Top LLMOps Tools: Deploying & Managing LLMs in Production

Introduction

You need to pick an LLMOps tool for production. The wrong choice means wasted GPU spend, poor latency, or a rewrite six months in. This comparison covers the five tools you will actually use: vLLM, TGI (Text Generation Inference), Ollama, BentoML, and Ray Serve. Each gets evaluated on four axes: deployment architecture (stateless vs stateful), scalability (vertical vs horizontal), GPU utilization (continuous batching support), and observability (metrics, traces, logs). The goal is to give you a decision matrix you can take to your team meeting tomorrow. No vendor fluff, no marketing claims. Just real Helm values, real resource limits, and real trade-offs.

Side-by-Side Comparison Table

Feature	vLLM	TGI (Hugging Face)	Ollama	BentoML	Ray Serve
Core purpose	High-throughput LLM serving	Production LLM serving	Local model runner	Model serving framework	Distributed serving
Continuous batching	Yes (PagedAttention)	Yes (v2)	No (sequential)	Yes (via vLLM backend)	Yes (via Ray)
GPU memory overhead	Low (~1.2 GB)	Medium (~2 GB)	Low (~800 MB)	Medium (~1.5 GB)	High (~3 GB)
Kubernetes native	Helm chart, HPA	Helm chart, KEDA	Manual (no official chart)	Helm chart, KEDA	Ray operator, KEDA
Observability	Prometheus metrics, OpenTelemetry	Prometheus metrics, request logs	Basic logs only	Prometheus, Jaeger	Ray dashboard, Prometheus
Model formats	Hugging Face, AWQ, GPTQ	Hugging Face, AWQ, GPTQ	GGUF, GGML	Hugging Face, ONNX	Hugging Face, PyTorch
License	Apache 2.0	Apache 2.0	MIT	Apache 2.0	Apache 2.0
Typical TPS (Llama 3 8B, A100)	~120	~100	~40	~90	~80

Tool 1 Strengths and Trade-offs: vLLM

vLLM is the current leader for latency-sensitive, high-throughput LLM serving. Its PagedAttention algorithm reduces GPU memory fragmentation by up to 60% compared to naive KV-cache allocation. This directly translates to higher throughput per GPU.

Strengths:

• Best-in-class continuous batching. You can serve Llama 3 70B on a single A100 with 80 GB and still get ~30 tokens per second.
• Native Kubernetes support via a Helm chart. The chart exposes Prometheus metrics out of the box, which you can feed into Grafana for dashboards. For a deeper look at setting up observability, see LLM Observability on Kubernetes: A Practical Guide.
• OpenTelemetry integration for traces. You can trace individual requests through the serving stack.

Trade-offs:

• No built-in model registry or experiment tracking. You need MLflow or a separate registry to manage model versions.
• The Helm chart is opinionated. Customizing the autoscaling behavior requires overriding the default HPA with KEDA, which adds complexity.

Deployment snippet (Helm values):

# values.yaml for vLLM v0.6.0
image:
  repository: vllm/vllm-openai
  tag: v0.6.0
model: meta-llama/Meta-Llama-3-8B-Instruct
serving:
  maxModelLen: 4096
  gpuMemoryUtilization: 0.90
resources:
  limits:
    nvidia.com/gpu: 1
autoscaling:
  enabled: true
  minReplicas: 2
  maxReplicas: 10
  targetCPUUtilizationPercentage: 80

Tool 2 Strengths and Trade-offs: TGI (Text Generation Inference)

TGI is Hugging Face’s answer to vLLM. It is tightly integrated with the Hugging Face ecosystem, which makes it the easiest choice if your models are already on the Hub.

Strengths:

• Seamless integration with Hugging Face Hub. You can deploy any model from the Hub with a single environment variable.
• Built-in watermarking and safety checks. TGI includes a content moderation filter that runs before the model output is returned.
• KEDA integration for event-driven scaling. You can scale based on queue depth rather than CPU.

Trade-offs:

• Higher GPU memory overhead than vLLM. The safety filters and watermarking consume about 2 GB of VRAM, which matters on smaller GPUs.
• Slower cold start. Loading a model from the Hub on the first request can take 30-60 seconds, compared to vLLM’s ~10 seconds.

Deployment snippet (Helm values):

# values.yaml for TGI v2.3.0
image:
  repository: ghcr.io/huggingface/text-generation-inference
  tag: 2.3.0
modelId: meta-llama/Meta-Llama-3-8B-Instruct
resources:
  limits:
    nvidia.com/gpu: 1
env:
  - name: HF_TOKEN
    valueFrom:
      secretKeyRef:
        name: hf-token
        key: token
autoscaling:
  enabled: true
  behavior:
    scaleDown:
      stabilizationWindowSeconds: 300

When to Choose Which

Real-time chat applications (latency < 500 ms): Choose vLLM. Its PagedAttention algorithm delivers the lowest p99 latency for interactive workloads. Pair it with KEDA for queue-based scaling. For a complete observability setup, refer to How to Set Up LLM Observability with OpenTelemetry.

Batch processing (offline inference, large datasets): Choose Ray Serve. Its distributed scheduler handles multi-node inference natively. You can process 10,000 documents in parallel across 4 nodes with 8 GPUs each. The Ray dashboard gives you per-task latency and memory usage.

Prototyping and local development: Choose Ollama. It runs on a laptop with no GPU. You can test prompts and model behavior before moving to production. The trade-off is no continuous batching, so throughput is low.

Enterprise with existing Hugging Face workflows: Choose TGI. If your team already uses the Hugging Face Hub for model storage and versioning, TGI reduces operational overhead. The built-in safety filters also help with compliance requirements.

Custom model serving with complex preprocessing: Choose BentoML. It lets you define custom Python logic for tokenization, post-processing, and routing. The trade-off is higher operational complexity compared to vLLM or TGI.

Migration / Adoption Checklist

1. Benchmark your workload. Run a load test with your target model (for example, Llama 3 8B) on vLLM and TGI. Measure tokens per second and p99 latency at your expected request rate. Use the locust tool with the OpenAI-compatible endpoints.

2. Set up observability first. Deploy Prometheus and Grafana before the serving engine. Configure the Prometheus scrape targets for vLLM or TGI. Create dashboards for GPU utilization, request latency, and error rates. Without observability, you are flying blind.

3. Configure autoscaling. Use KEDA with a custom metric (queue depth or request latency) rather than CPU-based HPA. GPU-bound workloads do not scale linearly with CPU usage. Set a stabilization window of 300 seconds to avoid thrashing.

4. Implement a model registry. Use MLflow or a simple S3 bucket with versioned model artifacts. Never deploy a model by pulling the latest tag from a container registry. Pin the model version in your Helm values.

5. Test rollback procedures. Deploy a canary version of the new model alongside the old one. Use Argo Rollouts or Flagger to shift 10% of traffic to the new model. Monitor error rates and latency before promoting. For a GitOps approach, see How to Set Up Argo CD GitOps for Kubernetes Automation.

6. Document your cost model. Calculate the cost per 1,000 tokens for your chosen tool and hardware. Include GPU instance costs, storage for model artifacts, and network egress. Share this with your finance team before going live.