Listed together in guide “AI platforms and infrastructure”
Inference platform for fast serving and fine-tuning of open-source generative AI models.
Paid
Use cases
- Serving open models in production with low latency
- Fine-tuning models on custom data
- Migrating from closed APIs with minimal code change
Pricing: Pricing is usage-based per token, with dedicated capacity options; new developers receive free credits.
Pricing ↗ · Documentation ↗ · Sources
This record was last reviewed on 2026-09-10.
Listed together in guide “AI platforms and infrastructure”
AI inference platform powered by custom LPU hardware for very low-latency serving of open language models.
Freemium
Use cases
- Latency-sensitive apps needing fast responses
- Real-time assistants and agents
- Serving open models at high speed
Pricing: A free tier is available; production usage is priced per token through Groq's API.
Sources
This record was last reviewed on 2026-09-10.
Listed together in guide “AI platforms and infrastructure”
Platform for deploying and serving machine-learning models as scalable APIs.
Paid
Use cases
- Serving custom and open models in production
- Low-latency inference APIs
- Scaling ML services reliably
Pricing: Dedicated GPU compute is billed per minute with no charge for idle time, model APIs are billed per million tokens, and new accounts come with credits.
Documentation ↗ · Sources
This record was last reviewed on 2026-10-09.
Listed together in guide “AI platforms and infrastructure”
AI compute company whose inference cloud serves open language models on wafer-scale hardware.
Freemium
Use cases
- Latency-sensitive applications needing fast tokens
- Serving open models at high speed
- Large-model training on specialized hardware
Pricing: Inference is usage-priced per token and starts with a free trial; training systems and on-premises deployments are sold separately.
Sources
This record was last reviewed on 2026-10-09.
Listed together in guide “AI platforms and infrastructure”
Generative media inference platform for running diffusion and other models with a fast API.
Paid
Use cases
- Adding image and video generation to apps
- Serving open diffusion models at scale
- Prototyping generative media pipelines
Pricing: Pricing is usage-based per inference or compute second; new developers receive free credits.
Documentation ↗ · Sources
This record was last reviewed on 2026-10-09.
Listed together in guide “AI platforms and infrastructure”
Serverless cloud platform for running AI and data workloads with usage-based pricing.
Freemium
Use cases
- Serving model inference at scale
- Fine-tuning and batch processing jobs
- Running compute-heavy Python without DevOps
Pricing: Pricing is usage-based by compute consumed, with a monthly allowance of free compute included.
Documentation ↗ · Sources
This record was last reviewed on 2026-09-10.
Compared on “Fireworks AI vs Together AI vs Amazon Bedrock”
Managed AWS service for building applications with foundation models from multiple providers via one API.
Paid
Use cases
- Enterprise apps that need a choice of models under one API
- RAG systems grounded in private company data
- Regulated workloads requiring AWS data controls
Pricing: Pricing is usage-based per input/output token, with on-demand and provisioned-throughput options.
Pricing ↗ · Documentation ↗ · Sources
This record was last reviewed on 2026-10-09.
Related: shares the task Running models yourself
Google Cloud's platform for building and running AI models and agents, branded Gemini Enterprise Agent Platform (formerly Vertex AI).
Paid
Use cases
- Enterprise generative AI apps on Google Cloud
- Custom model training and tuning at scale
- Production MLOps and model monitoring
Pricing: Pricing is usage-based through Google Cloud, varying by model, training, and serving resources; new customers receive free credits.
Sources
This record was last reviewed on 2026-09-10.