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<h1 style="color:#7c3aed;font-size:28px;margin-bottom:8px;">MLOps & AI Infrastructure Terminal — Compare 26 LLM Gateways, Vector Databases & Model Serving Platforms (2026)</h1>
<p style="color:#94a3b8;font-size:15px;margin-bottom:20px;">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.</p>
<h2 style="color:#7c3aed;font-size:18px;margin:24px 0 10px;">LLM Inference Gateways & API Providers</h2>
<p style="color:#94a3b8;margin-bottom:12px;">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.</p>
<ul style="list-style:none;padding:0;display:grid;grid-template-columns:repeat(auto-fill,minmax(278px,1fr));gap:10px;margin-bottom:20px;">
<li style="background:#110d1e;padding:12px;border-radius:6px;border:1px solid #4c1d95;"><a href="/infrastructure/tool/groq" style="color:#a78bfa;font-weight:bold;text-decoration:none;">Groq</a><br><span style="color:#94a3b8;font-size:13px;">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.</span></li>
<li style="background:#110d1e;padding:12px;border-radius:6px;border:1px solid #4c1d95;"><a href="/infrastructure/tool/cerebras" style="color:#a78bfa;font-weight:bold;text-decoration:none;">Cerebras</a><br><span style="color:#94a3b8;font-size:13px;">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.</span></li>
<li style="background:#110d1e;padding:12px;border-radius:6px;border:1px solid #4c1d95;"><a href="/infrastructure/tool/together-ai" style="color:#a78bfa;font-weight:bold;text-decoration:none;">Together AI</a><br><span style="color:#94a3b8;font-size:13px;">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.</span></li>
<li style="background:#110d1e;padding:12px;border-radius:6px;border:1px solid #4c1d95;"><a href="/infrastructure/tool/litellm" style="color:#a78bfa;font-weight:bold;text-decoration:none;">LiteLLM</a><br><span style="color:#94a3b8;font-size:13px;">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.</span></li>
<li style="background:#110d1e;padding:12px;border-radius:6px;border:1px solid #4c1d95;"><a href="/infrastructure/tool/portkey-ai" style="color:#a78bfa;font-weight:bold;text-decoration:none;">Portkey AI</a><br><span style="color:#94a3b8;font-size:13px;">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.</span></li>
<li style="background:#110d1e;padding:12px;border-radius:6px;border:1px solid #4c1d95;"><a href="/infrastructure/tool/openrouter" style="color:#a78bfa;font-weight:bold;text-decoration:none;">OpenRouter</a><br><span style="color:#94a3b8;font-size:13px;">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.</span></li>
</ul>
<h2 style="color:#7c3aed;font-size:18px;margin:24px 0 10px;">Vector Databases for RAG & Semantic Search</h2>
<p style="color:#94a3b8;margin-bottom:12px;">Purpose-built databases for storing and querying high-dimensional embeddings — the core infrastructure for retrieval-augmented generation (RAG), semantic search, and recommendation systems.</p>
<ul style="list-style:none;padding:0;display:grid;grid-template-columns:repeat(auto-fill,minmax(278px,1fr));gap:10px;margin-bottom:20px;">
<li style="background:#110d1e;padding:12px;border-radius:6px;border:1px solid #4c1d95;"><a href="/infrastructure/tool/pinecone" style="color:#a78bfa;font-weight:bold;text-decoration:none;">Pinecone</a><br><span style="color:#94a3b8;font-size:13px;">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.</span></li>
<li style="background:#110d1e;padding:12px;border-radius:6px;border:1px solid #4c1d95;"><a href="/infrastructure/tool/weaviate" style="color:#a78bfa;font-weight:bold;text-decoration:none;">Weaviate</a><br><span style="color:#94a3b8;font-size:13px;">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.</span></li>
<li style="background:#110d1e;padding:12px;border-radius:6px;border:1px solid #4c1d95;"><a href="/infrastructure/tool/qdrant" style="color:#a78bfa;font-weight:bold;text-decoration:none;">Qdrant</a><br><span style="color:#94a3b8;font-size:13px;">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.</span></li>
<li style="background:#110d1e;padding:12px;border-radius:6px;border:1px solid #4c1d95;"><a href="/infrastructure/tool/chroma" style="color:#a78bfa;font-weight:bold;text-decoration:none;">Chroma</a><br><span style="color:#94a3b8;font-size:13px;">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.</span></li>
<li style="background:#110d1e;padding:12px;border-radius:6px;border:1px solid #4c1d95;"><a href="/infrastructure/tool/milvus" style="color:#a78bfa;font-weight:bold;text-decoration:none;">Milvus</a><br><span style="color:#94a3b8;font-size:13px;">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.</span></li>
</ul>
<h2 style="color:#7c3aed;font-size:18px;margin:24px 0 10px;">Model Serving & Deployment Platforms</h2>
<ul style="list-style:none;padding:0;display:grid;grid-template-columns:repeat(auto-fill,minmax(278px,1fr));gap:10px;margin-bottom:20px;">
<li style="background:#110d1e;padding:12px;border-radius:6px;border:1px solid #4c1d95;"><a href="/infrastructure/tool/vllm" style="color:#a78bfa;font-weight:bold;text-decoration:none;">vLLM</a><br><span style="color:#94a3b8;font-size:13px;">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.</span></li>
<li style="background:#110d1e;padding:12px;border-radius:6px;border:1px solid #4c1d95;"><a href="/infrastructure/tool/modal" style="color:#a78bfa;font-weight:bold;text-decoration:none;">Modal</a><br><span style="color:#94a3b8;font-size:13px;">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.</span></li>
<li style="background:#110d1e;padding:12px;border-radius:6px;border:1px solid #4c1d95;"><a href="/infrastructure/tool/fireworks-ai" style="color:#a78bfa;font-weight:bold;text-decoration:none;">Fireworks AI</a><br><span style="color:#94a3b8;font-size:13px;">Fastest open-source model deployment with compound AI system support. FireOptimizer provides automatic throughput optimization — deployed via API with no infrastructure management required.</span></li>
</ul>
<h2 style="color:#7c3aed;font-size:18px;margin:24px 0 10px;">LLM Orchestration Frameworks</h2>
<ul style="list-style:none;padding:0;display:grid;grid-template-columns:repeat(auto-fill,minmax(278px,1fr));gap:10px;margin-bottom:20px;">
<li style="background:#110d1e;padding:12px;border-radius:6px;border:1px solid #4c1d95;"><a href="/infrastructure/tool/langchain" style="color:#a78bfa;font-weight:bold;text-decoration:none;">LangChain</a><br><span style="color:#94a3b8;font-size:13px;">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.</span></li>
<li style="background:#110d1e;padding:12px;border-radius:6px;border:1px solid #4c1d95;"><a href="/infrastructure/tool/llamaindex" style="color:#a78bfa;font-weight:bold;text-decoration:none;">LlamaIndex</a><br><span style="color:#94a3b8;font-size:13px;">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.</span></li>
<li style="background:#110d1e;padding:12px;border-radius:6px;border:1px solid #4c1d95;"><a href="/infrastructure/tool/crewai" style="color:#a78bfa;font-weight:bold;text-decoration:none;">CrewAI</a><br><span style="color:#94a3b8;font-size:13px;">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.</span></li>
<li style="background:#110d1e;padding:12px;border-radius:6px;border:1px solid #4c1d95;"><a href="/infrastructure/tool/haystack" style="color:#a78bfa;font-weight:bold;text-decoration:none;">Haystack</a><br><span style="color:#94a3b8;font-size:13px;">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.</span></li>
</ul>
<h2 style="color:#7c3aed;font-size:18px;margin:24px 0 10px;">Frequently Asked Questions</h2>
<div style="background:#110d1e;padding:16px;border-radius:6px;border:1px solid #4c1d95;margin-bottom:10px;">
<strong style="color:#e2e8f0;">Pinecone vs Weaviate vs Qdrant — which vector database should I choose for RAG in 2026?</strong>
<p style="color:#94a3b8;margin:6px 0 0;">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.</p>
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<strong style="color:#e2e8f0;">LangChain vs LlamaIndex — which LLM framework is better for RAG applications?</strong>
<p style="color:#94a3b8;margin:6px 0 0;">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.</p>
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<strong style="color:#e2e8f0;">What is the fastest open-source LLM inference engine in 2026?</strong>
<p style="color:#94a3b8;margin:6px 0 0;">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.</p>
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<h3 style="color:#94a3b8;font-size:13px;text-transform:uppercase;letter-spacing:1px;margin-bottom:10px;">Related Resources</h3>
<ul style="list-style:none;padding:0;display:flex;flex-wrap:wrap;gap:10px;">
<li><a href="https://teachaitools.blog" style="color:#a78bfa;">Teach AI Tools Blog</a></li>
<li><a href="https://teachaitools.blog/blog" style="color:#a78bfa;">All AI Tool Reviews</a></li>
<li><a href="https://teachaitools.blog/llm-pulse" style="color:#a78bfa;">LLM Pulse — AI Model Leaderboard</a></li>
<li><a href="https://teachaitools.blog/governance" style="color:#a78bfa;">AI Governance Dashboard</a></li>
<li><a href="https://teachaitools.blog/fintech" style="color:#a78bfa;">FinTech AI Terminal</a></li>
</ul>
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