162 AI tools reviewed with real pricing, quickstart code, and honest gotchas
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If you are building for a Fortune 500 company, this is the only choice that matters because of the IP indemnification. For everyone else, it is frustratingly inaccessible. There is no simple $0.02/image API tier; you must go through Adobe's enterprise sales motion. The model is competent but 'safe'—don't expect the wild creativity of Midjourney, but do expect legally bulletproof assets.
Vast.ai is the 'Airbnb of GPUs'—unbeatable prices if you're willing to navigate variable hardware quality. It's perfect for researchers and hobbyists who need cheap compute (e.g., RTX 4090s for $0.28/hr), but enterprise production workloads should stick strictly to their 'Secure Cloud' verified datacenters or look elsewhere.
Together AI is the gold standard for developers who want to use open-source models (like Llama 3) without managing infrastructure. Their 'Inference Engine' is legitimately faster than most bare-metal setups due to deep kernel-level optimizations like FlashAttention. Use this if you need an OpenAI-compatible API for open models with blazing speed. Avoid it if you need general-purpose GPU compute for non-AI workloads (like rendering) or if you require a fully private VPC without enterprise contracts.
RunPod is the 'hacker's choice' for GPU compute—unbeatable prices if you're willing to navigate a slightly less polished UI than AWS. It excels at ephemeral workloads like model training and batch inference where spot instances can save you a fortune. Enterprise teams needing 99.99% uptime should stick to their Secure Cloud or look elsewhere, but for individual devs and startups, it's a goldmine.
Replicate is essentially the 'Heroku for AI'—it offers the best developer experience for running models via API but charges a premium for the convenience. It is perfect for prototyping, hackathons, and batch jobs where latency isn't critical. However, for 24/7 high-throughput production workloads, the cost per GPU-hour and cold start latency often make bare-metal providers or dedicated instances a better choice.
Modal is the 'serverless for people who hate serverless'—specifically for Python AI engineers. It abstracts away Kubernetes and Dockerfiles, letting you define infrastructure directly in your Python code with decorators. While its sub-second cold starts and scale-to-zero are best-in-class for inference, the proprietary backend means you're locked into their platform. Use it if you want to ship an LLM app in an hour; avoid it if you need multi-cloud portability or rock-bottom spot pricing.
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.
Crusoe is the 'conscientious objector' of GPU clouds, ideal for well-funded startups and enterprises that need hundreds of H100s but also have strict ESG mandates. Unlike hobbyist-friendly clouds (RunPod, Vast), Crusoe feels like a serious enterprise partner: no Discord, no random $0.20 GPUs, just reliable, high-end infrastructure powered by flared gas. If you need a Python SDK or instant serverless containers, look elsewhere; but if you need a green, SOC2-compliant cluster for training Llama-3, this is a top tier choice.
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.
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.
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.
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.
