Hugging Face Inference Endpoints Alternatives: 10 Self-Hosted GPU Cloud Options for Production LLM Inference (2026)

Hugging Face Inference Endpoints is a managed serving product that runs TGI (Text Generation Inference) under the hood on dedicated NVIDIA GPUs. It is distinct from the Serverless Inference API: instead of shared resources billed per request, Endpoints provision a dedicated GPU for your model and charge by the hour while the endpoint is running. You can pause a paused endpoint to stop billing, but there is no spot pricing, no per-second billing, and no burst capacity.

The value prop is real for small teams and prototyping. Pick a model from the Hub, choose a GPU tier, hit deploy, and you have a private HTTPS endpoint in a few minutes. No Docker knowledge required, no infrastructure management, no multi-cloud config. HuggingFace handles model loading, health checks, and automatic restarts.

The cost cliff shows up fast. HF Inference Endpoints dedicated H100 pricing runs approximately $6.40-8.00/hr depending on plan and cloud provider. The same H100 PCIe hardware on Spheron starts from $2.01/hr on-demand. For a 24/7 production endpoint, that difference is $3,000-5,000/month for identical hardware running identical inference software. At H200, HF Endpoints charges approximately $5/hr on AWS. For teams running public-facing APIs or internal tools with steady traffic, those savings are hard to ignore.

This post covers 10 alternatives across the full spectrum: from fully managed serverless (no infrastructure) to bare-metal GPU cloud (full control, lowest cost). Each section includes pricing, what works, what does not, and who should use it. If you are specifically considering a move off HF Endpoints, the migration playbook near the end shows the exact steps to take your TGI config and run it yourself.

Why Teams Move Off HF Inference Endpoints

Cost ceiling at scale

HF Inference Endpoints uses AWS and GCP as the underlying cloud. You are paying for managed infrastructure on top of hyperscaler pricing. The markup is real:

The cost difference compounds at scale. A team running a 70B model endpoint 24/7 on H100 pays roughly $4,608-5,760/month on HF Endpoints vs $1,447/month on Spheron H100 PCIe. That is $38,000-52,000 in annual savings for the same inference stack.

No spot pricing

HF Inference Endpoints has no spot or preemptible option. Every dedicated endpoint bills at the full on-demand rate. Most GPU clouds, including Spheron, offer spot instances at 50-70% discounts over on-demand pricing. For batch inference workloads that tolerate interruption, spot access alone can cut your bill by more than half.

Hardware and region limits

HF Endpoints provides a fixed set of GPU tiers: T4, A10G, A100, H100, H200, on a small list of AWS and GCP regions. You cannot rent an L40S, RTX 4090, RTX 5090, or a bare H200 SXM5. If the GPU your model performs best on is not in HF's tier list, you are out of luck.

Inference engine lock-in

HF Endpoints runs TGI. You cannot swap to vLLM, SGLang, or a custom serving container with arbitrary runtime flags. If your model works better with vLLM's PagedAttention at high concurrency, or SGLang's RadixAttention for multi-turn agent workloads, HF Endpoints cannot accommodate that. You adapt your needs to their runtime, not the other way around.

Quick Comparison Table

1. Spheron: Bare-Metal GPU at Fraction of HF Endpoints Cost

A100 80G SXM4: from $1.70/hr | H100 PCIe: from $2.01/hr | H100 SXM5: from $4.34/hr | H200 SXM5: from $1.19/hr spot

Spheron runs bare-metal and virtual GPU instances from vetted data center partners globally. No hyperscaler markup, no managed-service overhead. You provision an instance, SSH in, and run the exact same TGI Docker image that HF Endpoints runs under the hood. Same inference engine, same API surface, different price tag.

The practical difference for a team moving off HF Endpoints: a Spheron H100 PCIe instance at $2.01/hr replaces an HF Endpoint costing roughly $6.40-8.00/hr. For a model that needs to run 24/7, that saves $100-140/day. For teams running multiple endpoints across different model sizes, the savings stack up.

Spheron also supports inference frameworks HF Endpoints cannot: vLLM for high-throughput continuous batching, SGLang for multi-turn agent workloads, Triton Inference Server for model ensembles. If you have been constrained to TGI because HF Endpoints only offers TGI, you can evaluate alternatives once you are on bare metal.

What Spheron does well

• H100 PCIe pricing 68-75% below HF Endpoints ($2.01/hr vs HF's $6.40-8.00/hr); savings vary by GPU tier
• Same TGI Docker image runs without modification
• Supports vLLM, SGLang, Triton, or any CUDA-compatible inference stack
• Per-minute billing, no minimum commitment, no contracts
• Spot instances available for batch inference at significant discounts
• Full root SSH access and custom driver/CUDA toolkit version support

Where it falls short

• No one-click model deployment from Hub; you provision and configure the inference stack yourself
• Requires Docker and command-line familiarity to deploy TGI or vLLM
• No managed auto-scaling or health monitoring out of the box (pair with a load balancer if needed)

Best for: Teams spending $2,000+/month on HF Inference Endpoints who want to cut GPU costs significantly by running the same TGI stack on cheaper bare-metal hardware. The migration takes under an hour if you already know Docker.

2. RunPod: Flexible GPU Marketplace with Serverless Option

RunPod Pods H100: ~$3.99-4.99/hr | RunPod Serverless: per-second with cold starts

RunPod offers two distinct products. RunPod Pods are always-on GPU instances: you pick a machine, it runs until you shut it down, billed per-second. RunPod Serverless is a per-request auto-scaling service where containers sleep between requests and cold-start on demand.

For replacing HF Endpoints, RunPod Pods is the closer analogue. You get always-on GPU access at per-second granularity. Pricing for H100s sits higher than Spheron but still well below HF Endpoints. The serverless product introduces cold starts of 10-30 seconds for large model loads, which is acceptable for batch workloads but problematic for user-facing APIs where p99 latency matters. For a full alternative comparison, see the RunPod alternatives guide.

What RunPod does well

• Large GPU selection including H100, A100, RTX 4090, and more exotic SKUs
• Serverless option for teams with bursty, unpredictable traffic
• Active community with many shared templates for inference stacks
• Competitive pod pricing vs HF Endpoints

Where it falls short

• H100 pod pricing still 50-100% above Spheron for equivalent hardware
• Serverless cold starts make it unsuitable for latency-sensitive endpoints
• Serverless format requires wrapping inference in RunPod's handler function format
• No spot-equivalent pricing for pods at HF Endpoints' always-on billing model

Best for: Teams with burst traffic patterns who want serverless auto-scaling, or developers comfortable with RunPod's ecosystem who want a step down from HF Endpoints pricing.

3. Together AI: Zero-Infrastructure per-Token Serving

Llama 3.3 70B: $0.88/1M output tokens | Managed endpoints: $3.49/hr for H100

Together AI is the furthest from infrastructure management on this list. You call an OpenAI-compatible API, pay per token, and Together AI handles everything else. For teams migrating from HF Endpoints because they want less infrastructure management, not more, Together AI fits that direction.

The per-token model makes sense at low-to-moderate token volumes. The crossover where a dedicated GPU becomes cheaper depends on the model, but for Llama 3.3 70B at $0.88/1M output tokens, a Spheron H100 PCIe at $2.01/hr becomes cheaper at roughly 55M output tokens/day without counting prompt tokens, or 14M output tokens/day when factoring in a 3:1 prompt-to-completion ratio. For teams well below those volumes, Together AI is simpler and cheaper. For a detailed breakdown, see the Together AI alternatives guide.

What Together AI does well

• Zero infrastructure management, immediate access to 200+ models
• OpenAI-compatible API for drop-in integration
• No cold starts; managed infrastructure maintains model warm state
• Per-token billing keeps costs low at low request volumes

Where it falls short

• Per-token billing becomes expensive above moderate daily token volumes
• No custom model loading; must use their hosted model catalog
• No bare-metal access for custom CUDA extensions or non-standard serving code
• Shared API means rate limits and latency variance at peak hours

Best for: Teams with low-to-moderate token volumes who want zero infrastructure management and OpenAI API compatibility without the cost overhead of a dedicated GPU.

4. Modal Labs: Python-Native Serverless with GPU Functions

H100 billing: ~$4.00/hr (per-second, pay per actual use) | Cold starts: 15-60s for large models

Modal is a Python-native serverless compute platform. You decorate Python functions with @app.function(gpu="H100") and Modal handles container building, GPU scheduling, and auto-scaling. The developer experience is excellent. But the underlying economics have the same cold-start problem as RunPod Serverless: large model loads take time, and every new container instance pays that startup cost.

Modal works well for batch inference pipelines, data processing, and endpoints with sufficient traffic that containers stay warm. For a low-traffic production endpoint where cost efficiency matters more than developer experience, Modal can end up more expensive than HF Endpoints at similar traffic volumes. For extended comparisons, see the Modal alternatives guide.

What Modal does well

• Best developer experience in the GPU serverless space
• Python-native with no Docker or Kubernetes knowledge required
• Per-second billing means you only pay for actual inference time
• Scales to zero between requests, excellent for sporadic workloads

Where it falls short

• Cold starts of 15-60 seconds for large 70B+ models are unavoidable
• Pricing can exceed dedicated GPU costs for sustained high-throughput workloads
• Less hardware flexibility than IaaS providers; no bare-metal access
• Lock-in to Modal's Python runtime and deployment framework

Best for: ML engineers building inference pipelines and APIs who want serverless convenience, can tolerate cold starts, and prefer Python-native deployment over infrastructure management.

5. Replicate: Pre-Built Model API with Cog Packaging

Billing: per-second of GPU compute | Cold starts: yes | Models: large community catalog

Replicate hosts a large catalog of pre-packaged models and lets you run them via API. It packages models using Cog, their open-source containerization tool. For teams using Replicate's existing model library, the migration from HF Endpoints is lateral: you are still calling an API, paying per invocation, dealing with cold starts.

Custom model deployment on Replicate requires Cog packaging. That is additional wrapping work compared to the TGI-compatible approach HF Endpoints uses. At scale, Replicate's per-second billing accumulates to costs higher than bare-metal GPU alternatives. For a full breakdown, see the Replicate alternatives guide.

What Replicate does well

• Massive catalog of pre-packaged community models ready to call
• Simple API for teams without inference infrastructure experience
• Cog is open-source and portable if you later want to self-host
• Pay-per-use pricing with no idle infrastructure cost

Where it falls short

• Custom model deployment requires Cog packaging overhead
• Per-second billing at high volume exceeds dedicated GPU pricing
• Cold starts for large models affect latency-sensitive use cases
• Less control over inference configuration than TGI or vLLM directly

Best for: Teams using community models from Replicate's catalog at low-to-moderate request volumes, or prototyping pipelines before committing to production infrastructure.

6. Baseten: Production ML APIs with Managed Infrastructure

Pricing: custom (contact sales) | Billing: per-second | Scale-to-zero: configurable

Baseten sits between fully managed and fully self-hosted. You package your model using their Truss framework (or use their vLLM and TensorRT-LLM integrations), Baseten provisions and manages the infrastructure, and you get a production API with auto-scaling, monitoring, and version management. More DevOps overhead than HF Endpoints, more flexibility.

The Truss packaging step adds initial setup work. But Baseten's support for vLLM and TensorRT-LLM as inference backends means you are not locked to TGI. If your model's throughput profile benefits from a different engine, Baseten can accommodate that. Pricing is custom and not publicly listed.

What Baseten does well

• Supports vLLM, TensorRT-LLM, and Triton as backends (not just TGI)
• Scale-to-zero configurable to avoid idle costs
• Production-grade monitoring and observability out of the box
• Truss framework is open-source and portable

Where it falls short

• No public pricing; requires sales conversation for quotes
• Truss packaging adds setup complexity vs HF Endpoints one-click deploy
• Vendor lock-in to Baseten's deployment and serving framework
• Less cost-transparent than providers with public per-hour pricing

Best for: Teams that need a production ML API with managed infrastructure, want flexibility in inference engine choice, and have engineering resources to handle initial packaging setup.

7. Fireworks AI: Fast Open Model Inference with per-Token Billing

Billing: per-token | Cold starts: none (managed warm inference) | OpenAI-compatible API

Fireworks AI offers managed inference for open-source models with low latency and OpenAI-compatible endpoints. Per-token pricing on popular models, with fast time-to-first-token due to their inference optimization stack. For teams switching from HF Endpoints because they want a simpler managed API, Fireworks AI fits.

The per-token model works at low volume. At high sustained throughput, the same economics as Together AI apply: dedicated GPU compute becomes cheaper above a daily token volume threshold. Fireworks AI also does not offer custom model deployment in the same way; you work with models from their catalog.

What Fireworks AI does well

• Low-latency managed inference with OpenAI API compatibility
• No cold starts; infrastructure maintains model warm state
• Competitive per-token pricing on popular open-source models
• No infrastructure management required

Where it falls short

• Per-token billing becomes expensive at high daily token volumes
• Custom or fine-tuned models have limited deployment options
• No bare-metal access or inference engine configurability
• Catalog limited to models they have optimized; arbitrary Hub model loading not supported

Best for: Teams with low-to-moderate, bursty inference needs on popular open-source models who want fast, managed API access without dedicated GPU overhead.

8. Anyscale: RayServe-Based Managed Inference

Pricing: custom (contact sales) | Backend: vLLM on RayServe | Enterprise SLAs available

Anyscale is the managed Ray platform, built by the team that created the Ray distributed computing framework. Anyscale Endpoints runs LLM inference on RayServe with vLLM as the inference backend. For teams already using Ray for distributed training or data pipelines, Anyscale offers a unified platform that covers both.

The setup complexity is higher than HF Endpoints. Ray has a significant learning curve, and Anyscale's managed offering assumes some familiarity with the Ray ecosystem. Pricing is not publicly listed. Best evaluated if your organization is already a Ray user.

What Anyscale does well

• vLLM backend with RayServe provides high-throughput production inference
• Unified platform for training and serving if you use Ray across your stack
• Enterprise SLAs and support contracts available
• Per-token billing at scale for managed inference

Where it falls short

• Steep learning curve if you are not already familiar with Ray
• Pricing not publicly listed; requires sales engagement
• Overkill for teams that only need inference serving without distributed compute
• Higher operational complexity than pure inference-focused alternatives

Best for: Teams already using Ray for distributed ML training who want to extend the same infrastructure to serve production LLM endpoints.

9. AWS SageMaker JumpStart: Pre-Packaged HF Model Deployment on AWS

ml.p4d.24xl (8x A100 40G): ~$32+/hr | ml.g5.12xlarge (4x A10G): ~$5.67/hr | Per-minute billing

AWS SageMaker JumpStart provides pre-packaged model deployments for popular Foundation Models including many HuggingFace models. It is the closest AWS-native analogue to HF Inference Endpoints: pick a model, choose an instance type, deploy. SageMaker handles the serving infrastructure using TGI, vLLM, or DJL (DeepJava Library) as the backend depending on the model.

The catch is AWS pricing. SageMaker inference instance rates are hyperscaler-priced. An ml.p4d.24xl with 8x A100 40G runs above $32/hr on-demand. By comparison, an 8x A100 80G node on Spheron costs significantly less for more VRAM per GPU. For AWS-locked organizations with existing SageMaker usage, reserved instances or SageMaker Savings Plans can reduce costs 30-60%.

Do not confuse SageMaker Inference (custom container deployment) with JumpStart (pre-packaged model deployments). JumpStart is the managed product that most closely mirrors the HF Endpoints experience.

What SageMaker JumpStart does well

• Deep integration with AWS ecosystem: VPC, IAM, S3, CloudWatch, SageMaker Pipelines
• Pre-packaged deployments for popular Foundation Models with minimal configuration
• SageMaker Savings Plans provide significant discounts for committed usage
• SOC 2, HIPAA, FedRAMP compliance for regulated industries

Where it falls short

• On-demand pricing is significantly higher than GPU cloud alternatives
• Not a good fit for teams without AWS infrastructure
• Limited GPU selection compared to specialized GPU clouds
• Setup complexity higher than HF Endpoints; SageMaker has its own IAM, role, and permission model

Best for: Organizations already heavily invested in AWS that need to keep inference within their existing AWS VPC for compliance, data residency, or internal tooling integration reasons.

10. Vast.ai: Community GPU Marketplace

H100: from ~$1.50-2.00/hr (varies) | A100 80G: ~$0.80-1.20/hr (varies) | Per-hour billing

Vast.ai is a GPU marketplace where independent hosts list hardware and renters bid on capacity. Pricing is variable and demand-driven: on a slow day you can find H100s under $1.60/hr; on a busy day the same GPU might cost $3.00+/hr. No guaranteed availability, no SLAs, variable hardware quality.

For dev and test workloads, Vast.ai is hard to beat on cost. Run your TGI container, test inference config, validate your migration plan, and shut down. For production endpoints where availability and latency guarantees matter, the marketplace model introduces too much uncertainty.

What Vast.ai does well

• Lowest available prices when supply is high
• Large GPU selection including consumer cards (RTX 3090, RTX 4090) for smaller models
• Full Docker execution; any inference image runs without modification
• Good for batch inference and validation workloads where interruption is acceptable

Where it falls short

• No guaranteed availability or uptime SLAs
• Hardware quality varies significantly between hosts
• Networking performance is inconsistent
• Security concerns with third-party hosted hardware for sensitive model weights
• Not suitable for production APIs requiring consistent latency

Best for: Individual researchers, small teams, and cost-sensitive batch inference workloads where availability guarantees are not required and cost is the primary constraint.

Cost Comparison: Per Million Tokens

This table estimates cost per million output tokens for two model sizes across all 10 platforms. For self-hosted providers, costs are derived from GPU hourly rates and representative throughput estimates: vLLM on H100 SXM5 achieves roughly 4,000-6,000 tokens/sec for 70B with tensor parallelism at moderate concurrency; 7-8B models push 8,000-15,000 tokens/sec on a single H100. For managed providers, listed per-token rates are used directly.

For the full methodology and cross-GPU benchmarks, see GPU cost-per-token benchmarks.

Llama 3.3 70B (2x H100 SXM5 for tensor parallelism)

Qwen 2.5 7B (single H100 PCIe)

Pricing fluctuates based on GPU availability. The prices above are based on 05 May 2026 and may have changed. Check current GPU pricing → for live rates.

When Does Managed Inference Make Sense?

Before switching everything to bare metal, be honest about your team's capacity and traffic patterns.

Managed is fine when:

• You are prototyping or in early development; OpEx savings do not outweigh engineering time
• Daily token volume is below the crossover point where dedicated GPU costs less than per-token billing
• Your models are all publicly available HF Hub models; no proprietary weights to worry about
• You have no DevOps budget; an extra $2,000/month to HF Endpoints is cheaper than a part-time DevOps hire
• Your team has no Docker or Linux administration experience

Self-hosted wins when:

• Daily token volume exceeds 50M+ output tokens (exact crossover depends on model and provider)
• You need spot pricing for batch inference; managed services rarely offer true spot
• Your inference stack benefits from vLLM or SGLang over TGI
• You are running proprietary fine-tuned models that should not leave your own infrastructure
• Multi-GPU tensor parallelism configurations matter; you want control over how the model is sharded

Hybrid approach: Run HF Endpoints or Together AI for development and staging environments where volume is low. Move production traffic to bare-metal GPU cloud once your model and serving config are validated. The dev/staging cost on managed is low; the savings on production are significant.

For a broader framework on when to self-host vs. use managed inference, see when to self-host LLM inference vs use a managed API.

Migration Playbook: HF Inference Endpoints to Self-Hosted TGI on Spheron

HF Endpoints runs TGI. Moving to self-hosted TGI on Spheron means running the same container with the same flags, on hardware you control. The full step-by-step deployment guide is in the TGI production deployment guide on Spheron, but here are the five steps:

Step 1: Export your HF Endpoints config

Before deleting the endpoint, document: the HF model ID, GPU tier, any environment variables set in the endpoint config (MAX_INPUT_LENGTH, MAX_TOTAL_TOKENS, HUGGING_FACE_HUB_TOKEN), and the number of GPU replicas.

Step 2: Pull the TGI Docker image

docker pull ghcr.io/huggingface/text-generation-inference:latest

HF Endpoints uses this same image. No conversion needed.

Step 3: Translate Endpoints config to TGI CLI flags

Step 4: Provision a GPU on Spheron and deploy

Provision an A100 or H100 instance on Spheron (select based on model VRAM requirements), SSH in, and run:

docker run --gpus all --shm-size 1g \
  -p 8080:80 \
  -e HUGGING_FACE_HUB_TOKEN=$HF_TOKEN \
  ghcr.io/huggingface/text-generation-inference:latest \
  --model-id $MODEL_ID \
  --max-input-length 4096 \
  --max-total-tokens 8192 \
  --max-batch-prefill-tokens 4096

For multi-GPU tensor parallelism, add --num-shard N and --ipc=host to the Docker run command.

Step 5: Test parity

TGI exposes an OpenAI-compatible endpoint at /v1/chat/completions. Change your application's base_url from the HF Endpoints URL to http://your-instance-ip:8080. The request format is identical. Validate with a curl test before cutting over production traffic.

For teams also considering vLLM as a TGI alternative, vLLM's OpenAI-compatible server uses the same /v1/chat/completions endpoint format and is often the better choice for high-concurrency production workloads.

The Bottom Line

HF Inference Endpoints is a clean, managed product for teams that want zero infrastructure operations. It makes sense to pay the premium when you are early in development or when the engineering time saved outweighs the cost delta. It stops making sense when the bill gets large and your serving config is stable.

Decision matrix:

• Best for cost-conscious production inference: Spheron bare-metal with vLLM or TGI. Same hardware, same inference engine, fraction of the cost. A100 GPU rental on Spheron starts from $1.70/hr; H100 PCIe from $2.01/hr.
• Best for zero-DevOps teams: Together AI or Fireworks AI if volume is moderate; HF Endpoints if you need Hub model access and do not want to leave the HF ecosystem.
• Best for burst workloads: Modal Labs or RunPod Serverless. Cold starts are the tradeoff.
• Best for AWS-locked teams: SageMaker JumpStart, ideally with Savings Plans to offset the premium.
• Best for dev/test budget: Vast.ai for non-critical workloads where you can tolerate variability.
• Best for existing Replicate users: Replicate is fine at low traffic; move off at scale.

If you are running a production endpoint at meaningful token volumes and paying HF Endpoints prices, the migration to self-hosted TGI or vLLM on bare-metal GPU cloud is the highest-ROI infrastructure move available. The inference engine, the model weights, and the API surface stay identical. Only the billing and the hardware owner change.

If your Hugging Face Inference Endpoints bill is growing, the most cost-effective path is bare-metal GPU access with the same TGI or vLLM engine you're already running under the hood. Spheron H100 starts from $2.01/hr with no per-token markup and no lock-in.

Rent H100 → | Rent A100 → | View all GPU pricing →

Get started on Spheron →