MLOps & AI Infrastructure Terminal — Compare 26 LLM Gateways, Vector Databases & Model Serving Platforms (2026)
The most complete MLOps tool comparison platform for ML engineers and AI infrastructure teams. Evaluate LLM inference gateways, vector databases, model serving platforms, and orchestration frameworks side-by-side on throughput, latency P99, cost per million tokens, deployment complexity, and production readiness. Includes AI-powered scoring, multi-tool chat, and benchmark comparisons.
LLM Inference Gateways & API Providers
Platforms for running open-source LLMs in production at scale — compare token throughput, latency, supported models, and pricing across the leading inference providers and gateway tools.
- Groq
Purpose-built LPU inference engine delivering the fastest token throughput for open-source LLMs. 800+ tokens/second on Llama 3 — 10x faster than GPU-based inference. Best for latency-sensitive applications like voice AI and real-time agents.
- Cerebras
Wafer-scale chip inference delivering 2,200+ tokens/second on frontier models — the fastest hardware for LLM serving. Cerebras Cloud is available for Llama 3.1 70B and 405B at record speeds.
- Together AI
Full-stack AI cloud for training, fine-tuning, and deploying open-source models with dedicated GPU clusters. Best for teams that need both inference and fine-tuning under one contract — supports custom model deployment.
- LiteLLM
Open-source LLM gateway providing unified OpenAI-compatible API across 100+ providers with cost tracking, load balancing, and spend controls. Most deployed open-source LLM proxy — essential for multi-provider AI architectures.
- Portkey AI
Production LLM gateway with semantic caching (80%+ cost reduction on repeat queries), guardrails, observability, and multi-provider failover. SOC 2 Type II certified for enterprise deployments.
- OpenRouter
Multi-LLM API aggregator routing to the best available model for each request by price and quality. Single API key for GPT-4, Claude, Gemini, Llama, and 200+ models — ideal for model-agnostic applications.
Vector Databases for RAG & Semantic Search
Purpose-built databases for storing and querying high-dimensional embeddings — the core infrastructure for retrieval-augmented generation (RAG), semantic search, and recommendation systems.
- Pinecone
Managed vector database built for production ML — sub-10ms ANN search across billions of vectors. Most widely used managed vector DB — chosen by Shopify, Notion, and Gong for production RAG deployments.
- Weaviate
Open-source vector database with GraphQL API, multimodal support, and built-in vectorization modules. Only vector DB that can generate embeddings natively — no separate embedding model call required.
- Qdrant
High-performance Rust-based vector database with advanced payload filtering, sparse vectors, and on-disk indexing. Fastest filtered vector search benchmark results in 2024 — ideal for multi-tenant RAG with complex metadata filters.
- Chroma
Open-source embedding database for LLM apps — zero-config local development with a simple Python/JS API. Most beginner-friendly vector database — the default choice for RAG tutorials and prototype development.
- Milvus
Cloud-native open-source vector database for billion-scale similarity search with GPU acceleration and Kubernetes-native deployment. Powers Alibaba, Tencent, and eBay at hyperscale — handles 100B+ vectors in production.
Model Serving & Deployment Platforms
- vLLM
Open-source high-throughput LLM serving engine with PagedAttention — the de facto standard for self-hosted model deployment. 24x higher throughput than Hugging Face Transformers with the same hardware. Used at Google, Meta, and Microsoft for internal LLM serving.
- Modal
Serverless GPU cloud for ML workloads — deploy Python functions as scalable GPU endpoints with zero infrastructure configuration. Pay-per-second billing with cold start times under 1 second for cached containers.
- Fireworks AI
Fastest open-source model deployment with compound AI system support. FireOptimizer provides automatic throughput optimization — deployed via API with no infrastructure management required.
LLM Orchestration Frameworks
- LangChain
The de facto standard framework for building LLM-powered applications with chains, agents, and retrieval pipelines. 100M+ downloads — the most widely adopted LLM framework with the largest ecosystem of integrations.
- LlamaIndex
Data framework for LLM apps — ingestion, indexing, and querying pipelines for RAG, agents, and structured data extraction. Better than LangChain for complex multi-document RAG with structured data sources.
- CrewAI
Multi-agent task orchestration framework for building collaborative AI crews with role-based agents, task delegation, and sequential/parallel workflows. Fastest-growing multi-agent framework in 2024 — 20M+ downloads.
- Haystack
Production NLP pipeline framework by deepset — component-based pipelines for RAG, document search, and agentic AI with deep evaluation tooling. Best choice for teams building enterprise search and document QA at scale.
Frequently Asked Questions
Pinecone vs Weaviate vs Qdrant — which vector database should I choose for RAG in 2026?
Pinecone is the safest choice for teams that want a fully managed solution with enterprise SLAs — no ops overhead. Qdrant is the best self-hosted option for production RAG with complex metadata filtering, and benchmarks fastest for filtered ANN search. Weaviate wins when you need multimodal search (text + images) or want the vectorizer built in. For prototyping, use Chroma — it runs locally in one pip install and speaks the same API semantics as production options.
LangChain vs LlamaIndex — which LLM framework is better for RAG applications?
LlamaIndex is specifically optimized for RAG — its data connectors, query engines, and index types are purpose-built for document retrieval. LangChain is broader and better for agentic applications that need tool use, memory, and multi-step planning. In practice, many teams use LlamaIndex for the retrieval layer and LangChain or CrewAI for the agent layer on top.
What is the fastest open-source LLM inference engine in 2026?
vLLM remains the gold standard for self-hosted serving with PagedAttention delivering 24x higher throughput than naive Transformers serving. For managed inference, Groq's LPU hardware hits 800+ tokens/second on Llama 3.3 70B — 10x faster than A100 GPU inference. Cerebras Cloud delivers 2,200+ tokens/second on its wafer-scale hardware for the absolute fastest generation.