MemetikEdition 2026-09

Lists / AI infra

Best open-source model hosting platforms for developers (2026): What ChatGPT, Claude & Gemini Recommend

Hugging Face, Together and Fireworks lead 18 inference hosts named across 50 recorded AI answers. A ranked shortlist with counts, model splits and who each suits.

Hugging Face is the name AI models give most often when a developer asks where to host and serve open-source models. It is named in 48 of 50 recorded answers and first in 17. Together is named in 45 and opens more answers, 19. Fireworks, Baseten, Modal and RunPod follow.

This page counts which platforms ten AI models name. It does not test the products.

TL;DR

1. Hugging Face

Pick Hugging Face if the model you want to serve already sits on the Hugging Face Hub and you want a managed endpoint without running GPUs yourself.

Measured: named 48 of 50 (Hugging Face 96%), first 17 of 50 (Hugging Face 34%), average position 3.1.

Hugging Face is the one name nearly every answer includes. The answers describe it as a deployment route: take a model repository from the Hub, pick an engine such as vLLM or TGI, and get a managed endpoint that can scale to zero. That framing is its strength and its limit. It leads whenever the question is about hosting a model. It is not the first name when the question is about buying cheap tokens. The only two answers that left it out were the fast, cheap replies from GPT-5.6 Sol and GPT-5.6 Terra. Together also opens more answers than it does, which matters if you read AI advice top-down.

Pros

Cons

Best for: serving a model that already lives on the Hugging Face Hub.

2. Together

Pick Together if you want to call popular open-weight models through an API now and keep the option of dedicated capacity later.

Measured: named 45 of 50 (Together 90%), first 19 of 50 (Together 38%), average position 1.78.

Together is the name the models reach for first. It opens more answers than any other vendor and holds the lowest average position in the table. The Claude models explain much of that. Claude Opus 5, Claude Sonnet 5 and Claude Fable 5 name it in every answer, and they describe it as a generalist that spans serverless, batch and dedicated inference plus fine-tuning. Claude Opus 5 made the practical point: a team can start with API calls and move to dedicated deployments without changing provider. The OpenAI models are cooler. GPT-5.6 Terra and GPT-5.6 Luna name it in three of five answers each.

Pros

Cons

Best for: calling popular open-weight models by API, with a path to dedicated capacity.

3. Fireworks

Pick Fireworks if speed and token cost on a popular open model decide your purchase, because that is the question where GPT-5.6 Sol and GPT-5.6 Terra put it first.

Measured: named 43 of 50 (Fireworks 86%), first 4 of 50 (Fireworks 8%), average position 2.56.

Fireworks has the widest gap in the category between being named and being named first: 78 points. The models treat it as the standard second name beside Together. The Claude answers call it the close alternative for teams that want an API experience without managing GPUs. It moves to the top when the question names price and speed. GPT-5.6 Sol and GPT-5.6 Terra both opened their fast, cheap answers with it, and those answers cited Fireworks’ own serverless and pricing documentation. Its site is the second most-cited vendor-owned host in the run. Some answers build their case for Fireworks from Fireworks’ own pages.

Pros

Cons

Best for: fast, low-cost API inference on popular open models.

4. Baseten

Pick Baseten if you are serving your own custom or fine-tuned model in production and want a managed endpoint built for that.

Measured: named 36 of 50 (Baseten 72%), first 0 of 50 (Baseten 0%), average position 5.25.

Baseten is the clearest case of a name the models include but never lead with. The answers give it a specific job. Claude Fable 5 described it as “best when you want to deploy your own custom or fine-tuned model as a production endpoint (uses their Truss framework)”. GPT-5.6 Sol’s shortlist put it against custom models and production engineering. That role explains the position. A general question gets a general answer first, and Baseten arrives once the answer turns to custom deployments. If your model is a fine-tune rather than a catalogue model, its low first count says little about fit.

Pros

Cons

Best for: production endpoints for your own custom or fine-tuned models.

5. Modal

Pick Modal if your team writes Python and wants serverless GPUs for its own inference code rather than a catalogue of hosted models.

Measured: named 34 of 50 (Modal 68%), first 2 of 50 (Modal 4%), average position 5.62.

Modal splits the panel more sharply than any vendor above it. GPT-5.6 Sol names it in all five answers, which makes Modal that model’s leader. GPT-5.6 Luna and both Gemini models also name it every time. Sonar Pro never names it, and Sonar Reasoning Pro names it once. GPT-5.6 Luna made it the default in one answer and described defining GPUs, containers, dependencies and endpoints in Python, with scale-to-zero.

Pros

Cons

Best for: Python teams running custom inference code on serverless GPUs.

6. RunPod

Pick RunPod if you want raw GPU capacity to run your own serving stack and unit cost matters more than a managed API.

Measured: named 34 of 50 (RunPod 68%), first 0 of 50 (RunPod 0%), average position 7.18.

RunPod ties Modal on names and sits later in the answers. That fits the role the answers give it. GPT-5.6 Sol’s shortlist labels it the low-cost, infrastructure-oriented option, and GPT-5.6 Luna lists it among the clouds for running your own vLLM or SGLang deployment. In other words, RunPod appears after the managed APIs, as the do-it-yourself branch. The split by model is sharp. Both Gemini models and Sonar Pro name it in every answer. GPT-5.6 Terra never does. A developer who asks only GPT-5.6 Terra will not hear the name in this sample.

Pros

Cons

Best for: running your own serving stack on rented GPUs.

7. Groq

Pick Groq if latency is the constraint and the models you need are ones Groq serves.

Measured: named 32 of 50 (Groq 64%), first 5 of 50 (Groq 10%), average position 4.09.

Groq splits the panel by model family. Claude Opus 5, Claude Fable 5, Gemini 3.6 Flash and Sonar Reasoning Pro name it in every answer. Each OpenAI model names it once in five. The answers that do name it agree on the trade. Claude Opus 5 summed it up as “Groq for ultra-low latency but a narrower model selection”. That pairing is the buying decision. Speed is the reason to pick it, and catalogue breadth is the thing to check first. It also opens more answers than Fireworks does, from fewer names.

Pros

Cons

Best for: latency-sensitive inference on the models Groq serves.

8. Replicate

Pick Replicate if you want to try many models quickly or ship a demo, the role the OpenAI answers give it.

Measured: named 23 of 50 (Replicate 46%), first 0 of 50 (Replicate 0%), average position 5.43.

Replicate is the only vendor that every model names at least once and no model names every time. That spread reads as a stable secondary option, and the first count confirms it. GPT-5.6 Luna names it most often, in four of five answers, and its comparison answer gave Replicate the slot for quick demos and broad model experimentation. GPT-5.6 Sol cited Replicate’s guide to deploying a custom model. Claude Opus 5 and Sonar Reasoning Pro name it once each. OpenAI family share: Replicate 53.3%.

Pros

Cons

Best for: quick demos and trying many models.

9. Ollama

Pick Ollama if you mean to run open models on your own hardware, which is a different job from managed hosting.

Measured: named 21 of 50 (Ollama 42%), first 1 of 50 (Ollama 2%), average position 7.76.

Ollama is on this list because several answers treat self-hosting as one branch of the decision. Claude Sonnet 5 put it at the top of its local and self-hosted section. Sonar Reasoning Pro named it with vLLM as the route to full control on your own hardware. The OpenAI models barely use it. GPT-5.6 Sol and GPT-5.6 Terra never name it, and GPT-5.6 Luna names it once. Claude Opus 5 and Sonar Reasoning Pro name it in four of five answers. Its late average position matches that framing. It usually appears after the hosted options, once an answer turns to running models yourself.

Pros

Cons

Best for: running open models locally or on your own servers.

10. Amazon Bedrock

Pick Amazon Bedrock if your team is already standardised on AWS and wants open-weight models served inside that account.

Measured: named 19 of 50 (Amazon Bedrock 38%), first 0 of 50 (Amazon Bedrock 0%), average position 7.26.

Bedrock is a Claude-side name. Claude Fable 5 names it in every answer, and Claude Opus 5 and Claude Sonnet 5 in four of five. None of the three OpenAI models names it, and neither does Gemini 3.5 Flash. The Claude answers place it in a recurring shortlist of managed providers drawn from the guides they cite. Claude Opus 5 noted that AWS Bedrock serves open-weight models alongside proprietary ones. Claude Fable 5 set it against Together as the more AWS-native choice. That makes Bedrock a cloud-platform decision more than a model-hosting one.

Pros

Cons

Best for: teams already standardised on AWS.

11. SiliconFlow

Pick SiliconFlow only if price-to-performance on hosted open models decides the purchase, and check an independent source before you trust its ranking.

Measured: named 17 of 50 (SiliconFlow 34%), first 1 of 50 (SiliconFlow 2%), average position 6.

SiliconFlow’s own site is cited more than any other vendor-owned host in the run, 54 times. Only llmapi.ai and edenai.co are cited more. Claude Opus 5 flagged the pattern in its own reply: ‘many of the “best of 2026” listicles above are SEO content, sometimes published by the vendors they rank (SiliconFlow ranking SiliconFlow first, VisualWebTechnologies ranking itself)’. Sonar Reasoning Pro names it in four of five answers and credits it with the strongest price-to-performance for open models. Every OpenAI model leaves it out. The name is real in the answers. Its standing in the pages behind them is partly self-published.

Pros

Cons

Best for: price-led API inference, checked against independent sources.

12. Lambda

Pick Lambda if you want GPU cloud capacity for a serving stack you run yourself. The answers file it with self-managed infrastructure, not managed APIs.

Measured: named 14 of 50 (Lambda 28%), first 0 of 50 (Lambda 0%), average position 8.93.

Lambda appears late and in a narrow band of models. Claude Sonnet 5 names it in four of five answers. Claude Opus 5 and Gemini 3.5 Flash name it in three. Claude Fable 5, GPT-5.6 Terra, Sonar Pro and Sonar Reasoning Pro never name it. GPT-5.6 Luna placed it in a list of clouds for running your own vLLM or SGLang deployment at sustained scale. That placement explains its average position. It is an infrastructure name that arrives after the hosted services, once an answer has moved on to renting GPUs.

Pros

Cons

Best for: GPU capacity for a self-run serving stack.

13. DeepInfra

Consider DeepInfra as a secondary API to price against the leaders. The answers name it rarely but early.

Measured: named 8 of 50 (DeepInfra 16%), first 0 of 50 (DeepInfra 0%), average position 4.25.

DeepInfra is named in 8 answers, but it arrives early when it does appear. Its average position of 4.25 sits close to Groq’s rather than to the vendors around it in the table. Gemini 3.5 Flash names it in three of five answers and Claude Sonnet 5 in two. The count misses at least one answer. Claude Opus 5 listed it among the serverless inference APIs as “Deepinfra”, and the string-based count did not register that spelling. The true figure for this vendor is therefore a floor, not a ceiling.

Pros

Cons

14. Vast.ai

Treat Vast.ai as a late-list name that reaches you mainly through Gemini.

Measured: named 8 of 50 (Vast.ai 16%), first 0 of 50 (Vast.ai 0%), average position 9.13.

Vast.ai has the latest average position in the table. When it appears, it is near the end of a long answer. Its names come from three models: Gemini 3.6 Flash in four answers, Claude Sonnet 5 in three and Gemini 3.5 Flash in one. The OpenAI and Perplexity models never name it, and neither do Claude Opus 5 or Claude Fable 5. A buyer who hears about Vast.ai from an AI answer most likely heard it from Gemini. The count cannot say more than that about where it fits.

Pros

Cons

15. OpenRouter

Treat OpenRouter as an occasional Claude-side mention, not a shortlist entry, on this evidence.

Measured: named 6 of 50 (OpenRouter 12%), first 1 of 50 (OpenRouter 2%), average position 5.17.

OpenRouter is a thin name with one notable moment. It is named in 6 answers and, in one of them, it came before every other tracked vendor. Claude Opus 5 and Claude Fable 5 name it twice each. GPT-5.6 Sol and Gemini 3.5 Flash name it once each. No other model names it. Six mentions cannot carry a buying decision in either direction. What they do record is that some answers treat it as part of this category at all.

Pros

Cons

16. Cerebras

Keep Cerebras on a watch list rather than a shortlist. Two answers name it, both from Claude models.

Measured: named 2 of 50 (Cerebras 4%), first 0 of 50 (Cerebras 0%), average position 6.

Cerebras appears twice in 50 answers, once from Claude Opus 5 and once from Claude Fable 5. No OpenAI, Google or Perplexity model names it. Two mentions from one model family say more about how that family builds its lists than about Cerebras. They record that the name sits in the Claude models’ working set and nowhere else in this panel. A buyer who wants Cerebras on a comparison should add it by hand. The answers will not put it there for most people.

Pros

Cons

17. Anyscale

Anyscale has one mention and no basis for a shortlist place. Note it and move on.

Measured: named 1 of 50 (Anyscale 2%), first 0 of 50 (Anyscale 0%), average position 2.

Anyscale’s single mention came from Gemini 3.5 Flash. In that answer it sat second, which is why its average position of 2 looks strong. An average over one answer is just that answer’s position. It does not mean the models rate Anyscale near the top. No other model in the panel named it, so the table records presence at the edge of the category and nothing more. A developer who wants a managed option should start further up this list.

Pros

Cons

18. Vercel AI

Leave Vercel AI off an inference shortlist on this evidence. Its one mention may not refer to the AI product at all.

Measured: named 1 of 50 (Vercel AI 2%), first 0 of 50 (Vercel AI 0%), average position 6.

Vercel AI is tracked under two aliases: Vercel AI Gateway and plain Vercel. Its one mention came from Gemini 3.6 Flash. Because the plain name also matches Vercel’s general app-hosting product, a single hit cannot show whether the answer meant the AI gateway. One captured hosting guide describes Vercel and Netlify as frontend-only. That is the other reading of the name. Treat this row as noise until a later edition gives it more than one answer.

Pros

Cons

How the tools compare

Hugging Face leads on names and Together leads on first place. Below them the pattern is a long shelf of specialists that the models name often and almost never lead with. Baseten, Modal and RunPod are each named in at least 34 answers, and only Modal is ever first. The tail from Replicate down is carried by a few models each. No public pricing is recorded for any vendor in this edition’s record, so the pricing column reads Not recorded. The full record, with every answer, is on the inference hosting index.

Rank Vendor Named Answer share First First share Avg position Pricing model
1 Hugging Face 48 of 50 Hugging Face 96% 17 of 50 Hugging Face 34% 3.1 Not recorded
2 Together 45 of 50 Together 90% 19 of 50 Together 38% 1.78 Not recorded
3 Fireworks 43 of 50 Fireworks 86% 4 of 50 Fireworks 8% 2.56 Not recorded
4 Baseten 36 of 50 Baseten 72% 0 of 50 Baseten 0% 5.25 Not recorded
5 Modal 34 of 50 Modal 68% 2 of 50 Modal 4% 5.62 Not recorded
6 RunPod 34 of 50 RunPod 68% 0 of 50 RunPod 0% 7.18 Not recorded
7 Groq 32 of 50 Groq 64% 5 of 50 Groq 10% 4.09 Not recorded
8 Replicate 23 of 50 Replicate 46% 0 of 50 Replicate 0% 5.43 Not recorded
9 Ollama 21 of 50 Ollama 42% 1 of 50 Ollama 2% 7.76 Not recorded
10 Amazon Bedrock 19 of 50 Amazon Bedrock 38% 0 of 50 Amazon Bedrock 0% 7.26 Not recorded
11 SiliconFlow 17 of 50 SiliconFlow 34% 1 of 50 SiliconFlow 2% 6 Not recorded
12 Lambda 14 of 50 Lambda 28% 0 of 50 Lambda 0% 8.93 Not recorded
13 DeepInfra 8 of 50 DeepInfra 16% 0 of 50 DeepInfra 0% 4.25 Not recorded
14 Vast.ai 8 of 50 Vast.ai 16% 0 of 50 Vast.ai 0% 9.13 Not recorded
15 OpenRouter 6 of 50 OpenRouter 12% 1 of 50 OpenRouter 2% 5.17 Not recorded
16 Cerebras 2 of 50 Cerebras 4% 0 of 50 Cerebras 0% 6 Not recorded
17 Anyscale 1 of 50 Anyscale 2% 0 of 50 Anyscale 0% 2 Not recorded
18 Vercel AI 1 of 50 Vercel AI 2% 0 of 50 Vercel AI 0% 6 Not recorded

Answer share is the share of recorded answers that named the product. First share is the share where it appeared before any other tracked product. SambaNova was tracked and never named, so it has no row.

Where do the models disagree?

The models disagree on the leader, and the four model families do not. Nine models name Hugging Face more than any other vendor. GPT-5.6 Sol is the exception, with Modal in all five of its answers. At family level, Hugging Face leads OpenAI, Anthropic, Google and Perplexity alike.

Vendor GPT-5.6 Sol GPT-5.6 Terra GPT-5.6 Luna Claude Opus 5 Claude Sonnet 5 Claude Fable 5 Gemini 3.6 Flash Gemini 3.5 Flash Sonar Pro Sonar Reasoning Pro
Hugging Face 4/5 4/5 5/5 5/5 5/5 5/5 5/5 5/5 5/5 5/5
Together 4/5 3/5 3/5 5/5 5/5 5/5 5/5 5/5 5/5 5/5
Fireworks 3/5 3/5 3/5 5/5 5/5 5/5 5/5 4/5 5/5 5/5
Baseten 4/5 2/5 1/5 5/5 3/5 5/5 4/5 5/5 4/5 3/5
Modal 5/5 3/5 5/5 4/5 3/5 3/5 5/5 5/5 0/5 1/5
RunPod 2/5 0/5 4/5 3/5 4/5 2/5 5/5 5/5 5/5 4/5
Groq 1/5 1/5 1/5 5/5 4/5 5/5 5/5 2/5 3/5 5/5
Replicate 2/5 2/5 4/5 1/5 3/5 2/5 3/5 3/5 2/5 1/5
Ollama 0/5 0/5 1/5 4/5 3/5 2/5 3/5 2/5 2/5 4/5
Amazon Bedrock 0/5 0/5 0/5 4/5 4/5 5/5 1/5 0/5 2/5 3/5
SiliconFlow 0/5 0/5 0/5 3/5 4/5 2/5 1/5 2/5 1/5 4/5
Lambda 1/5 0/5 1/5 3/5 4/5 0/5 2/5 3/5 0/5 0/5
DeepInfra 1/5 0/5 0/5 0/5 2/5 1/5 1/5 3/5 0/5 0/5
Vast.ai 0/5 0/5 0/5 0/5 3/5 0/5 4/5 1/5 0/5 0/5
OpenRouter 1/5 0/5 0/5 2/5 0/5 2/5 0/5 1/5 0/5 0/5
Cerebras 0/5 0/5 0/5 1/5 0/5 1/5 0/5 0/5 0/5 0/5
Anyscale 0/5 0/5 0/5 0/5 0/5 0/5 0/5 1/5 0/5 0/5
Vercel AI 0/5 0/5 0/5 0/5 0/5 0/5 1/5 0/5 0/5 0/5

Model by model, the sharpest splits are these.

GPT-5.6 Sol is the Modal model. It names Modal in every answer and Groq in one. GPT-5.6 Terra keeps the shortest list. It never names RunPod, Ollama, Bedrock, SiliconFlow or Lambda. GPT-5.6 Luna is the only OpenAI model that names RunPod in most of its answers.

The three Claude models are the Groq and Bedrock models. Claude Opus 5 and Claude Fable 5 name Groq every time, and Claude Fable 5 names Bedrock every time. No OpenAI model names Bedrock at all. Claude Sonnet 5 carries the long tail, with Lambda, Vast.ai and SiliconFlow in most of its answers.

The two Gemini models are the infrastructure models. Both name Modal and RunPod in every answer, which makes Modal 100% and RunPod 100% at the Google family level.

The two Perplexity models split from each other on Modal. Sonar Pro never names it, and Sonar Reasoning Pro names it once. Both name RunPod often, and Sonar Reasoning Pro names Ollama and SiliconFlow in four of five.

The practical reading: the shortlist you get depends on the assistant you ask. The top three hold across all ten. Everything below them moves.

Why does the question change the leader?

The leader changes when the question turns to price. The only two answers that left Hugging Face out were replies to the fast, cheap question, from GPT-5.6 Sol and GPT-5.6 Terra. Both opened with Fireworks AI instead. GPT-5.6 Sol put it plainly: “Best overall: Fireworks AI.” The same two models led their answers to the head question with Hugging Face. GPT-5.6 Terra’s opened: “Best default for most developers: Hugging Face Inference Endpoints.”

The mechanism is in how the answers frame the job. Asked to host and serve a model, the OpenAI answers describe deploying a model repository from the Hub to a managed endpoint, and they cite Hugging Face’s documentation. Asked for fast, cheap inference, they describe buying tokens from a serverless API, and their citations move to Fireworks’ serverless and pricing pages. Same models, same run, two different products at the top.

That also explains Fireworks’ shape in the table. It is named in 43 answers and first in only 4. The models keep it on nearly every list and, in these answers, move it to the top when the question names speed and cost. The totals cannot show that. Only the answers can.

What should a buyer do with this list?

Decide which job you are buying before you read the ranking. There are three, and the answers sort vendors into them.

  1. Deploying a model you chose or fine-tuned. Start with Hugging Face if the model is on the Hub, Baseten if it is your own fine-tune, and Modal if your team wants to define the serving code in Python.
  2. Calling a popular open model by API. Start with Together and Fireworks, and add Groq if latency is the constraint.
  3. Running the stack yourself. Start with RunPod or Lambda for rented GPUs, or Ollama for your own hardware.

Then price the names for your job on each vendor’s own pricing page, because this record holds no pricing. Check which assistant produced any AI advice you already have, using the split above. Read vendor-published comparison guides as marketing, including the ones AI answers cite. The method page explains how the panel runs and what each count means.

How the sample was built

Inference hosting here means a platform that takes an open-source or open-weight model and serves it to your application, as a pay-per-token API, a dedicated endpoint or rented GPUs you run it on.

The sample: 10 models x 5 fixed prompts = 50 recorded answers, in the September 2026 edition. One answer was recorded per model-and-question pair, and every model ran with web search on. The five questions, verbatim:

  1. What is the best platform to host and serve open-source models for a developer? Name specific products.
  2. Which platform to host and serve open-source models would you recommend to a developer in 2026?
  3. Compare the top platform to host and serve open-source models options right now.
  4. I’m a developer and I need a platform to host and serve open-source models. What should I use and why?
  5. Best platform to host and serve open-source models for fast, cheap inference?

The model families and their answer counts:

The edition tracked 19 vendors, and 18 were named at least once.

How this sits against the pages Google ranks for this question

None of the pages captured from Google’s results for this question ranks inference hosts. They answer neighbouring questions, and each is published by a company with a product in or beside its list.

Kuberns’ guide ranks cloud hosting for freelancers and agencies, and puts Kuberns first. The same guide promotes a partnership program that pays referrers from client infrastructure spend. eXo Platform’s guide covers open-source portal software and includes a section on why eXo Platform is the best open source portal software. Hostinger’s guide lists Heroku alternatives, from self-hosted Coolify and Dokku to managed Render and Fly.io. It also promotes Hostinger’s own VPS plans inside the list. Ability AI’s guide ranks platforms for running AI agents in production and discloses that it builds Trinity, the platform it ranks first. GitBook’s guide ranks API documentation and SDK tools and names GitBook the best overall choice.

The pages the AI answers cite are a different set. The most-cited hosts in the recorded responses are llmapi.ai (107 citations), edenai.co (66) and siliconflow.com (54). Citation counts reflect citations returned in the recorded API responses, and coverage varies by model.

The distinction is simple. Those guides rank products for purchase, usually with a stake in the result. This page counts the names ten AI answers produce, shows the split by model, and explains where the order changes. It adds the only inference-hosting ranking in the captured set, the model-by-model disagreement, and the evidence that the question’s wording moves the leader.

What these counts cannot tell you

These counts measure presence in AI answers. They say nothing about uptime, speed, support, pricing fairness or fit with your stack, and a name in an answer is not always a recommendation, since an answer can list a product to warn against it. Each model answered each question once, so one different reply can move a small vendor’s count. This is one dated snapshot. Vendor names are matched as strings, which misses variants: the DeepInfra count, for example, did not register a Claude Opus 5 answer that spelled it “Deepinfra”. The answers came through each model’s API, which can differ from the consumer chat apps. The prompts were in English.

Frequently asked questions

For serving open-source AI models, the names the recorded answers give most are Hugging Face, Together and Fireworks, with Baseten, Modal and RunPod next. For hosting open-source web apps, which is how several of Google’s results read the question, one captured guide lists Coolify and Dokku as open-source, self-hosted alternatives to Heroku.

Is Platform as a Service (PaaS) open source?

Some are. PaaS describes how a platform is delivered, not how it is licensed. One captured guide describes Coolify as a self-hosted, open-source Heroku alternative. On the model side, the answers in this panel point to Ollama and vLLM as the open software you run yourself, while the hosted platforms above are services you pay for.

Which platform should serve a model I fine-tuned myself?

The answers point to Baseten first for custom and fine-tuned production endpoints, to Hugging Face if the fine-tune is on the Hub, and to Modal if you want to write the serving code in Python. Together also appears in Claude answers as a provider you can start on by API and move to dedicated deployments later.

Do these AI models name the same inference hosts?

On the top three, mostly yes. Hugging Face, Together and Fireworks appear across all ten models. Below them the lists diverge sharply by model, so the same question put to GPT-5.6 Sol and Sonar Pro returns different specialists.

Where is the pricing for each platform?

This edition’s record holds no pricing for any of the vendors, so none is quoted here. Check each vendor’s own pricing page for the tier you need, and note that the answers split pay-per-token APIs from GPU-time billing.

Can a vendor pay to appear or rank higher on this list?

No. No position is sold, sponsored or influenced. Vendors cannot pay to appear, be reordered or be removed, and a product that was never named is not listed.

How often is this list updated?

Editions are monthly, and each runs the fixed panel of five buyer questions for the category. A vendor’s movement between editions reflects changes in the answers, not changes in the method.

How this list is ordered

The order is the measurement, not an assessment of the products. Answer share is the share of recorded answers that named the tool. Named first is the share where it appeared before any other tracked tool. Both are counts from one dated edition and are published in full on the category page.

A tool appears here only if it was named in the edition and its record carries a sourced claim. A product that was never named is not listed, and no position is sold.

Where to check it