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Lambda Cloud

GPU Cloud

Lambda Cloud is the 'developer's choice' for raw, cheap GPU compute, consistently undercutting hyperscalers like AWS by 30-50% (e.g., A100s at $1.79/hr). It is excellent for training runs and fine-tuning where you need bare-metal performance without the 'driver tax'—their pre-installed Lambda Stack just works. However, it is NOT for teams needing serverless auto-scaling or guaranteed availability; stockouts are frequent, and you are renting VMs, not endpoints.

Python

CoreWeave

GPU Cloud

CoreWeave is the 'pro shop' of GPU clouds—built for engineers who dream in Kubernetes manifests and need thousands of H100s, not for hobbyists wanting a quick Jupyter notebook. While their raw GPU prices look competitive (e.g., $2.21/hr for A100), the hourly billing minimums and 'a la carte' pricing structure (CPU/RAM extra) mean it's overkill for casual users. Use this if you are an enterprise or research lab scaling production workloads; avoid it if you just want to run a 10-minute test script.

Paid Python Go

Banana

GPU Cloud

DO NOT USE. Banana.dev officially shut down its serverless GPU infrastructure on March 31, 2024, citing unsustainable business economics in the low-margin GPU resale market. While it was once a beloved tool for its 'Potassium' SDK and easy deployment experience, it is no longer operational. Former users have largely migrated to Modal, RunPod, or Fal.ai.

Python

Alibaba Cloud PAI

GPU Cloud

Alibaba PAI is a powerhouse for users already in the Alibaba Cloud ecosystem or those targeting the Asian market, offering battle-tested scale that handles massive events like Singles' Day. While its serverless inference (EAS) and spot pricing are highly competitive, the developer experience for non-Chinese speakers is marred by fragmented documentation and a complex UI. Use it if you need robust infrastructure in APAC; avoid it if you rely heavily on English-first community support or simple, one-click deployments.

Python Java Go

Voyage AI

Embedding Models

Voyage AI is the "specialist's choice" for embedding models, now backed by MongoDB's engineering muscle. If you are building RAG for legal, finance, or heavy codebases, their domain-specific models (like voyage-law-2) are arguably the best in class. However, for generic chat apps, the premium pricing ($0.12/1M tokens for top-tier) might be overkill compared to OpenAI's commoditized options. The new `voyage-4` series (released Jan 2026) brings Matryoshka embeddings to the masses, allowing you to truncate vectors to save DB costs without losing much accuracy—a killer feature for large-scale deployments.

Python JavaScript TypeScript

OpenAI Embedding API

Embedding Models

OpenAI's embedding API is the 'nobody ever got fired for buying IBM' of vector search—it's cheap, reliable, and integrated into everything. Use 'text-embedding-3-small' for 95% of use cases; it's virtually free ($0.02/1M tokens) and supports variable dimensions to save on vector DB costs. Avoid it if you need absolute state-of-the-art retrieval accuracy (look at Voyage or BGE-M3) or strict on-prem privacy requirements.

Python JavaScript TypeScript Java Go

Nomic Embed

Embedding Models

Nomic Embed is the 'good guy' of the embedding world—releasing not just weights but the actual training data, which is unheard of from OpenAI or Cohere. Use `v1.5` if you need a massive 8k context window for RAG over large documents; it's a workhorse that beats OpenAI's older models and trades blows with the new ones. Avoid the new `v2 MoE` model if you need long context, as it's capped at 512 tokens, though it's superior for multilingual tasks.

Python JavaScript Open Source

Mixedbread AI

Embedding Models

Mixedbread is a hidden gem for developers who want the performance of OpenAI's large embeddings but with the flexibility of open source. Their 'Matryoshka' models are a game-changer, allowing you to truncate vectors to save 50%+ on storage without retraining. Use this if you need high-performance, self-hostable English embeddings or want to optimize vector DB costs; avoid if you need deep multilingual support or 8k+ token context windows.

Freemium Python JavaScript TypeScript Open Source

Jina Embeddings

Embedding Models

Jina Embeddings is the 'senior engineer's choice' for complex RAG systems, offering features others ignore like variable output dimensions (Matryoshka) and native late interaction (ColBERT). It shines in multilingual and multimodal scenarios where OpenAI falls short. However, its CC-BY-NC license on 'open' weights is a trap for commercial self-hosters—if you're building a for-profit product, be prepared to pay for the API or an enterprise license.

Paid Python JavaScript Open Source

Google Vertex Embeddings

Embedding Models

Vertex AI embeddings are the industrial-grade choice for teams already in the Google ecosystem. The new `gemini-embedding-001` model finally adds Matryoshka support and competitive MTEB scores (~68.3), making it a serious rival to OpenAI. Use it if you need enterprise compliance and massive scale; avoid it if you just want a simple API key without managing IAM permissions.

Python Java Node.js Go

Cohere Embed

Embedding Models

Cohere Embed is the 'senior engineer's choice' for enterprise RAG—prioritizing noise robustness and real-world retrieval over raw academic benchmarks. While OpenAI's embeddings are the default, Cohere's v4 model outshines them with a 128k context window, native multimodal support, and Matryoshka embeddings that let you slash vector storage costs by up to 96%. Use this if you're building serious multilingual search or need to embed complex documents; skip it if you just need a cheap, simple vector for a side project.

Python JavaScript TypeScript Go Java

BGE (BAAI)

Embedding Models

BGE is the go-to open-source choice for developers who want state-of-the-art embedding performance without paying OpenAI or Cohere. The BGE-M3 model is a technical marvel, offering hybrid retrieval (dense + sparse) in a single pass, while the newer BGE-Multilingual-Gemma2 tops benchmarks with a massive 74.1 score. Use it if you can manage self-hosting or use a provider like DeepInfra; avoid it if you just want a simple, managed API endpoint and don't care about squeezing out the last 5% of retrieval accuracy.

Python Open Source