Best Hugging Face Inference Alternatives for Indie Hackers in 2026

Nvidia agreed to buy Hugging Face on September 3, 2026, for $12.93 billion. Jensen Huang said the platform stays open and that Nvidia compute won't be required to build or deploy on it. Clem Delangue said Nvidia committed to keeping it open, independent and compute agnostic. Take them at their word, because the acquisition isn't really the reason to go looking.

The reason is the bill. Dedicated Inference Endpoints have always been the expensive part of Hugging Face, and a $12.93 billion deal is a good excuse to actually read your invoice. An H100 endpoint on the Hugging Face GCP tier costs $10.00 an hour. RunPod rents the same card for $1.99. That gap existed in August. Nobody was looking.

Which Hugging Face Product Are You Actually Replacing?

Three different products share one name, and most "alternatives" lists mash them together.

Inference Providers is a router. Your request goes through Hugging Face to Together, Fireworks, Replicate or another partner, and Hugging Face bills you the provider's own rate with no markup added. That's their documented policy, not a marketing line. Free accounts get $0.10 of credit a month, PRO at $9 gets $2, Team and Enterprise seats get $2 each. If this is what you use, price is not your problem and you can stop reading.

Inference Endpoints is the dedicated product. Your model, your GPU, billed per minute at Hugging Face's rates. This is where the money goes.

Spaces is for demos. Free on CPU basic and ZeroGPU, $0.40 an hour for a small T4 if you upgrade.

Everything below is about replacing the middle one.

Quick Verdict

Hugging Face for comparison: $2.50 an hour for an A100 on AWS, $3.60 on GCP, $5.00 for an H200, $10.00 for an H100.

RunPod

The cheapest way to stop paying platform rates. An A100 PCIe is $1.19 an hour on Community Cloud and $1.39 on Secure Cloud. H100 PCIe is $1.99 and $2.89. An RTX 4090 at $0.34 an hour will serve a 7B model perfectly well, which is the config most side projects actually need.

Community Cloud is hardware rented from third parties, so treat it as spot capacity rather than something you'd put a paying customer's checkout flow behind. Serverless exists at $4.79 an hour for an H100 if you want autoscaling without managing pods.

Who should skip it: anyone who wants a platform to handle deploys, versioning and observability. RunPod hands you a machine. The rest is yours.

Modal

Modal charges per second and boots containers in about one second. That combination is what makes scale to zero work in practice rather than just exist on a pricing page. Memory snapshots let a warmed container skip its initialization on later boots, which matters when your model weights take 40 seconds to load.

An A100 80GB works out to $2.50 an hour, identical to Hugging Face on AWS. H100 SXM5 lands at $3.95, L40S at $1.95, T4 at $0.59. The Starter plan includes $30 of free credit a month, which covers a lot of a hobby project. Team is $250 a month before compute.

Who should skip it: anyone who wants a UI. Modal is a Python SDK and you define infrastructure in code. If that sounds like work rather than relief, look elsewhere.

Replicate

Replicate is the fastest path from idea to working endpoint, and it has been for three years. Push a model with Cog, get an API. Public models bill by run time, so a text-to-image call costs $0.025 to $0.09 an image and you pay nothing when idle.

Hardware is pricier than the rest. T4 at $0.81 an hour, L40S at $3.51, A100 80GB at $5.04, H100 at $5.49. You're paying for the packaging, and for a lot of projects that's a fair trade.

Who should skip it: anyone running steady traffic. At consistent load, Replicate's A100 costs twice DeepInfra's and four times RunPod's. Prototype here, then move.

DeepInfra

The best managed price on the list. A100 at $0.89 an hour, H100 at $2.20, H200 at $2.69. Per-token rates are aggressive too, with DeepSeek V4 Flash at $0.09 in and $0.18 out per million tokens, and Llama 3.3 70B Turbo at $0.10 and $0.32. A Flex tier drops to 0.8x base for non-production work.

So why isn't everyone here? Smaller model catalog, thinner tooling, and a support story that assumes you can debug your own deployment.

Who should skip it: teams who need a specific fine-tuned architecture or an SLA with a name on it.

Together AI

Together is the per-token option when you want open models without running anything. Qwen3.8 Flash at $0.15 and $0.47 per million tokens, DeepSeek V4 Flash at $0.14 and $0.28, Llama 3.3 70B at $1.04 flat. Dedicated HGX H100 endpoints are $3.99 an hour on demand if you outgrow serverless.

It pairs well with a gateway in front. If you're already routing through one, adding Together as a fallback provider takes an afternoon. Our LiteLLM, Portkey and Cloudflare AI Gateway comparison covers that layer, and the OpenRouter alternatives roundup covers the routing question directly.

Who should skip it: anyone deploying a custom model. Together's catalog is the product.

Baseten

The most polished operator experience here. Per-minute billing, real autoscaling, and deployment tooling that a team can hand between people without a handover doc. T4 at $0.63 an hour, A10G at $1.21, A100 80GB at $4.00, H100 at $6.50.

That H100 rate is over three times RunPod's. You're buying support and a control plane, and for a funded team shipping a product that's defensible. For one person with an idea, it isn't.

Who should skip it: solo builders watching every dollar. This is the enterprise-shaped choice on the list.

How Should You Choose?

Answer one question first. Do you have steady traffic?

If no, use per-token serverless. DeepInfra or Together, pay nothing when idle, revisit in three months. Most side projects never leave this box, and the ones that do can afford the migration.

If yes, and you can operate a machine, RunPod. The saving against Hugging Face is real money at any meaningful uptime, roughly $5,800 a year on a single H100 running half the day.

If yes, but you'd rather write Python than manage pods, Modal. It costs more than RunPod and less than everything else, and the one second boot is worth paying for.

Watch the meter either way. Idle GPUs have bankrupted more side projects than bad code, and runaway agent bills are the same lesson in a different jacket.

The Pick

For most indie hackers reading this: DeepInfra for per-token, RunPod when you need your own GPU. That covers the real range of what a solo builder ships, at prices that don't punish you for succeeding.

Modal is the one I'd actually reach for personally, because per-second billing and a one second boot remove the thing that makes self-managed inference annoying. It's the middle option that behaves like the expensive one.

And if you're only using Inference Providers, stay. No markup means no problem, whoever owns the company. If you're running models on your own hardware instead, the local AI tooling roundup is the other half of this decision.