing engine]
         [Drift detection]
         [Audit trail]
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Correctover sits between your app and your gateway. It validates every response, heals every failure, and monitors every trend. Your gateway handles routing. Correctover handles correctness.

Try It 

pip install correctover
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from correctover import CorrectoverClient

client = CorrectoverClient(
    providers=["openai", "anthropic", "bedrock"],
    validation={"schema": "strict", "language": "en", "quality": 0.85}
)

# Every response is validated before reaching your app
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What's the GDP of France?"}]
)
# If the response is wrong, Correctover already retried with a different provider
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Or use the local gateway (zero-code, BYOK):

pip install local-gateway
local-gateway start --providers openai,anthropic --validate --self-heal
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Links:

Correctover is open-source (Apache 2.0). BYOK — bring your own keys. No vendor lock-in. No telemetry. No excuses.

Because failover switches. Correctover verifies.™

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