Comparison of Generative AI Libraries and Platforms: How to Pick What Fits Your Stack

If you're building a generative AI application, you're going to make two separate decisions that most teams conflate. First, which library handles orchestration: prompt construction, retrieval, memory, tool calling. Second, which platform serves the model: where the GPU lives, where inference runs, where you pay per token or per hour. A comparison of generative ai libraries and platforms only makes sense once you accept that these are different layers. Libraries run in your codebase. Platforms run the model.

What libraries do and what platforms do

The distinction sounds obvious until you start shopping. Libraries like LangChain, LlamaIndex, and Haystack are SDKs you install with a package manager. They give you abstractions for chains, agents, retrievers, document loaders, and memory. They don't run the model. They call a model endpoint, either one you self-host or one provided by a platform. The library controls how you compose prompts and pipeline logic. The platform controls how the model executes.

Platforms are where the GPU lives. A managed inference platform takes a model, deploys it on GPU infrastructure, exposes an API, scales replicas with traffic, and bills you for compute. Some platforms offer their own SDKs, which is where the confusion starts. A platform SDK handles authentication, request routing, and model selection specific to that platform. A library handles the application logic layer above that.

Here's a quick breakdown of which layer handles what:

• Libraries handle: prompt templates, retrieval-augmented generation pipelines, agent logic, conversation memory, document chunking and embedding, tool and function calling composition.
• Platforms handle: model loading, GPU scheduling, autoscaling, request batching, latency optimization, billing, and endpoint management.

The practical implication is that swapping a library is a code-level change. Swapping a platform is an infrastructure-level change. Get the library right for your developer ergonomics. Get the platform right for your cost and latency.

Comparing the main generative AI libraries

Three libraries dominate the generative AI orchestration space. They overlap heavily but optimize for different defaults. Here's how they compare on the dimensions that actually affect your codebase.

Dimension	LangChain	LlamaIndex	Haystack
Primary focus	Agent and chain composition	Data ingestion and retrieval	Production search and RAG pipelines
Abstraction style	Broad, many integrations	Document-centric, index-first	Pipeline-centric, component graph
Learning curve	Steep, large API surface	Moderate, RAG-oriented	Moderate, opinionated structure
Agent support	First-class, multiple agent types	Growing, experimental	Mature via pipeline nodes
Best fit	Complex multi-tool agents	Document-heavy retrieval apps	Enterprise search and RAG

LangChain

LangChain's strength is breadth. It has the largest collection of integrations with model providers, vector stores, tools, and data loaders. If you're building an agent that calls multiple tools and needs to swap providers without rewriting logic, LangChain gives you the abstractions to do that.

LlamaIndex

LlamaIndex starts from data. Its core abstractions are documents, nodes, indices, and query engines. If your application is primarily retrieval-augmented generation over a large document corpus, LlamaIndex's defaults are tuned for that workflow out of the box. It handles chunking strategies, embedding models, and retrieval ranking with less manual configuration than LangChain. Its agent story is less mature than LangChain's.

Haystack

Haystack, built by deepset, takes an opinionated pipeline approach. You compose components into a directed graph, and the pipeline handles execution order, routing, and debugging. This structure shines in enterprise settings where you need reproducible, testable pipelines for search and RAG. Haystack's component model makes it easier to unit-test individual steps and version your retrieval logic. The trade-off is that it's less flexible for experimental agent patterns.

Comparing platform categories

Once you've picked a library, the next question is where the model runs. There are three realistic platform categories, and the trade-offs map to cost, control, and how much operations you want to handle.

Dimension	Serverless API platforms	Managed dedicated endpoints	Self-hosted on GPU infrastructure
Pricing model	Per-token or per-request	Per-GPU-hour, dedicated	Per-GPU-hour, you own scheduling
Cold start	Yes, varies by provider	Minimal, persistent	None, always warm
Scaling	Automatic, scale to zero	Manual or autoscaled	You build it
GPU model control	Limited to offered models	Full, you pick the model	Full, you pick everything
Best fit	Prototyping, variable traffic	Stable production inference	Maximum control, sustained load

Serverless API platforms

Serverless platforms expose a model behind an API endpoint and charge per token or per request. You don't manage GPUs, replicas, or scaling. This is the fastest path from idea to working application, and for many prototypes and low-traffic pilots, it's the right choice.

Managed dedicated endpoints

A managed dedicated endpoint gives you a GPU (or a set of GPUs) reserved for your workload. You pick the model, the platform deploys it, and you pay per GPU-hour. There's no cold start because the endpoint is always warm. You get consistent latency and predictable cost. This is where most production generative AI workloads land once traffic stabilizes. You're paying for the GPU whether or not traffic is hitting it.

Self-hosted on GPU infrastructure

Self-hosting means you rent or own GPU hardware and run the model serving stack yourself. You control everything: the model, the serving framework (vLLM, TGI, TensorRT-LLM), the batching logic, the networking. This gives you maximum control and, at high utilization, the lowest cost per token. The cost is operational. You need a team to manage deployments, monitor GPU health, and keep the serving stack updated. For teams with sustained, predictable, high-volume inference workloads, the control is worth the overhead. For everyone else, it's usually not.

Pairing libraries with platforms

The library you pick should be independent of the platform you pick, with one consideration: how well the library integrates with the platform's API. Most libraries support OpenAI-compatible endpoints, which means any platform that exposes an OpenAI-compatible API works with any of the three libraries. If your platform speaks OpenAI format, you can swap libraries without changing platforms, and swap platforms without changing libraries.

Here's a pairing sequence that works for most projects:

1. Pick the library by application shape. Agent-heavy work points to LangChain. Document retrieval points to LlamaIndex. Structured enterprise search points to Haystack. Pick it because it fits how your application reasons about data and tools.
2. Pick the platform by stage and traffic. Prototyping and bursty traffic point to serverless APIs. Stable production traffic points to managed dedicated endpoints. Sustained, high-volume load points to self-hosted on dedicated GPU infrastructure.
3. Verify OpenAI-compatible endpoints. Confirm the platform exposes an OpenAI-compatible API. This lets your library call it without custom adapters and gives you portability if you switch platforms later.
4. Measure cost per token, not per GPU-hour. A serverless API at $0.0001 per token can cost more than a dedicated endpoint at $2.00 per GPU-hour if your traffic is high enough. Run real traffic through both for a week and compare delivered cost.
5. Plan the migration path before you need it. Know what it takes to move from serverless to dedicated, or from dedicated to self-hosted. The cheapest migration is the one you planned for, not the one you rush under load.

Where GMI Cloud fits in the library-to-platform stack

GMI Cloud is an AI-native inference cloud built for production AI, and as an NVIDIA Reference Architecture Provider, it designs the stack from the GPU up for inference and training rather than retrofitting AI onto a general-purpose cloud. GMI Cloud sits on the platform side, not the library side. You bring whatever library you've chosen (LangChain, LlamaIndex, Haystack, or a custom SDK) and point it at GMI Cloud's inference endpoints.

GMI Cloud's platform is designed so that the migration path between stages doesn't require switching providers. A team that starts on the serverless API can move to a dedicated endpoint as traffic stabilizes, and scale into a bare metal cluster for sustained production load, all on the same platform without re-architecting. The infrastructure is backed by 30,000-plus GPUs deployed, 99.99 percent platform availability, and sub-200ms average cross-region latency across regions in North America, Europe, and Asia-Pacific. GMI Cloud's inference platform is complementary to generative AI libraries rather than competitive with them, which means the library decision and the platform decision can be made independently.

Matching the stack to your project stage

The library-to-platform pairing changes as a project moves from prototype to production. Trying to run production-grade infrastructure during prototyping wastes money. Trying to run prototype-grade infrastructure in production causes outages.

Stage	Library focus	Platform category	Why
Prototype	Rapid iteration, simple chains	Serverless API	Fastest setup, no GPU management, scale to zero
Pilot	Retrieval tuning, memory, tools	Serverless or small dedicated	Test real traffic patterns without overcommitting
Production	Stable pipelines, monitoring	Managed dedicated endpoint	Consistent latency, predictable cost, no cold starts
Scale	Optimized pipelines, batching	Bare metal or managed cluster	Lowest cost per token at high volume, full control

Most teams skip the pilot stage and jump from prototype directly to production, which means they either over-provision during prototyping or under-provision when real traffic arrives. The pilot stage is where you learn your actual utilization pattern, your real latency targets, and your real cost per token before committing to a platform category.

Pick the library for your code, pick the platform for your traffic

The comparison of generative ai libraries and platforms comes down to two independent decisions. First, pick the library that matches how your application reasons about data and tools: LangChain for agent composition, LlamaIndex for document retrieval, Haystack for structured RAG pipelines. Second, pick the platform that matches your traffic shape and operational capacity: serverless APIs for prototyping and bursty load, managed dedicated endpoints for stable production, self-hosted GPU infrastructure for sustained high-volume control. Keep the two layers separate, verify OpenAI-compatible endpoints so you can swap either layer without rewriting the other, and measure cost per token rather than cost per GPU-hour. GMI Cloud provides the platform-side inference infrastructure, and you can review current rates on the GMI Cloud pricing page and the models catalog.