Open-Source LLM Hosting: Providers, Owned Endpoints & Costs

Reviewed 22 October 2026. Written by the ShareAI team. ShareAI is discussed separately as a routing/commercial and eligible infrastructure participation layer. This guide compares hosting responsibilities and documented offers; it is not an independent speed ranking. Screenshots are actual documentation/product captures. No GPU deployment or paid cross-host performance benchmark was run for this article.

Choosing open-source LLM hosting starts with what you want to buy: a supported model API, a managed dedicated endpoint, GPU resources for a serving stack you operate, or inference on hardware you own. Hugging Face Inference Endpoints, Together AI, Fireworks AI, Runpod, Replicate and Scaleway provide different combinations of those choices. A gateway can route a model call without being the service that loads the model weights.

Use “open-source” carefully. A downloadable model checkpoint, its license, the inference engine and the hosting service are separate artifacts. Check the exact model’s permissions and deployment requirements. This guide uses the familiar search term while making the model-rights check explicit; it does not assume every open-weight model permits every commercial or redistribution use.

Hosting options at a glance

A model repository is not an active endpoint, an API plan is not necessarily a reserved GPU, and an allocated GPU is not a production inference service. Keep those boundaries visible when comparing setup effort, response times and invoices. Record the exact model version and the place it was served together.

Choose a serving model before choosing a vendor

• Hosted model API: start with the available catalog and billable usage. The service manages much of the serving stack, while you validate model behavior and account limits.
• Managed dedicated endpoint: select model, engine and capacity under the service’s supported configuration. Include warm/idle and initialization behavior.
• GPU resources: decide who packages, updates, exposes, protects and monitors the serving process, and how model artifacts and storage survive restarts.
• Owned hardware: include acquisition, energy, networking, availability and maintenance. A checkpoint download does not establish that the machine can serve the desired workload.
• Routing or participation: evaluate gateways and eligible infrastructure programs separately from the underlying host and model rights.

For a prototype, reducing setup work may matter more than squeezing utilization from a GPU. For steady production traffic, predictable capacity, change control and support may matter more. For a fine-tuned or private model, verify artifact access and serving compatibility before assuming that a public hosted-model catalog can deploy it.

Six hosting choices: strengths, limits and best fit

Hugging Face Inference Endpoints: managed model and engine deployment

Best for: teams that want a managed dedicated endpoint around an appropriate Hub model and inference engine. The official overview separates model artifacts, engine and infrastructure.

Pros: the service manages the endpoint container lifecycle and scaling, with documented engine options including vLLM, SGLang and others. That can reduce infrastructure work while retaining a concrete model/engine configuration to qualify.

Cons: a Hub repository does not guarantee that every model/engine/hardware combination is supported. Check access requirements, quotas, scaling behavior, networking and the features available to the chosen account. Dedicated deployment is distinct from Hugging Face Inference Providers routing.

Cost check: published endpoint pricing shows hourly resource rates billed per minute and charges during applicable initialization and running states. Include replicas, idle capacity and scaling behavior rather than comparing that bill directly with one token tariff.

Together AI: serverless or dedicated inference

Best for: teams that want hosted model access while evaluating shared and dedicated operating models.

Pros: the official overview distinguishes serverless inference from dedicated model deployment. That makes the procurement choice explicit before a volume commitment.

Cons and cost check: the available model, integration behavior and throughput depend on the selected route and account. Compare the provider’s current model usage terms with dedicated capacity and operational commitments rather than assuming those bills use the same unit.

Fireworks AI: inference and configured deployments

Best for: applications evaluating a hosted model catalog together with deployment configuration options.

Pros: the official introduction separates serverless inference, deployments and model capabilities. Those are useful boundaries for comparing a small pilot with a production workload.

Cons and cost check: test tools, streaming, structured output and any custom-model path on the actual selected deployment. Use the relevant serverless or deployment pricing, including reserved capacity when chosen. The article makes no independent speed or price ranking.

Runpod: distinguish serverless workers from GPU Pods

Best for: teams choosing how much of the serving container and compute lifecycle they want to own. Runpod documents Serverless separately from its Pods and other compute offers.

Pros: Serverless supports worker-based deployments with scaling choices; load-balancing endpoints and queue-based endpoints expose different request models. Those options can support a custom inference stack when the workload and container fit.

Cons: a traditional queued job interface is not automatically an OpenAI Chat Completions server. Cold starts include container/model initialization, and an idle worker can remain billable before scaling down. Validate the exact endpoint type, API path, model loading and storage lifecycle.

Cost check: compare the actual worker or Pod rate, active/warm settings, cold-start and idle time, storage and other applicable charges. A low compute rate does not establish low cost per accepted request when the workload spends much of its time waiting or starting.

Replicate: native predictions and dedicated deployment control

Best for: applications using an available Replicate model or packaging a custom model for its platform. Its documentation covers model versions and predictions, while Deployments provides a separate infrastructure-control workflow.

Pros: documented deployments support hardware and scaling choices, including keeping instances warm or scaling to zero. Model-version and deployment concepts provide a useful baseline for release and rollback planning.

Cons: validate the model’s native input/output and prediction lifecycle. Do not assume every model uses an OpenAI-compatible chat endpoint. Check license, custom packaging, streaming and retention, and distinguish a model-specific offer from dedicated capacity.

Cost check: inspect the actual model/deployment charging basis and minimum/warm instance settings. Include initialization, hardware, idle capacity and output handling as applicable. A platform supporting many model types does not mean every model has identical pricing or features.

Scaleway: choose hosted usage or dedicated capacity

Best for: applications that can use a supported hosted model or need a dedicated inference deployment instead of a multi-provider marketplace. The Generative APIs FAQ distinguishes Serverless and Dedicated Deployment.

Pros: the FAQ documents Serverless model hosting in Europe, currently Paris, and recommends Dedicated Deployment for a single-region requirement. Dedicated deployment provides a separate capacity and model-configuration choice.

Cons: a host’s available models, lifecycle and deployment features differ from a broad gateway catalog. Check exact model access, required tools, quota and retention, and decide who owns any routing between services. Do not infer every third-party model is available because it has released weights.

Cost check: Serverless usage and dedicated provisioned capacity use different billing assumptions. Dedicated resources can incur charges while idle, so compare realistic utilization and operating needs instead of treating a per-token quote as equivalent to reserved capacity.

Size memory and concurrency for the actual workload

Treat model weights as one part of capacity planning. The serving runtime, request context, generated output, concurrency and cache settings also affect whether the workload fits. Quantization can change memory use and output behavior; supported formats and kernels depend on the selected engine and hardware. Use the vLLM stable quickstart and memory configuration guide with the model documentation as a starting point, not a universal GPU-size rule.

Pin the model revision, tokenizer/chat template, precision or quantization and serving version. Record the requested context/output limits and representative concurrency. A service that returns a short answer for one request has not yet proved that it can handle the longest input or the busiest user period your application needs.

Inspect the model card and actual license for hosting, distribution and commercial use. Include adapters, fine-tunes and any additional assets in the review. A hosting provider’s terms and a model author’s conditions answer different questions. Do not infer permission to serve a proprietary model merely because a gateway accepts its name elsewhere.

Data control is also a complete-path question: inference host, artifact storage, logs, traces, support and downstream tools can have different destinations and retention. A private endpoint or owned server does not by itself establish every part of the arrangement. Record the actual route and enabled features you qualified.

Compare utilization before projecting a dedicated-GPU saving

For a simple illustration, suppose capacity costs $1 per provisioned hour and you allocate 100 hours. The base resource bill is $100 regardless of whether useful accepted work occupies 10, 50 or 90 of those hours. Dividing that fixed bill by productive time shows why utilization matters; this is hypothetical arithmetic, not a provider tariff or tokens-per-second result.

Compare the actual hosted-model bill for the same accepted tasks, then add storage, network, warm instances, supporting services, failure/retry cost and operations. A dedicated deployment may improve control or predictability without being the lowest-cost choice at sparse traffic. A serverless worker can also incur billable startup or idle time, so “serverless” is not a promise of zero overhead.

Use ordinary traffic and peaks separately. Warm capacity can reduce startup delay but costs money when quiet. Scaling down can reduce resource use while adding restart and queue behavior. Choose a configuration that meets the response deadline and availability requirement, then compare total cost on that qualified configuration.

From deployment documentation to an observable request

A practical trial begins with permitted model artifacts and hardware that fits the declared workload. Select a supported engine release, configure model identity and access, start the serving process, and verify that the model list and readiness behavior match the deployment. Keep the endpoint local or appropriately protected while qualifying it; follow the selected engine’s installation and serving guide.

The cURL example below targets an already running, authenticated vLLM-style local chat endpoint on port 8000. Replace the model placeholder with the deployed ID and use the key configured for that server. It does not install an engine or download weights. The JavaScript tab separately demonstrates ShareAI’s implemented route for an account-supported model; onboarding an owned endpoint is a separate step, not a result of that request.

curl http://127.0.0.1:8000/v1/chat/completions \
  -H "Authorization: Bearer $INFERENCE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "YOUR_DEPLOYED_MODEL_ID",
    "messages": [{"role": "user", "content": "Return a short test response."}],
    "max_tokens": 64
  }'

const response = await fetch("https://api.shareai.now/api/v1/chat/completions", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.SHAREAI_API_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: process.env.SHAREAI_MODEL,
    messages: [{ role: "user", content: "Reply with one short greeting." }],
    stream: false,
  }),
});
if (!response.ok) throw new Error(`Inference failed: ${response.status}`);
console.log(await response.json());

• Single request: verify model identity, input/output format, supported parameters, usage and accepted task quality.
• Streaming and concurrency: measure first-token and total time, queued work, completed throughput and errors under the declared input/output limits.
• Warm and cold behavior: test startup, scaling, sustained load and unavailable capacity separately; record actual billed units.
• Release: verify model updates, access, retained evidence and rollback, then move a small observable traffic share.

The local request format was checked against the current serving documentation, and the JavaScript syntax against the implemented ShareAI endpoint. No running GPU host or paid provider benchmark is claimed. Run the proposed test on your chosen configuration before turning any vendor rate or benchmark into an application forecast.

ShareAI: connect the commercial layer and eligible owned capacity

ShareAI is a separate consideration when you already have inference or want an approved application commercial workflow. Inference-service onboarding and Prosumer/Provider paths let eligible participants evaluate owned endpoints or compute. Check compatibility, model rights, hardware eligibility, capacity and program terms. A connected machine does not guarantee traffic, rewards, automatic fallback or the ability to run every model.

Builders have another role: an approved application can configure an AI surcharge while the customer pays ShareAI for routed usage. ShareAI handles routing, usage, customer payment and account-specific settlement; the application is built and hosted elsewhere. Company payouts require validation and a 45-day review period. Individual Builders currently receive inference value inside ShareAI rather than cash-out.

A hypothetical $1,000 inference cost with a 15% surcharge gives a $1,150 customer charge and $150 gross surcharge before applicable adjustments. It is not profit, a hosting tariff or guaranteed earnings. Provider compute incentives and Creator model-rights arrangements are separate. Custom endpoint connections or proxy margin settings alone are not unique to ShareAI.

Use the LLM provider-layer guide to distinguish labs, hosts and gateways, and the model-version and workflow comparison for a concrete model-selection example. Keep the host decision, model rights and customer commercial flow explicit.

Frequently asked questions

Choose the serving responsibility you can sustain

Start with permitted model artifacts and a representative workload, then qualify a hosting configuration before projecting cost or scale. For eligible owned-capacity participation, explore AI Prosumer. For approved application AI-surcharge workflows, open ShareAI Builder. Choose hosting, routing and commercial participation from the complete tested workflow.