Hugging Face Inference Endpoints alternatives: 12 tools compared (October 2026)

Baseten is an inference platform for teams that run open-source, fine-tuned or custom AI models in production. It provides hosted Model APIs, dedicated GPU deployments with autoscaling and training jobs, either in Baseten's cloud or in the customer's own environment.

Key functions

• Dedicated deployments scale down to zero replicas, with GPU time billed per minute
• Model APIs with an OpenAI-compatible endpoint for hosted open models
• Inference engines built on TensorRT-LLM for language models and BEI for embeddings

Compared with Hugging Face Inference Endpoints

• Offers a free trial; we found none for Hugging Face Inference Endpoints.
• Starts lower: from $0.01 usage-based, Hugging Face Inference Endpoints from $0.03 usage-based.
• Strong in the same markets as Hugging Face Inference Endpoints: United States.

Check before buying: Check which GPU types and regions your workloads need, because the listed prices apply to standard capacity and other countries and regions go through sales. For EU personal data, review the public DPA, the subprocessor list in the trust center and the transfer basis, which relies on Standard Contractual Clauses. Compare per-minute dedicated pricing with per-token Model APIs for your expected traffic, and note that custom SLAs are part of Enterprise only.

CoreWeave is a GPU cloud built for AI workloads. It rents NVIDIA GPU capacity for model training and inference to AI labs, software companies and enterprises that need large clusters.

Key functions

• Serverless Inference serves open-source models from a catalog, billed per token
• Dedicated Inference deploys custom or fine-tuned models on selected GPUs
• SUNK combines Slurm and Kubernetes for large-scale model training

Compared with Hugging Face Inference Endpoints

• Built for larger organizations than Hugging Face Inference Endpoints's smallest customers.
• Names 4 security certificates, Hugging Face Inference Endpoints 1.
• Starts higher: from $6.50 usage-based, Hugging Face Inference Endpoints from $0.03 usage-based.

Check before buying: Clarify which region your data and model weights will be stored in, since the pricing page names North America and Europe without a finer breakdown. Hourly GPU prices add up quickly for idle instances, so plan reservations or spot use. Request the security documentation through the trust center and check the data processing terms before sending personal data to inference endpoints.

Modal is a serverless compute platform where developers run AI inference, training, batch jobs and sandboxes from Python code, with GPUs billed per second and a free Starter plan.

Key functions

• Serverless inference for language, audio, image and video models with autoscaling
• Fine-tuning on single or multi-node clusters
• Sandboxes that run AI-generated code in isolated containers

Compared with Hugging Face Inference Endpoints

• Has a free plan; Hugging Face Inference Endpoints does not.
• Strong in the same markets as Hugging Face Inference Endpoints: United States.

Check before buying: Select the region for functions and storage explicitly and budget for the regional surcharge. Inputs and outputs are kept for up to seven days, and logs for one day on Starter, 30 days on Team and a configurable period on Enterprise. A data processing agreement and subprocessor list are not linked publicly, so request them before processing personal data.

Pinecone is a fully managed vector database for semantic search, retrieval-augmented generation and recommendations. Teams store embeddings and query them without running database servers themselves, from a free starter tier to enterprise deployments in their own cloud.

Key functions

• Dense, sparse and full-text indexes for semantic and hybrid search
• Integrated inference creates embeddings and reranking at write and query time
• Pinecone Assistant answers questions about uploaded documents with citations

Compared with Hugging Face Inference Endpoints

• Has a free plan; Hugging Face Inference Endpoints does not.
• Can store data in the EU; Hugging Face Inference Endpoints states no EU option.
• Strong in the same markets as Hugging Face Inference Endpoints: United States.

Check before buying: Choose a paid plan if data must stay in the EU, since the free Starter plan only runs in the US. Check which plan includes SAML single sign-on, audit logs and private endpoints. Model the monthly minimum and usage fees, and request the BAA if you store health data.

Fireworks AI runs open models for developers and enterprises, from serverless per-token APIs to dedicated GPU deployments, and offers supervised and reinforcement fine-tuning on the same platform.

Key functions

• Serverless models billed per token for text, vision, audio and embeddings
• Supervised and reinforcement fine-tuning for models up to more than 1T parameters
• On-demand deployments on dedicated GPUs with autoscaling

Compared with Hugging Face Inference Endpoints

• Offers a free trial; we found none for Hugging Face Inference Endpoints.
• Starts higher: from $8 usage-based, Hugging Face Inference Endpoints from $0.03 usage-based.
• Strong in the same markets as Hugging Face Inference Endpoints: United States.

Check before buying: Servers are located in the United States, so check the transfer basis for EU personal data; standard contractual clauses are available on request. Note that the Responses API stores conversations for 30 days by default. GPU deployments cost $8.00 or more per hour, so compare against serverless usage.

NVIDIA NIM is a set of prebuilt inference microservices that package AI models, optimized engines and standard APIs in containers. Platform teams use it to self-host models on NVIDIA GPUs in the cloud, in their data center or on workstations.

Key functions

• Prebuilt inference containers for language models and models for speech, vision and retrieval
• Selects an optimized engine such as TensorRT-LLM, vLLM or SGLang for the available GPU
• OpenAI-compatible API for self-hosted endpoints

Compared with Hugging Face Inference Endpoints

• Has a free plan; Hugging Face Inference Endpoints does not.
• Strong in the same markets as Hugging Face Inference Endpoints: United States.

Check before buying: Plan the license path early: the Developer Program covers research, development and testing, while production use requires NVIDIA AI Enterprise, priced through NVIDIA or its partners. Check that your GPUs support the models you need and that your clusters can pull images from NVIDIA's registry. Self-hosted NIM keeps data inside your environment; for the hosted endpoints, review NVIDIA's terms before sending real data.

Together AI is a cloud platform for running and adapting open AI models. Development teams call models through serverless APIs, fine-tune them or rent dedicated GPUs and clusters.

Key functions

• Serverless inference for open models, billed per million tokens
• Fine-tuning of open models via supervised fine-tuning or DPO
• Dedicated endpoints and GPU clusters for custom models

Compared with Hugging Face Inference Endpoints

• Starts higher: from $0.09 usage-based, Hugging Face Inference Endpoints from $0.03 usage-based.
• Strong in the same markets as Hugging Face Inference Endpoints: United States.

Check before buying: Check where requests are processed, since the privacy policy names no data center locations and relies on standard contractual clauses for EU data. Activate Zero Data Retention if prompts contain personal data. Token costs scale with usage, so set budget alerts. A data processing agreement is not linked publicly and should be requested.

Anomalib is an open source Python library for visual anomaly detection. Machine learning engineers and quality teams use it to train, benchmark and deploy models that find defects in images and video without large sets of labeled defect examples.

Key functions

• Ready implementations of anomaly detection models such as PatchCore, PaDiM, STFPM and EfficientAd
• Detects and localizes anomalies in images and video
• Exports trained models to OpenVINO for fast inference on Intel hardware

Compared with Hugging Face Inference Endpoints

• Has a free plan; Hugging Face Inference Endpoints does not.
• Strong in the same markets as Hugging Face Inference Endpoints: United States.

Check before buying: Anomalib is a library, not a finished inspection system: you need Python and machine learning skills, camera setup, labeled test data and your own operation and maintenance. There is no vendor support contract; plan who updates versions and validates models. Deployment is optimized for Intel hardware via OpenVINO.