Best LLM Inference Stack (2026)
Serving a model in production is a throughput and cost problem before it is a quality one. The engine decides how many concurrent requests one GPU can hold; the gateway decides what happens when a provider is slow, expensive, or down; tracing decides whether you find out. Teams usually buy these three separately, and the first decision — operate the GPUs or rent them — constrains the other two.
Who is this for?
- Teams moving an open-weight model from a prototype to production traffic
- Engineers weighing a managed endpoint against their own GPUs
- Platform teams putting several model vendors behind one internal API
- Anyone comparing vLLM, SGLang, and TensorRT-LLM for self-hosted serving
How it works
A runtime or inference provider holds the weights and answers the request — vLLM or SGLang on your own GPUs, or a managed endpoint from Baseten, Fireworks, Together or Modal. A gateway (LiteLLM, OpenRouter) sits in front of it so application code does not name a provider: it carries the keys, the budgets, the rate limits and the failover. Tracing and evaluation (Langfuse, Helicone, Arize Phoenix) record the requests, the latency and the token spend. A frontier model provider is an optional layer, for the cases the open model does not handle.
Scroll horizontally to inspect every architecture layer.
Evidence-backed reference architecture based on selected constraints, public adoption signals, product evidence, and available integration data. See how recommendations are scored.
Estimated cost: Pricing unavailable
Why this recommendation
- Optimized for a default llm inference stack architecture across the required stack layers.
- Balances role fit, your stated requirements, and available integration evidence.
- Public activity is not part of Fit. It only separates candidates that fit equally well and are equally well evidenced.
Recommended tools
Runtime / Inference Provider
Commercial Ray platform for scaling AI workloads — managed infrastructure for training, fine-tuning, and serving ML models with Ray Serve and Ray Train.
Anyscale and Baseten both meet every requirement you set for this layer. Nothing we can assess separates them, so Anyscale comes first by a fixed ordering rather than by any advantage over Baseten.
Runner-up: Baseten
Tracing & Evaluation
LLM tracing and evaluation built on OpenTelemetry — run it in a notebook, a container or the free Phoenix Cloud tier, and keep the same spans when you move to Arize's commercial platform. Source-available under the Elastic Licence 2.0, not OSI open source.
Arize Phoenix and Braintrust both meet every requirement you set for this layer. Nothing we can assess separates them, so Arize Phoenix comes first by a fixed ordering rather than by any advantage over Braintrust.
Runner-up: Braintrust
How recommendations change with your constraints
The same architecture adapts to your cloud, budget, and deployment preferences. Here's what our algorithm recommends for common scenarios:
Managed Inference
Somebody else runs the GPUs. Dedicated or serverless endpoints for open models at production scale, with tracing over the traffic.
Customize this scenario →Self-hosted GPU
Your own GPUs and an open-source serving runtime, for many concurrent callers or data that cannot leave your network.
Customize this scenario →Cost-sensitive
A small team keeping the bill near zero: free tiers and open-source components rather than reserved capacity.
Customize this scenario →Frequently asked questions
vLLM or SGLang?▾
Both are open-source serving engines with an OpenAI-compatible API and both batch continuously. vLLM has the wider deployment base and the broader hardware and model coverage. SGLang's RadixAttention reuses the KV cache across requests that share a prefix, which is worth measuring on your own traffic if your prompts share a long system prefix or a common few-shot block.
Do I need a gateway if I only use one provider?▾
No, and that is why it is an optional layer here rather than a required one. A gateway earns its place the moment you have a second provider, several teams sharing keys, or a budget somebody has to enforce.
Is self-hosting cheaper?▾
Only above a load you can predict. A managed endpoint charges per token and scales to zero; your own GPUs charge by the hour whether or not anyone is asking. The crossover depends on utilisation, and the honest answer is to measure a week of real traffic before buying hardware.
Why is observability a required layer?▾
Because the failure mode here is financial as much as technical. An unrecorded inference stack cannot tell you which feature spent the money, which prompt got slow, or which provider quietly started returning worse answers.
Build your llm inference stack
These recommendations use public adoption signals, product evidence, and verified integrations. Customize them for your specific requirements and review the methodology behind the available evidence.