LangChain vs vLLM: Which One to Choose?

LangChain and vLLM operate at different layers of the LLM stack. LangChain orchestrates how your application interacts with language models. vLLM optimizes how those models run on hardware. Choosing between them is the wrong framing. The real question is whether you need one, the other, or both.

Let’s compare them across architecture, performance, and use cases to help you build the right stack.

What is LangChain?

LangChain is an open-source framework for building LLM-powered applications. It provides abstractions for chaining prompts, managing conversation memory, calling external tools, and orchestrating multi-step workflows.

The framework supports Python and JavaScript with over 600 integrations. You can connect to OpenAI, Anthropic, HuggingFace models, vector databases, and external APIs. LangChain handles the application logic while the actual inference happens elsewhere.

Common use cases include RAG systems, chatbots, document processing, and autonomous agents. The framework excels at prototyping and getting applications to market quickly.

What is vLLM?

vLLM is an open-source inference engine optimized for serving LLMs at scale. It focuses on one thing: running models fast with efficient memory usage.

The key innovation is PagedAttention, which manages GPU memory more efficiently than standard approaches. This enables higher throughput and lower latency, especially under concurrent load.

vLLM provides an OpenAI-compatible API server. You deploy it, point your application at it, and it handles the inference. It does not manage prompts, memory, or workflows. That is your application's job (or LangChain's).

Key Differences at a Glance

LangChain and vLLM operate at different layers of the LLM ecosystem. LangChain focuses on orchestrating workflows and integrating models into applications, while vLLM is optimized for high-throughput model inference. 

Criteria	LangChain	vLLM
Purpose	Application orchestration	Model inference
Architecture	Chains, agents, tools	Inference engine with PagedAttention
Performance	Depends on backend	Optimized for throughput/latency
Ease of Use	High-level abstractions	Server deployment
Scalability	Depends on infrastructure	Built for concurrent load
Model Support	Any via integrations	HuggingFace models
Use Cases	Prototyping, chatbots, RAG	Production inference APIs

In-Depth Technical Comparison

Architecture and Design

LangChain uses a composable abstraction system. Chains define sequences of operations. Agents decide which tools to use at runtime. Memory components track conversation state. Retrievers fetch relevant documents. These pieces connect together to form applications.

The framework does not run models itself. It calls external APIs (OpenAI, Anthropic) or connects to inference servers (vLLM, TGI, Ollama). LangChain adds orchestration overhead but enables complex workflows.

vLLM is a runtime engine. It loads a model onto GPUs, accepts requests via HTTP API, and returns completions. Internally, it uses continuous batching to process multiple requests simultaneously and PagedAttention to manage KV cache memory efficiently.

The architecture prioritizes inference speed over application features. vLLM has no concept of agents, chains, or memory. It serves completions.

Performance and Efficiency

vLLM benchmarks show significant performance advantages over naive serving approaches. Version 0.6.0 achieved 2.7x higher throughput and 5x faster time-per-output-token on Llama 8B compared to version 0.5.3. On Llama 70B, it achieved 1.8x higher throughput and 2x lower TPOT.

In benchmarks against Ollama, vLLM delivered peak throughput of 793 tokens per second compared to Ollama's 41 TPS, with P99 latency of 80ms versus 673ms at peak load. vLLM's throughput scaled with concurrent users while Ollama's remained flat.

Key metrics vLLM optimizes:

• TTFT (Time to First Token): How quickly the model starts responding. Critical for interactive applications.

• TPOT (Time Per Output Token): Average generation speed after the first token.

• ITL (Inter-Token Latency): Time between consecutive tokens. Affects perceived streaming speed.

• Throughput: Requests or tokens per second under load.

LangChain performance depends entirely on the backend. If you use LangChain with OpenAI's API, performance depends on OpenAI. If you use LangChain with vLLM as the backend, you get vLLM's performance plus LangChain's orchestration overhead.

The orchestration overhead is typically single-digit milliseconds. For most applications, it is negligible compared to inference time. For ultra-low-latency use cases (sub-50ms response requirements), it matters.

Model Compatibility and Customization

LangChain integrates with nearly any LLM provider: OpenAI, Anthropic, Google, Cohere, HuggingFace, local models, and custom endpoints. Switching providers often requires changing a few lines of code.

vLLM supports models available on HuggingFace that fit its architecture. This includes Llama, Mistral, Falcon, MPT, and many others. It supports quantized models (AWQ, GPTQ) for reduced memory usage. You can serve fine-tuned models and LoRA adapters.

vLLM does not support closed-source models like GPT-4 or Claude. Those require using the provider's API directly or through LangChain.

Prompt Management, Chains, and Workflow

LangChain's core strength is workflow orchestration:

• Prompt templates with variable substitution.

• Conversation memory that persists across turns.

• Multi-step chains that process outputs through multiple stages.

• Agents that decide actions at runtime.

• Retrievers that fetch documents for RAG.

vLLM has none of these. It accepts a prompt string and returns a completion. Any workflow logic lives in your application code.

If you need simple inference (send prompt, get response), vLLM alone works. If you need conversation memory, tool use, or multi-step reasoning, you need application logic, whether from LangChain or custom code.

Best Use Cases for LangChain

LangChain is ideal when your priority is orchestrating application logic and workflows rather than managing model infrastructure. Its abstractions simplify iterating on prompts, memory, and tool integrations.

MVPs and prototyping: Get a working application fast. LangChain's abstractions let you iterate on prompts, add memory, and integrate tools without building infrastructure.

Chatbots with memory: Conversation history management is built in. Choose from buffer memory, summary memory, or custom implementations.

RAG systems: Retrievers, document loaders, and vector store integrations handle the retrieval pipeline. Focus on tuning rather than building.

Multi-agent systems: LangGraph (part of the LangChain ecosystem) supports complex agent workflows with state management.

Teams exploring LLM capabilities: Non-ML engineers can build applications using high-level abstractions.

Best Use Cases for vLLM

vLLM is suited for situations where high-performance model inference is critical. It handles scale, low latency, and cost-efficient execution, allowing teams to run models directly on their own infrastructure.

Production inference APIs: When you need to serve a model at scale with low latency and high throughput.

Batch processing: Process large document sets efficiently. vLLM handles concurrent requests well.

Cost optimization: Run open-source models on your own infrastructure instead of paying per-token for API calls.

Low-latency applications: When milliseconds matter, eliminate API call overhead by running inference locally.

Data privacy requirements: Keep prompts and responses on your infrastructure. No data leaves your network.

Hybrid Approaches: When to Combine Both

Using LangChain with vLLM as the backend gives you both orchestration and performance. LangChain manages the application logic. vLLM handles inference.

Example configuration:

from langchain.llms import VLLMOpenAI

llm = VLLMOpenAI(  openai_api_base="http://localhost:8000/v1",
    model_name="meta-llama/Llama-3.1-8B-Instruct",
    openai_api_key="not-needed")

This setup is common for production deployments. You get LangChain's developer experience with vLLM's inference performance.

Operational Considerations

Infrastructure Requirements

LangChain: Runs on any Python environment. CPU is sufficient since inference happens elsewhere. Memory requirements depend on conversation history and document storage. A basic server handles most LangChain applications.

vLLM: Requires NVIDIA GPUs with CUDA. A100 (40GB or 80GB) and H100 are common for production. Smaller models (7B-13B) can run on consumer GPUs like RTX 4090. Memory requirements depend on model size and batch size.

Deployment options for vLLM include Docker containers and Kubernetes. The OpenAI-compatible API makes it easy to integrate with existing applications.

vLLM server example:

python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3.1-8B-Instruct \
    --tensor-parallel-size 1 \
    --port 8000

For larger models, use tensor parallelism across multiple GPUs:

python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3.1-70B-Instruct \
    --tensor-parallel-size 4 \
    --port 8000

Maintenance, Scalability, and Cost

LangChain: Frequent updates with occasional breaking changes. The team ships new versions every 2-3 months. Monitor the deprecation list to avoid breaking your application. Scaling depends on your infrastructure. If using external APIs, costs scale linearly with token usage.

vLLM: Active development with performance improvements in each release. v0.6.0 brought major throughput gains through multi-step scheduling and CPU optimization. Scaling requires more GPUs or horizontal scaling with load balancing across multiple vLLM instances.

Total cost comparison: API-based approaches (OpenAI via LangChain) have low upfront cost but scale linearly with usage. At high volume, costs become significant. Self-hosted vLLM has higher initial infrastructure cost (GPU servers) but lower marginal cost per request. The breakeven point depends on your usage volume.

Security, Privacy, and Data Control

LangChain with external APIs: Prompts and responses traverse third-party infrastructure. Check provider data handling policies for compliance (GDPR, SOC2, HIPAA).

vLLM on-premises: Complete data control. Nothing leaves your network. Required for many enterprises and regulated industry deployments.

For sensitive applications, run vLLM locally. Use LangChain for orchestration but route all inference through your own infrastructure.

Alternatives to Consider

Different LLM tools approach orchestration and inference in distinct ways.

LangChain alternatives:

• LlamaIndex: RAG-focused with strong indexing and retrieval capabilities

• Haystack: Production-grade pipelines for search and question answering

• CrewAI: Multi-agent systems with role-based agent design

• Semantic Kernel: Microsoft's orchestration framework with .NET support

vLLM alternatives:

• HuggingFace TGI: Similar inference server with good performance

• TensorRT-LLM: NVIDIA's optimized runtime, requires model compilation

• Ollama: Simple local deployment, better for development than production

• LMDeploy: Competitive throughput, especially for quantized models

Each option comes with specific strengths and constraints. TensorRT-LLM maximizes raw throughput but needs more setup, whereas Ollama trades scale for simplicity. Choosing the right tool depends on your concurrency, latency, and infrastructure requirements.

How to Evaluate Alternatives

Decision checklist:

• Latency requirements: What P99 latency is acceptable? Milliseconds matter for real-time applications.

• Throughput needs: How many concurrent users or requests per second?

• Data security: Can data leave your network? Compliance requirements (GDPR, SOC2, HIPAA)?

• Model requirements: Do you need GPT-4/Claude, or can you use open-source models?

• Team expertise: Python familiarity, infrastructure experience, ML background?

• Budget: API costs at projected volume vs infrastructure investment?

• Community support: Active development, documentation quality, issue response time?

Start with your non-negotiable requirements (data security, specific model needs) and filter from there.

Getting Started

LangChain is best when you need rapid prototyping, high-level abstractions for memory and agents, or integrations across multiple LLM providers and tools, especially if your team is not primarily ML engineers. vLLM fits production scenarios requiring high-throughput, low-latency inference, cost control at scale, or keeping data on your own infrastructure. 

For most projects, starting with LangChain and external APIs gets you a working application quickly. As requirements evolve, vLLM can be added as the backend to handle performance, scalability, and privacy needs. Using both together is common in production systems, leveraging each tool where it contributes most.

You can also reach out to our experts to discuss how to structure your LLM stack, optimise inference performance, or integrate LangChain and vLLM effectively for your projects.

Frequently Asked Questionsions

Is LangChain an LLM?

No, LangChain is not a Large Language Model (LLM); rather, it is an open-source framework for building applications that are powered by LLMs. It provides the tools and abstractions to connect different components, including LLMs, to create more complex and robust applications.

What is vLLM used for?

vLLM serves LLMs with optimized inference. It runs models on GPUs with high throughput and low latency. Common uses include production API servers, batch processing, and any application needing efficient local inference.

Can I use LangChain with vLLM?

Yes. LangChain can use vLLM as its inference backend via the OpenAI-compatible API. Configure LangChain to point to your vLLM server endpoint. You get LangChain's orchestration features with vLLM's inference performance.

Which is faster: vLLM or LangChain?

This compares different things. vLLM handles inference speed. LangChain adds orchestration. vLLM serving a model directly will have lower latency than LangChain calling the same vLLM server, but the difference is typically milliseconds. For most applications, it is negligible compared to the actual model inference time.