LLM API routers, gateways, serving infrastructure, and model hosting. Tools that sit between your app and one or more language models.

LLM Gateways & Serving

## Anyscale: Scalable Compute for AI and Python

Anyscale is a unified compute platform that enables the development, deployment, and management of scalable AI and Python applications using Ray. Ray is the most popular open-source framework for scaling and productionizing AI workloads. Anyscale offers a comprehensive suite of tools and services to help organizations scale their AI workloads and achieve their AI goals.

## Key Features:
- 
- Scalable AI and Python: Anyscale provides a platform for developing, running, and scaling AI and Python applications using Ray.
- High Performance: Anyscale offers higher throughput, lower cost, and faster time to scale compared to other platforms.
- Trusted by Leading AI Teams: Anyscale is trusted by leading AI teams such as OpenAI, Uber, and Cohere.
- Anyscale Endpoints: Anyscale offers endpoints for adding open-source LLMs to apps, training LLMs, and fine-tuning open-source models.

## Ideal Use Case:

Anyscale is ideal for organizations looking to develop, deploy, and manage scalable AI and Python applications. It is suitable for AI teams, researchers, and developers who need a high-performance platform for AI workloads.

## Why use Anyscale:

- High Performance: Anyscale offers higher throughput, lower cost, and faster time to scale compared to other platforms.
- Trusted by Leading AI Teams: Anyscale is trusted by leading AI teams such as OpenAI, Uber, and Cohere.
- Scalable AI and Python: Anyscale provides a platform for developing, running, and scaling AI and Python applications using Ray.
- Anyscale Endpoints: Anyscale offers endpoints for adding open-source LLMs to apps, training LLMs, and fine-tuning open-source models.

Anyscale is a powerful platform for scalable AI and Python applications. With its high performance, trusted reputation, and comprehensive features, Anyscale is a valuable tool for AI teams, researchers, and developers.
### FAQ

**What does Anyscale help you build?**
Anyscale is a unified compute platform that lets you scale AI and Python applications using Ray. It handles the infrastructure complexity so you can focus on building and deploying your models and code at scale.

**Who should use Anyscale?**
Anyscale is designed for teams and organizations building scalable AI applications and Python workloads that need reliable, distributed computing infrastructure without managing the underlying systems themselves.

**How much does Anyscale cost?**
Anyscale is a paid platform. Visit the Anyscale pricing page for current plans and to discuss your specific needs with their team.

**How does Anyscale compare to alternatives like Grok, fal.ai, and Vercel AI SDK?**
Anyscale focuses specifically on providing a unified compute platform for Ray-based Python and AI applications, whereas alternatives like Grok, fal.ai, and Vercel AI SDK each take different approaches to AI infrastructure and deployment. Your choice depends on whether you need Ray's distributed computing framework and Anyscale's specific scaling capabilities.

### Related

Looking for more options? Browse the [AI Infrastructure](/top-ai-categories/ai-infrastructure) directory or read our [best AI infrastructure tools](/best-ai-collections/top-8-ai-infrastructure-tools-for-scalable-machine-learning) listicle. Anyscale is also tracked on [Crunchbase](https://www.crunchbase.com/organization/anyscale).


Anyscale

Anyscale-logo-symbol.jpg

Unified compute platform for scalable AI and Python applications using Ray

## Nebius

Nebius is an AI-native GPU cloud, traded on the Nasdaq as NBIS, that builds and operates large NVIDIA clusters for training and inference. Spun out of Yandex's non-Russian assets and led by Yandex co-founder Arkady Volozh, Nebius designs its own racks and data centers rather than reselling another provider's capacity. The platform gives AI teams bare-metal and virtualized access to H100, H200, B200 and GB200 GPUs, wired with InfiniBand and orchestrated through managed Slurm or Kubernetes. Alongside raw compute, Nebius runs a Token Factory inference API and managed data services. For labs and startups that want owned-hardware scale with a public-company balance sheet behind it, Nebius is a serious option.

**Production credibility:** Nebius Group N.V. is headquartered in Amsterdam and trades on Nasdaq under NBIS; its market capitalization was roughly $58 billion in late May 2026. The company was created in July 2024 when Yandex N.V. sold its Russian assets for about $5.2 billion (the largest corporate exit from Russia) and renamed the remaining non-Russian businesses Nebius Group, with Yandex co-founder Arkady Volozh (who first co-founded Yandex in 1997) as CEO; Nasdaq trading resumed in October 2024. NVIDIA made a strategic investment of approximately $2 billion. In Q1 2026 Nebius reported revenue of about $399 million, up roughly 684% year-over-year, and guided to $3.0-3.4 billion in 2026 revenue. Its contracted backlog approaches $50 billion, anchored by a reported ~$27 billion multi-year Meta deal and a commitment of up to $19.4 billion from Microsoft. Named customers include Brave, Recraft, CentML and the open-source vLLM project. Nebius is an NVIDIA Reference Platform Cloud Partner and is building an owned AI data center campus in Pennsylvania.

### Key Features
- Bare-metal and virtualized NVIDIA GPUs: H100, H200, B200, B300, GB200 NVL72 and GB300 NVL72
- High-bandwidth InfiniBand networking for multi-node distributed training
- Managed Slurm and managed Kubernetes orchestration for cluster scheduling
- Token Factory: a hosted LLM inference API for serving open-weight and custom models
- Managed data services including MLflow, PostgreSQL and Apache Spark
- Terraform provider, public API and CLI for infrastructure-as-code provisioning
- Owned data centers and racks rather than rented third-party capacity
- 24/7 support with solution architects for large training deployments

### Ideal Use Case
AI labs and infrastructure-heavy startups use Nebius to train and serve large models on reserved NVIDIA clusters when they need owned-hardware scale and predictable multi-year capacity rather than spot availability on a hyperscaler.

### How Nebius differentiates
Versus CoreWeave, Lambda and Together AI, the main difference is that Nebius is a public company (NASDAQ: NBIS) that owns and operates its own data centers and racks, which can mean steadier long-term capacity for anchor customers. Like CoreWeave, it sells reserved GPU clusters more than casual on-demand instances, so it suits committed training workloads over quick experiments. Compared with Lambda, Nebius leans further into full-stack managed orchestration plus a Token Factory inference layer rather than just GPU rental. The trade-off is that Nebius is not the place for someone who wants a single cheap A100 for an afternoon; its center of gravity is large, planned deployments. Its Yandex heritage also means most of its operating history sits outside the US market.

### FAQ

**Q: Is Nebius publicly traded and what is its ticker?**
A: Yes. Nebius Group N.V. trades on the Nasdaq under the ticker NBIS. Its market capitalization was roughly $58 billion in late May 2026, and it guided to $3.0-3.4 billion in 2026 revenue.

**Q: Who founded Nebius?**
A: Nebius Group was formed in July 2024 from the non-Russian assets of Yandex N.V. and is led by Yandex co-founder Arkady Volozh, who first co-founded Yandex in 1997. The company is headquartered in Amsterdam.

**Q: How much has Nebius raised or what backing does it have?**
A: As a public company Nebius is funded through the public markets rather than venture rounds, but it also took a strategic investment of approximately $2 billion from NVIDIA and has a contracted backlog approaching $50 billion.

**Q: Nebius vs CoreWeave: which should I choose?**
A: Both rent large NVIDIA GPU clusters for AI. Nebius is publicly traded (NBIS) and operates its own data centers with a managed Slurm/Kubernetes stack and a Token Factory inference API; CoreWeave is a larger pure-play GPU cloud. Choose based on region, GPU availability, pricing and contract terms.

**Q: What GPUs and services does Nebius offer?**
A: Nebius offers NVIDIA H100, H200, B200, B300, GB200 NVL72 and GB300 NVL72 GPUs over InfiniBand, plus managed Kubernetes and Slurm, managed data services, and a Token Factory LLM inference API.

### tl;dr
Nebius is an AI-native GPU cloud, publicly traded as NASDAQ: NBIS, that owns its data centers and rents NVIDIA H100 through GB200 clusters with managed Slurm, Kubernetes and an inference API. Spun out of Yandex and led by Arkady Volozh, it reported ~$399M revenue in Q1 2026 and a ~$50B backlog.

### Related

Looking for more options? Browse the [AI Infrastructure](/top-ai-categories/ai-infrastructure) directory or read our [best AI infrastructure tools](/best-ai-collections/top-8-ai-infrastructure-tools-for-scalable-machine-learning) listicle. Nebius is also tracked on [Crunchbase](https://www.crunchbase.com/organization/nebius).

Nebius product homepage screenshot showing the interface and branding

nebius.png

4368-logo.png

Nebius

Nebius is an AI-native GPU cloud platform that rents NVIDIA H100 through GB200 clusters with managed Slurm, Kubernetes and an inference API.

## Ollama

Ollama is a free, open-source tool that downloads, runs, and serves large language models on your own machine, no cloud account or API key required. Ollama wraps llama.cpp behind a one-line install and a simple CLI, so a developer can pull a model like Llama 3.3, Qwen, or gpt-oss and start chatting or hitting a local OpenAI-compatible endpoint in minutes. Built by ex-Docker engineers, Ollama has become the default way people experiment with local models, with an ecosystem of GUIs, libraries, and integrations built on top. It runs on macOS, Linux, and Windows, on CPU or GPU.

**Production credibility:** Created by Jeffrey Morgan and Michael Chiang, who previously built Kitematic (acquired by Docker) and the developer-infrastructure startup Infra; the team went through Y Combinator (W21). Disclosed outside funding is limited (a small pre-seed associated with YC and angels such as Essence Venture Capital), so Ollama's credibility comes from adoption rather than capital: 170K+ GitHub stars and a reported 52M+ monthly model pulls in early 2026, an official Docker image, first-party Python and JavaScript libraries, and a public library of thousands of community model builds in GGUF format.

### Key Features
- Run a model with a single command (`ollama run llama3.3`) and serve it over a local REST API.
- Pull from a public library of thousands of community model builds (Llama, Qwen, Gemma, gpt-oss, DeepSeek) in GGUF format.
- Exposes an OpenAI-compatible API endpoint, so existing client code can point at localhost with no rewrite.
- Built by Jeffrey Morgan and Michael Chiang, ex-Docker engineers behind Kitematic (acquired by Docker).
- Official Docker image (ollama/ollama) plus first-party Python and JavaScript client libraries.
- Reported 52M+ monthly model pulls in early 2026 and 170K+ GitHub stars; MIT-licensed and fully open-source.
- Supports quantized models sized to fit consumer GPUs and laptops, with optional GPU acceleration.
- Cross-platform: macOS, Linux, and Windows, running on CPU or GPU.

### Ideal Use Case
Use Ollama when you want to run an LLM privately on your own hardware, for offline work, sensitive data, local prototyping, or as a no-cost inference backend behind an application.

### How Ollama differentiates
Compared with cloud APIs from OpenAI or Anthropic, Ollama trades frontier model quality for privacy, offline use, and zero per-token cost, you supply the hardware. Versus LM Studio, Ollama is more script and server oriented and ships a cleaner API, while LM Studio offers a more polished desktop GUI. Versus running llama.cpp directly, Ollama hides the build flags and model plumbing at the cost of some low-level control. Versus vLLM, Ollama targets single-machine convenience rather than high-throughput production serving. It is a runtime and model hub, not a hosted endpoint, so you manage the box.

### FAQ

**Q: Is Ollama free?**
A: Yes. Ollama is free and open-source under the MIT license. There is no paid tier from the project itself, though you pay for the hardware it runs on.

**Q: Ollama vs LM Studio: what's the difference?**
A: Both run open models locally. Ollama emphasizes a command line, a server, and an OpenAI-compatible API, making it easy to embed behind apps. LM Studio emphasizes a desktop GUI for browsing and chatting with models. Many people use Ollama for automation and LM Studio for exploration.

**Q: Who founded Ollama?**
A: Jeffrey Morgan and Michael Chiang, ex-Docker engineers who previously created Kitematic (acquired by Docker). The company went through Y Combinator.

**Q: How much has Ollama raised?**
A: Disclosed outside funding is limited, primarily a small pre-seed tied to Y Combinator and angel investors. No large priced round or valuation has been publicly confirmed; Ollama's traction is driven by open-source adoption.

**Q: What hardware do I need to run Ollama?**
A: Smaller quantized models run on a modern laptop CPU; mid-size models benefit from a GPU with 8GB+ of VRAM or an Apple Silicon Mac. Larger models need more RAM/VRAM. Ollama auto-detects available GPUs.

### tl;dr
Ollama is a free, open-source runtime that lets you download and run large language models locally with one command and an OpenAI-compatible API. Built by ex-Docker founders, it is the default tool for private, offline, and on-device LLM use, with 170K+ GitHub stars and tens of millions of monthly model pulls.

### Related

Looking for more options? Browse the [Developer Tools](/top-ai-categories/developer-tools) directory or read our [best AI coding tools](/best-ai-collections/best-ai-tools-for-coding-code-smarter-not-harder) listicle. Ollama is also tracked on [Crunchbase](https://www.crunchbase.com/organization/ollama).

Ollama product homepage screenshot showing the interface and branding

ollama.png

Ollama brand logo mark shown as a square app icon

ollama-logo.png

Ollama

Ollama is a local LLM runtime that downloads, runs, and serves open models on your own hardware via a CLI and an OpenAI-compatible API.

## Together AI: Streamlining AI Development with Open-Source Solutions

TOGETHER is a cloud service designed for developers aiming to harness the power of open-source AI. The platform offers a suite of tools and services, including APIs, distributed training systems, and leading open-source models. With a focus on simplifying the complexities of training generative AI models, TOGETHER ensures that building AI models is fast, efficient, and straightforward.

### Key Features:

- Fine-tuning & Running Large AI Models: Utilize the Together API for efficient model management.
- Private GPU Clusters: Together Compute offers private GPU clusters and a training stack for custom fine-tuning.
- Leading Open-Source Models: Access RedPajama and other top-tier open-source models and datasets.
- Transparent Data Recipes: Complete transparency with data recipes, training code, and instruct datasets.
- Research-Driven Innovations: Breakthroughs like FlashAttention, FlexGen, and CocktailSGD for optimal AI optimizations.

### Ideal Use Case:

Developers, researchers, and AI enthusiasts looking for a comprehensive platform to fine-tune, run, and manage large AI models with ease.

### Why use TOGETHER:

- Simplified AI Training: Overcome the challenges of training generative AI models.
- Open-Source Ecosystem: Harness the power of leading open-source LLMs.
- Research-Backed Innovations: Stay updated with the latest AI optimizations.
- Comprehensive AI Stack: From APIs to GPU clusters, get everything in one place.

### FAQ

**What does Together do for developers?**
Together is a cloud service that lets developers build AI applications using open-source models through APIs and distributed training systems, making it easier to work with leading open-source AI models at scale.

**Who should use Together?**
Developers and teams building applications with open-source AI models will find Together useful, especially those needing flexible infrastructure for training, fine-tuning, or inference without being locked into proprietary solutions.

**How much does Together cost?**
Together operates on a paid model. Visit the Together pricing page for current plans and detailed pricing information based on your usage needs.

**How does Together compare to alternatives like GitHub Copilot or Cursor?**
Unlike GitHub Copilot and Cursor which focus on code completion, Together provides a broader cloud infrastructure for building with open-source AI models, offering APIs and distributed training capabilities for custom AI applications.

### tl;dr:

TOGETHER offers a cloud service for developers to seamlessly build with open-source AI, providing APIs, distributed training systems, and access to leading open-source models.
### Related

Looking for more options? Browse the [Developer Tools](/top-ai-categories/developer-tools) directory or read our [best AI coding tools](/best-ai-collections/best-ai-tools-for-coding-code-smarter-not-harder) listicle. TOGETHER is also tracked on [Crunchbase](https://www.crunchbase.com/organization/together).


TOGETHER logo with a cloud and AI symbols

TOGETHER's logo

TOGETHER

Cloud service for developers to build with open-source AI, offering APIs, distributed training systems, and leading open-source models.

## Groq: Pioneering Ultra-fast AI Solutions for Language Processing

Groq is at the forefront of AI innovation, offering ultra-fast Language Processing Unit™ systems combined with a streamlined software ecosystem. This powerful combination accelerates inference, ensuring optimal utilization of both human and technological resources. With a focus on real-time AI solutions, Groq's platform is designed to transform the way businesses handle and process vast amounts of data, especially in the realm of language processing. Their solutions are not only efficient but also easily integrable with popular platforms like Slack, Salesforce, and Notion.

### Key Features:

- Ultra-fast Language Processing: Leveraging the power of Groq's Language Processing Unit™ for rapid data processing.
- Simplified Software Ecosystem: A cohesive environment that accelerates inference and maximizes performance.
- Real-time AI Solutions: Designed for immediate data processing and insights extraction.
- Native Integrations: Seamless integration capabilities with popular business tools.

### Ideal Use Case:

Businesses and organizations that handle vast amounts of language data and require real-time processing and insights extraction.

### Why use Groq:

- Speed and Efficiency: Ultra-fast language processing capabilities ensure rapid data handling.
- Integrated Ecosystem: A cohesive software environment that simplifies AI tasks.
- Real-time Solutions: Designed for businesses that require immediate insights from their data.
- Cutting-edge Technology: Stay ahead of the curve with Groq's innovative AI solutions.

### FAQ

**What does Groq do?**
Groq provides enterprise-scale AI solutions designed for ultra-fast language processing and inference. It enables organizations to run large language models with exceptional speed and efficiency.

**Who should use Groq?**
Groq is built for enterprises and developers who need high-performance AI infrastructure for deploying and serving language models at scale. It's ideal for teams requiring rapid inference speeds for production applications.

**How much does Groq cost?**
Groq operates on a paid pricing model. Visit the Groq pricing page for current plans and to discuss your specific enterprise requirements.

**How does Groq compare to other AI infrastructure tools?**
Unlike alternatives such as Grok, fal.ai, and Vercel AI SDK, Groq specializes in ultra-fast inference and enterprise-scale language processing, positioning it as a dedicated solution for organizations prioritizing speed and reliability in AI deployment.

### tl;dr:

Groq offers cutting-edge AI solutions designed for ultra-fast language processing, ensuring businesses can extract real-time insights from their data efficiently.
### Related

Looking for more options? Browse the [AI Infrastructure](/top-ai-categories/ai-infrastructure) directory or read our [best AI infrastructure tools](/best-ai-collections/top-8-ai-infrastructure-tools-for-scalable-machine-learning) listicle. Groq has a [Wikipedia entry](https://en.wikipedia.org/wiki/Groq) and is tracked on [Crunchbase](https://www.crunchbase.com/organization/groq).


Groq logo with a blue background

601-logo.png

Groq

Enterprise-scale AI solutions for ultra-fast language processing and inference.

## Accelerate Product Innovation with Fireworks AI

Fireworks AI offers a cutting-edge platform for leveraging generative AI to drive product innovation. It provides state-of-the-art, open-source language and image models optimized for speed and cost-efficiency. Fireworks AI enables businesses to fine-tune and deploy their own models without additional costs, empowering organizations to focus on their core business objectives while enhancing their AI capabilities.

The platform is built on FireAttention, the world’s fastest inference engine, delivering unmatched speeds and reliability. With Fireworks AI, you can deploy models in minutes, scale seamlessly from small projects to enterprise-level applications, and ensure data security and compliance with industry standards.

### Key Features:

- Optimized Inference: Powered by FireAttention, offering 2.5x the generation speed and 6x lower costs compared to competitors.
- High-Quality Models: Provides access to top-tier open-source models and the ability to import and fine-tune custom models.
- Flexible Deployment: Simultaneously deploy up to 100 models for fast, serverless inference at no extra cost.
- Scalability: Scales with usage, supporting everything from small businesses to large enterprises.
- Developer-Friendly API: OpenAI-compatible API with enhanced user experience for quick and easy integration.
- Enterprise-Ready: Secure, reliable, and compliant with HIPAA and SOC2, offering dedicated deployments with VPC and VPN connectivity.
- Customizable Performance: Tailored setups configured for optimal performance and speed improvements using your own data.
- Cost Efficiency: Reduces operational costs while serving more customers with less overhead.

### Ideal Use Case:

- Fireworks AI is ideal for businesses and developers seeking to leverage generative AI for product innovation. It is particularly beneficial for:

- Tech Startups: Looking to integrate advanced AI capabilities without extensive infrastructure investment.
- Enterprises: Needing scalable, secure AI solutions for mission-critical applications.
- AI Developers: Wanting to fine-tune and deploy custom AI models quickly and efficiently.
- Product Managers: Aiming to enhance product offerings with cutting-edge AI technology.

### Why Use Fireworks AI:

- Efficiency: Delivers high-speed inference and fine-tuning, reducing time to market for AI-powered products.
- Scalability: Supports seamless scaling from small projects to large enterprise applications.
- Security: Ensures data privacy and compliance with industry standards.
- Cost-Effective: Offers competitive pricing with lower operational costs.
- Innovation: Empowers businesses to innovate with the latest AI technologies.

### FAQ

**What does Fireworks AI do?**
Fireworks AI is a generative AI platform designed for product innovation, offering high-speed inference and cost-efficient model serving with advanced fine-tuning capabilities. It helps teams build and deploy custom AI models quickly.

**Who should use Fireworks AI?**
Fireworks AI is built for product teams and developers who need to integrate generative AI into their applications and want control over model customization and deployment efficiency. It's ideal for organizations prioritizing speed and cost optimization in their AI workflows.

**How much does Fireworks AI cost?**
Fireworks AI operates on a paid model. Visit the Fireworks AI pricing page for current plans and detailed cost information.

**How does Fireworks AI compare to similar tools?**
Fireworks AI differentiates itself through its focus on speed and cost efficiency, complementing broader AI platforms like Claude and Anthropic. It specializes in model serving and fine-tuning, making it a strong choice for teams that need both performance and customization in their generative AI infrastructure.

### tl;dr:

Fireworks AI is a generative AI platform offering high-speed, cost-efficient models for product innovation. It supports advanced fine-tuning and scalable deployment, ensuring secure, reliable AI integration for businesses of all sizes.
### Related

Looking for more options? Browse the [AI/ML Models](/top-ai-categories/ai-ml-models) directory or read our [best AI models](/best-ai-collections/best-large-language-models-llm-of-2023) listicle. Fireworks AI is also tracked on [Crunchbase](https://www.crunchbase.com/organization/fireworks-ai).


Generative AI platform for product innovation

2623-logo.png

Fireworks AI

High-speed, cost-efficient generative AI for product innovation with advanced fine-tuning capabilities.

## Replicate: Simplifying Machine Learning Deployment and Scaling

Replicate is a platform designed to make machine learning deployment straightforward and efficient. It allows users to run machine learning models in the cloud without delving deep into the intricacies of machine learning. With just a few lines of code, users can execute models, and with tools like Cog, they can package these models into production-ready containers. Replicate also offers an automatic API generation for models, ensuring scalability and efficient resource utilization. Whether you're using open-source models or deploying custom, private models, Replicate streamlines the process, ensuring you focus on building products rather than the complexities of deployment.

### Key Features:

- Easy Model Execution: Run machine learning models in the cloud with minimal code.
- Cog Integration: Package machine learning models into production-ready containers.
- Automatic API Generation: Define your model, and Replicate will generate a scalable API server.
- Automatic Scaling: Replicate scales according to traffic, ensuring efficient resource utilization.
- Pay-per-use Model: Only pay for the actual runtime, ensuring cost-effectiveness.

### Ideal Use Case:

Developers and organizations looking for a streamlined process to deploy and scale machine learning models without the complexities of manual deployment.

### Why use Replicate:

- Simplified Deployment: No need to grapple with dependencies, configurations, or scaling issues.
- Cost-Effective: Pay only for the actual runtime, ensuring you're not charged for idle resources.
- Community Engagement: Access to a vast collection of models and integration with platforms like GitHub.
- Flexibility: Use open-source models or deploy custom models with ease.

### FAQ

**What does Replicate do?**
Replicate is a cloud platform that makes it easy to run, deploy, and scale machine learning models. It handles the infrastructure complexity so you can focus on building with AI.

**Who should use Replicate?**
Replicate is built for developers and teams who need to integrate machine learning models into their applications without managing servers or infrastructure themselves.

**How much does Replicate cost?**
Replicate uses a paid pricing model. Visit the Replicate pricing page for current plans and detailed pricing information.

**How does Replicate compare to similar tools?**
Unlike code assistants like GitHub Copilot and Cursor, Replicate focuses specifically on deploying and scaling ML models rather than code generation. It serves a different purpose than design tools like v0, targeting developers who need production-ready model serving infrastructure.

### tl;dr:

Replicate offers a seamless platform for deploying and scaling machine learning models, ensuring developers can focus on building products rather than deployment complexities.
### Related

Looking for more options? Browse the [Developer Tools](/top-ai-categories/developer-tools) directory or read our [best AI coding tools](/best-ai-collections/best-ai-tools-for-coding-code-smarter-not-harder) listicle. Replicate has a [Wikipedia entry](https://en.wikipedia.org/wiki/Replicate) and is tracked on [Crunchbase](https://www.crunchbase.com/organization/replicate).


re:tune logo emphasizing no-code AI solutions

612-logo.png

Replicate

Cloud platform for running, deploying, and scaling machine learning models with ease.

## RunPod: GPU Cloud for Efficient AI Inference & Training

RunPod offers a globally distributed GPU cloud designed for production, making AI inference and training seamless and efficient. With a focus on speed, scalability, and security, RunPod provides a platform that caters to a wide range of AI workloads.

### Key Features: 

- Deployable GPU Instances: Quickly deploy container-based GPU instances using both public and private repositories.
- Serverless GPUs: Benefit from pay-per-second serverless GPU computing with autoscaling, low cold-start times, and enhanced security.
- AI Endpoints: Fully managed and scalable for various workloads, including - - Dreambooth, Stable Diffusion, Whisper, and more.
- Diverse GPU Options: Choose from a range of GPU options, including A100, L40, RTX A6000, and more, with flexible pricing.
- Community and Secure Cloud: Opt for either the community cloud with vetted hosts and rock-bottom pricing or the secure cloud with enterprise-grade hardware and strict privacy measures.
- Cloud Sync: Easily download or upload pod data to any cloud storage.
- API, CLI, and GraphQL Integration: Automate workflows and deploy GPUs swiftly, leveraging spot GPUs for cost savings.
- Persistent Volumes: Pause and resume pods without data loss.

### Ideal Use Case:

RunPod is ideal for AI professionals, businesses, and developers who require a robust GPU cloud platform for their AI training and inference tasks. Its flexibility in GPU options and pricing makes it suitable for both small-scale tasks and large-scale production workloads.

### Why use RunPod:

- Rapid Deployment: Spin up GPU instances in seconds.
- Cost Efficiency: Take advantage of serverless GPUs and spot GPUs for significant cost savings.
- Versatility: A wide range of GPU options to fit different workloads.
- Security: Choose between community and secure cloud based on your privacy and security needs.
- Scalability: Fully managed AI endpoints that scale according to the workload.

### FAQ

**What is RunPod and what can I use it for?**
RunPod is a globally distributed GPU cloud platform designed for AI workloads. It provides the computational infrastructure needed to train, fine-tune, and deploy machine learning models and large language models at scale.

**Who should consider using RunPod?**
RunPod is built for machine learning engineers, AI researchers, and development teams who need flexible, on-demand GPU computing resources without the overhead of managing their own hardware infrastructure.

**What is the pricing structure for RunPod?**
RunPod operates on a paid model. Visit the RunPod pricing page for current plans and detailed cost information based on your GPU and compute requirements.

**How does RunPod compare to similar GPU cloud platforms?**
RunPod competes in the AI infrastructure space alongside alternatives like Grok, fal.ai, and frameworks like Vercel AI SDK, each offering different approaches to GPU access and model deployment for AI applications.

### tl;dr:

RunPod offers a versatile GPU cloud platform tailored for AI inference and training. With rapid deployment, cost-saving options, and a range of GPU choices, it caters to diverse AI workloads efficiently.
### Related

Looking for more options? Browse the [AI Infrastructure](/top-ai-categories/ai-infrastructure) directory or read our [best AI infrastructure tools](/best-ai-collections/top-8-ai-infrastructure-tools-for-scalable-machine-learning) listicle. RunPod is also tracked on [Crunchbase](https://www.crunchbase.com/organization/runpod).


RunPod GPU Cloud Platform Interface

661-logo.png

RunPod

Globally distributed GPU cloud for AI tasks.

## Modal: Simplifying Cloud Functions and Large-Scale Computing for Developers
Modal is redefining cloud computing by offering developers an intuitive platform to run generative AI models, batch jobs, job queues, and more in the cloud. It eliminates the hassle of managing infrastructure, providing containerized, serverless compute environments that are easy to use and highly scalable. Modal is engineered for large-scale workloads, making it an ideal choice for data-intensive applications.

### Key Features:
- Fast Cold-Start Times: Built-in Rust for rapid scaling and efficient performance.
- Custom Container Images: Deploy functions with custom images and hardware requirements.
- Versatile Cloud Functions: Supports a wide range of applications, including AI models and batch processing.
- Developer-Friendly Interface: Simplifies deployment without the need for complex configurations.
- Comprehensive Cloud Services: Offers environments, storage, job scheduling, web endpoints, observability, and security features.

### Ideal Use Case:
Modal is perfect for developers and teams working on data-intensive projects that require scalable, efficient cloud computing resources. It's particularly useful for those who need to run AI models or handle large-scale data processing without managing their own infrastructure.

### Why use Modal:
- Ease of Use: Simplifies cloud computing with an intuitive interface and quick deployment.
- Scalability: Efficiently handles large-scale workloads with dynamic scaling.
- Cost-Effective: Only pay for actual compute usage, with flexible pricing.
- Rapid Development: Accelerates the development process with fast deployments and no infrastructure management.
- Comprehensive Features: Offers a wide range of cloud services to meet diverse computing needs.

### FAQ

**What does Modal do for developers?**
Modal is a cloud platform that lets developers run code serverlessly without managing infrastructure. It handles containerized environments, making it straightforward to deploy and scale applications in the cloud.

**Who should use Modal?**
Modal is designed for developers who want to offload compute work to the cloud without dealing with server setup and maintenance. It's particularly useful for teams building applications that need reliable, scalable backend infrastructure.

**How is Modal priced?**
Modal uses a paid pricing model. Visit the Modal pricing page for current plans and details on what's included.

**How does Modal compare to other developer tools?**
While tools like GitHub Copilot and Cursor focus on code generation and editing, Modal solves a different problem by providing serverless cloud infrastructure for running code. It complements code-writing tools rather than replacing them.

### tl;dr:
Modal is transforming the way developers interact with the cloud, offering a user-friendly platform for running code in containerized, serverless environments. It's an efficient, scalable solution for data-intensive applications, enabling rapid development and deployment without the complexities of infrastructure management.
### Related

Looking for more options? Browse the [Developer Tools](/top-ai-categories/developer-tools) directory or read our [best AI coding tools](/best-ai-collections/best-ai-tools-for-coding-code-smarter-not-harder) listicle. Modal is also tracked on [Crunchbase](https://www.crunchbase.com/organization/modal).


Modal Cloud Computing

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Modal

Modal offers an easy way for developers to run code in the cloud with serverless compute and containerized environments.

## OpenRouter: Unified OpenAI-Compatible API for 200+ LLMs From Every Major Provider

OpenRouter is the unified API and marketplace for large language models, giving developers a single OpenAI-compatible endpoint that routes to 200+ models from OpenAI, Anthropic, Google, Meta, Mistral, DeepSeek, and many more. Engineers use OpenRouter to compare prices, fall over between providers, run prompt-level model selection, and avoid lock-in to any single AI lab. With strong pricing transparency, fast onboarding, and an active developer community, OpenRouter has become the default model gateway for many AI startups.

### Key Features:
- Unified OpenAI-compatible API across 200+ language models
- Pay-as-you-go pricing per model with full provider coverage
- Routing, fallbacks, and model selection per prompt
- Side-by-side model comparison and leaderboards
- Transparent token pricing and provider routing
- Free credits to start and team plans for production
- SDKs and integrations for popular AI frameworks
- Active Discord, GitHub, and developer community
- No lock-in to any single AI lab or provider

### Ideal Use Case:
OpenRouter is built for engineers and AI startups who want one API to access every major model and avoid lock-in to a single provider. It is especially valuable for teams that want to compare prices, fall over between providers, or pick models per prompt.

### Why Use OpenRouter:
- Use one OpenAI-compatible API for 200+ models
- Compare and route to the best model and price per prompt
- Avoid lock-in to any single AI lab or provider
- Fall over between providers when one has an outage
- Tap into transparent token pricing across the market
- Onboard fast with credits, then scale to team plans

### FAQ
**Is OpenRouter free?** Yes. OpenRouter offers free credits to start. After that, pricing is pay-as-you-go per model based on the upstream provider, plus a small OpenRouter fee.

**What models does OpenRouter support?** 200+ models from OpenAI, Anthropic, Google, Meta, Mistral, DeepSeek, and many other providers.

**Is OpenRouter OpenAI compatible?** Yes. OpenRouter exposes an OpenAI-compatible API so existing SDKs and frameworks work out of the box.

**Does OpenRouter support routing and fallbacks?** Yes. You can pick a primary model and define fallbacks per request.

**How is OpenRouter different from going direct?** OpenRouter gives you one API, transparent pricing across providers, and model-routing flexibility you would otherwise build yourself.

### tl;dr:
OpenRouter is the unified API and marketplace for the best LLMs at the best prices, with one OpenAI-compatible endpoint that routes to every major provider. For engineers and AI startups, it is the easiest way to avoid model lock-in.

### Related

Looking for more options? Browse the [Developer Tools](/top-ai-categories/developer-tools) directory or read our [best AI coding tools](/best-ai-collections/best-ai-tools-for-coding-code-smarter-not-harder) listicle. OpenRouter is also tracked on [Crunchbase](https://www.crunchbase.com/organization/openrouter).

openrouter_screenshot.png

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OpenRouter

Unified API and marketplace for the best LLMs at the best prices for any prompt.

## LiteLLM: Universal LLM Proxy

LiteLLM is Universal LLM proxy — 100+ providers via OpenAI-format API. Locking your codebase to one LLM vendor's SDK is a strategic mistake. LiteLLM gives you one OpenAI-format API across 100+ providers — switch models in one line.

### Key Features
- Universal LLM proxy — 100+ providers via OpenAI-format API
- Cost tracking, fallbacks, retries, observability
- 14K+ GitHub stars; Series A backed
- Used by every major agent framework
- Open source + cloud platform

### Ideal Use Case
Engineering teams running LLM workloads across multiple providers — for cost optimization, redundancy, model A/B testing, or vendor independence.

### Why Use LiteLLM
Locking your codebase to one LLM vendor's SDK is a strategic mistake. LiteLLM gives you one OpenAI-format API across 100+ providers — switch models in one line.

### FAQ

**Q: vs OpenRouter?**
A: Both unify provider APIs. LiteLLM is OSS-first, can self-host; OpenRouter is API-first.

**Q: Free?**
A: OSS core is free; cloud platform has tiers.

### tl;dr
Universal OSS LLM proxy. 100+ providers. 14K stars. The default for multi-LLM products.

### Related

Looking for more options? Browse the [Developer Tools](/top-ai-categories/developer-tools) directory or read our [best AI coding tools](/best-ai-collections/best-ai-tools-for-coding-code-smarter-not-harder) listicle. LiteLLM is also tracked on [Crunchbase](https://www.crunchbase.com/organization/litellm).

lite-llm.png

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LiteLLM

Universal LLM proxy — call 100+ LLMs (OpenAI, Anthropic, Bedrock, Vertex) with one API.

## Voltage Park

Voltage Park is a GPU cloud that rents NVIDIA H100 and Blackwell clusters for AI training and inference, either on-demand by the hour or on longer dedicated-reserve contracts. Voltage Park is unusual in its ownership: it was funded by the Navigation Fund, a nonprofit endowment created by Stellar and Ripple co-founder Jed McCaleb, and its profits flow back to that nonprofit. The company owns its hardware, around 36,000 Hopper, Blackwell and Grace Blackwell GPUs, connected with Quantum-2 InfiniBand and offered as bare metal. In January 2026 Voltage Park completed a merger with Lightning AI to form a combined AI cloud, though the Voltage Park brand and product still operate.

**Production credibility:** Voltage Park was launched in October 2023 with roughly $500 million in initial funding from the Navigation Fund, a 501(c)(3) nonprofit endowment founded by Jed McCaleb (co-founder of Stellar, Ripple and Mt. Gox); Voltage Park is structured as a subsidiary of that nonprofit, so its profits go back to the fund. It initially bought 24,000 NVIDIA H100 GPUs and now operates about 36,000 Hopper, Blackwell and Grace Blackwell GPUs across multiple US data centers. On January 21, 2026, Voltage Park completed a merger with Lightning AI; the combined company is valued at over $2.5 billion with annual recurring revenue reported above $500 million. William Falcon (Lightning AI) is CEO and former Voltage Park CEO Ozan Kaya became President. Voltage Park advertises on-demand H100 nodes from $1.99/hr, up to 3,200 Gbps of Quantum-2 InfiniBand bandwidth, and 99.982% uptime on a Tier 3+ architecture. Named users include Cursor, Phind and Caltech researchers.

### Key Features
- On-demand NVIDIA HGX H100 nodes, self-serve and bootable in about 15 minutes, from $1.99/hr
- NVIDIA Blackwell and Grace Blackwell GPUs available alongside Hopper
- Bare-metal access for direct hardware control during large training runs
- Up to 3,200 Gbps aggregate bandwidth over NVIDIA Quantum-2 InfiniBand
- Dedicated Reserve contracts (12+ months) for committed, long-term capacity
- Company-owned hardware across multiple US data centers rather than resold capacity
- Tier 3+ data center architecture with a stated 99.982% uptime
- No long-term commitment required for on-demand usage

### Ideal Use Case
ML teams use Voltage Park to spin up bare-metal H100 clusters on-demand for training and fine-tuning runs without signing a long contract, or to lock in dedicated reserve capacity for sustained production inference.

### How Voltage Park differentiates
Against CoreWeave, Lambda and Together AI, Voltage Park's distinguishing trait is its nonprofit ownership: it sits under Jed McCaleb's Navigation Fund, and profits return to that endowment rather than to outside venture investors. Like Lambda, it leans on transparent hourly pricing (from $1.99/hr) and self-serve bare metal, which suits teams that want to start a run quickly. Compared with CoreWeave's very large reserved deployments, Voltage Park is friendlier to on-demand bursts. The key caveat for buyers is the January 2026 Lightning AI merger: the two now operate as one company, so the long-term roadmap is shared with Lightning's software platform. Its GPU fleet is also smaller than the biggest neoclouds, so very large reservations may need to be scoped.

### FAQ

**Q: Who founded and funds Voltage Park?**
A: Voltage Park was funded by the Navigation Fund, a nonprofit endowment created by Jed McCaleb, co-founder of Stellar, Ripple and Mt. Gox. Voltage Park is a subsidiary of that nonprofit, so its profits flow back to the fund. Ozan Kaya led it as CEO before the Lightning AI merger.

**Q: How much funding does Voltage Park have?**
A: It launched in October 2023 with roughly $500 million from the Navigation Fund and bought 24,000 NVIDIA H100 GPUs. After merging with Lightning AI in January 2026, the combined company is valued at over $2.5 billion with reported ARR above $500 million.

**Q: Did Voltage Park merge with Lightning AI?**
A: Yes. Voltage Park and Lightning AI completed a merger on January 21, 2026 to form a combined AI cloud. The Voltage Park GPU product and brand still operate, with William Falcon as CEO of the combined company and former Voltage Park CEO Ozan Kaya as President.

**Q: How much does Voltage Park cost?**
A: Voltage Park lists on-demand NVIDIA H100 nodes starting at $1.99/hr with no long-term contract, plus Dedicated Reserve contracts of 12 months or more for committed, long-term GPU capacity.

**Q: Voltage Park vs Lambda: which is better for GPU rental?**
A: Both offer on-demand NVIDIA H100 bare metal with transparent hourly pricing. Voltage Park owns its hardware under a nonprofit endowment and is now merged with Lightning AI's software platform; Lambda is an independent GPU cloud. Compare GPU availability, regions and reserve terms.

### tl;dr
Voltage Park is a GPU cloud renting NVIDIA H100 and Blackwell clusters on-demand from $1.99/hr or on dedicated reserve, backed by Jed McCaleb's nonprofit Navigation Fund. It owns ~36,000 GPUs and, in January 2026, merged with Lightning AI into a combined ~$2.5B AI cloud.

### Related

Looking for more options? Browse the [AI Infrastructure](/top-ai-categories/ai-infrastructure) directory or read our [best AI infrastructure tools](/best-ai-collections/top-8-ai-infrastructure-tools-for-scalable-machine-learning) listicle. Voltage Park is also tracked on [Crunchbase](https://www.crunchbase.com/organization/voltage-park).

Voltage Park product homepage screenshot showing the interface and branding

voltage-park.png

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Voltage Park

Voltage Park is a GPU cloud platform that rents NVIDIA H100 and Blackwell clusters on-demand or on dedicated reserve for AI training and inference.

## BentoML: Unified AI Application Framework

BentoML is a platform designed specifically for software engineers to build AI products. With its comprehensive features, users can effortlessly build AI products using any pre-trained models, deploy them to production in mere minutes, and scale with utmost confidence.

### Key Features:

- Model Management: Manage and version all your models in an open and standardized format.
- Service APIs: Unify your AI app's business logic, pre/post-processing, model inference, and multi-model graphs within a single framework.
- Run Anywhere: Build once and run it anywhere, be it HTTP, gRPC, Server Sent Events, or Batch Inference.

### Ideal Use Case:

BentoML is perfect for software engineers and AI teams looking to streamline the process of building, deploying, and scaling AI applications. Whether you're a startup or an established enterprise, BentoML provides the tools and framework to bring your AI products to market faster and more efficiently.

### Why use BentoML:

- Versatility: Build AI products with any pre-trained models.
- Speed: Ship to production in minutes.
- Scalability: Scale your AI applications with confidence.
- Community Support: Join a global community with over 3000+ members and benefit from collective knowledge and experience.

### tl;dr:

BentoML is a comprehensive platform for software engineers, enabling them to build, deploy, and scale AI applications efficiently. With features like model management, unified service APIs, and the ability to run applications anywhere, it simplifies the AI product development process.

### FAQ

**Q: What is BentoML used for?**
A: Platform for software engineers to build AI applications.

**Q: How much does BentoML cost?**
A: Pricing varies by plan. Visit the BentoML pricing page for current tiers and details.

**Q: Who is BentoML's ideal user?**
A: BentoML is commonly used by developers and engineering teams.

**Q: What can replace BentoML?**
A: Top alternatives to BentoML include GitHub Copilot, Cursor, and v0. See our directory for in-depth comparisons.

### Related

Looking for more options? Browse the [Developer Tools](/top-ai-categories/developer-tools) directory or read our [best AI coding tools](/best-ai-collections/best-ai-tools-for-coding-code-smarter-not-harder) listicle. BentoML is also tracked on [Crunchbase](https://www.crunchbase.com/organization/bentoml).


BentoML AI Application Framework

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BentoML

Platform for software engineers to build AI applications.

Top LLM gateway and serving infra — OpenRouter, Portkey, LiteLLM, Together, Replicate, Groq, Fireworks, and the LLM inference layer.

LLM Gateways & Serving — 13 reviewed on ToolDirectory.AI

Route, host, and serve LLM inference at scale.

Best LLM Gateways & Serving 2026

Best LLM Gateways & Serving 2026 | ToolDirectory.AI

Compare 13 LLM Gateways & Serving AI tools reviewed by our editors. Pricing, features, alternatives, updated May 2026.

LLM Gateways & Serving

Featured

Anyscale

Paid - Inquire

4.83

360

Featured

Anyscale

Paid - Inquire

4.83

360

Featured

Anyscale

Paid - Inquire

4.83

360

Featured

Anyscale

Paid - Inquire

4.83

360

Featured

Anyscale

Paid - Inquire

4.83

360

Sorted by

Rating

Favorites

Trending

Nebius

Paid - Paid

4.93

470

Ollama

Free

4.93

470

TOGETHER

Paid - Inquire

4.93

441

Groq

Paid - Inquire

4.87

430

Fireworks AI

Paid - Inquire

4.92

420

Replicate

Paid - Inquire

4.92

420

RunPod

Paid - Inquire

4.91

410

Modal

Paid - $30 /mo

4.86

400

OpenRouter

Freemium

4.84

360

Anyscale

Paid - Inquire

4.83

360

LiteLLM

Freemium

4.75

325

Voltage Park

Paid - Paid

4.75

240

BentoML

Paid - Inquire