Choosing an Inference Engine for Production LLM Serving

If You Remember Nothing Else

The inference engine choice determines 60 to 80% of your serving cost. A naive setup wastes most of the GPU you're paying for; a well-configured one keeps it saturated.

For nearly every self-hosted production deployment, the right default is vLLM. It's the only engine your team needs to know unless one of three specific things is true: the team is deeply integrated with the Hugging Face ecosystem (TGI may fit better), raw throughput is the single binding constraint (TensorRT-LLM may be worth its operational complexity), or constrained generation dominates the workload (SGLang).

If your team's proposal calls for something other than vLLM, ask why. The answer should be specific (a measured benchmark, a feature gap), not preference.

Recommendations by Situation

Worked Examples

Example 1: Mid-stage SaaS, 20M tokens per day, single model

A B2B SaaS company runs a single fine-tuned Llama 8B for customer-facing AI features. Volume: about 20M tokens per day, growing.

The right choice: vLLM on a single L40S GPU. Throughput at typical chat shape: about 3,000 tokens per second aggregate. Inference cost: about $400 per month.

What worked: vLLM's continuous batching kept GPU utilization above 80%, even with bursty traffic. The team didn't have to think about the inference engine after week 1; it just worked.

What they nearly got wrong: an early proposal called for TensorRT-LLM "for best performance." Tuning the engine for their specific model would have taken 2 weeks of engineering time. The 30% throughput gain wouldn't have justified the time at their scale.

What to remember: at typical SaaS scales, vLLM's simplicity is the win. TensorRT-LLM's tuning complexity isn't worth it until you're at very high volume.

Example 2: High-volume content moderation, 1B tokens per day

A social platform runs content moderation on every post. Volume: 1 billion tokens per day across multiple model sizes.

The right choice: TensorRT-LLM on H100s for the hot path (90% of traffic), vLLM on A100s for the experimental tail (10%, used for new policy categories).

What worked: TensorRT-LLM's tuned throughput meant 30% fewer GPUs needed for the hot path. At 1B tokens per day, that's about $50K per month in saved GPU cost. The 2-week tuning investment paid back in 3 weeks.

What they nearly got wrong: starting with TensorRT-LLM for the experimental tail too. The new categories changed weekly; the tuning overhead wouldn't have amortized. Keeping vLLM for the experimental side preserved iteration speed where it mattered.

What to remember: hybrid engines for hybrid workloads. TensorRT-LLM where stability and throughput matter, vLLM where iteration matters.

Example 3: Voice assistant with structured tool calls, 50M tokens per day

A voice assistant generates structured tool calls (JSON schema) for downstream actions. Volume: 50M tokens per day, with 95% of outputs being structured.

The right choice: SGLang. Constrained generation throughput is 3 to 5x vLLM on this specific workload because SGLang's grammar-based decoding doesn't fall back to slow per-token CPU sampling.

What worked: switching from vLLM to SGLang took one engineer two weeks. Throughput tripled, GPU costs fell 60%. Quality stayed identical because the model itself didn't change.

What they nearly got wrong: ignoring the workload shape. The team had been told vLLM was the "industry standard" and didn't audit whether it was the right choice for their specific output shape. SGLang exists for exactly this case.

What to remember: workload shape matters. Structured-output-dominated workloads have a specialized engine that beats general-purpose ones.

Anti-Patterns to Watch For

"TensorRT-LLM gives the best throughput, let's use it"

What it looks like: engine selection driven by published benchmark numbers without considering operational complexity.

Why it's wrong: TensorRT-LLM tuning is real engineering work. Every model and hardware combination needs its own tuned engine binary. Iteration is slower; debugging is harder. The 30 to 50% throughput gain only matters when you're at scale where it justifies the ops cost.

How to redirect: ask "what's our current throughput at peak load? What's the projected cost saving at 12-month volume? Does that exceed 2 weeks of engineering time?" If the answer is "we haven't measured," default to vLLM.

"We need to support multiple inference engines for flexibility"

What it looks like: architectural ambition to abstract over multiple engines.

Why it's wrong: every additional engine multiplies operational surface area. Different metrics, different bugs, different deployment paths. The flexibility rarely pays for itself.

How to redirect: pick one engine for the production hot path, use a second only for very specific workloads where it earns its keep. Don't run three engines at the same scale.

"Let's use the engine that supports the most features"

What it looks like: engine selection driven by feature checklists.

Why it's wrong: feature lists are easy to inflate. What matters is whether the engine handles your specific workload well, not whether it has features you don't use.

How to redirect: list the 3 features the team actually needs. Verify that the simplest engine supports them. Add complexity only if it doesn't.

"We'll switch engines if the current one stops working"

What it looks like: no exit strategy for the engine choice.

Why it's wrong: switching engines mid-flight is painful. Different APIs, different deployment paths, different observability hooks. Plan for it being possible but not easy.

How to redirect: keep the inference layer behind an abstraction in your code. Engine-specific configuration in one place. Most code shouldn't care which engine is running underneath.

"Continuous batching is too risky for production"

What it looks like: defaulting to static batching or one-request-at-a-time inference.

Why it's wrong: continuous batching is the production default in 2026. Static batching wastes 80%+ of GPU compute on typical chat shapes.

How to redirect: continuous batching has been production-proven for years. If the team is uncomfortable, that's a sign of unfamiliarity, not actual risk. Push them to validate it on a sample workload.

When NOT to Self-Host Inference at All

Sometimes the right answer is to use a hosted inference provider:

• Volume is too low to justify the engineering investment in self-hosting (under 10M tokens per day).
• The team has no GPU operations capacity and no budget to build it.
• The model changes weekly and operational stability isn't yet the goal.
• Compliance allows hosted APIs and there's no specific reason to bring it in-house.

In all these cases, OpenAI, Anthropic, AWS Bedrock, Google Vertex AI, or RunPod/Modal/Together are appropriate. The "we should self-host" instinct is wrong below the volume threshold; the cost of operations exceeds the API savings.

What to Ask Your Engineering Team

1. Which inference engine are you proposing, and why this one specifically? "It's popular" is not an answer.
2. What's the projected throughput at 12-month expected load? In tokens per second per GPU, not just "fast."
3. What's the engineering investment to set up and tune? Hours, not "quick."
4. What's our fallback if this engine has issues? A specific second engine, not "we'd figure it out."
5. Have we benchmarked on our actual workload, not just published numbers? Real traffic shape matters.
6. What's the LoRA hot-swapping story? Critical for multi-tenant or multi-variant serving.
7. What's the deployment path? Containers, GPU operator, autoscaling, monitoring.

Cost & Timeline Quick Reference

Realistic ranges for production inference deployment:

The Bottom Line

vLLM is the right default for self-hosted production inference. TGI when HF ecosystem matters. TensorRT-LLM when raw throughput at scale justifies the operational complexity. SGLang when structured output dominates the workload.

Don't let engineering chase the latest engine for engineering's sake. Pick one, ship it, optimize it, and only switch when there's a measured reason. The teams that operate inference well aren't the ones with the most exotic engines; they're the ones who picked the right engine for their workload and operated it consistently.