LLM API Providers Compared: Model Labs, Hosts & Gateways

Reviewed 22 October 2026. Written by the ShareAI team. We operate one of the services discussed. This is a representative buyer’s guide, not an independent performance ranking or an exhaustive directory. Screenshots are actual public product/documentation captures; no paid cross-provider benchmark was run.

Choosing an LLM API provider starts with the layer you need. A model lab gives access to its model family. An inference host serves supported models on its infrastructure. A gateway routes or governs calls, while a marketplace can also simplify procurement and payment. ShareAI adds approved application AI surcharges and eligible infrastructure participation. These layers can work together; putting them in one numbered “best” list hides who owns the model, the request path and the customer relationship.

This guide groups twelve representative options into those roles, then gives a cost and migration worksheet. Evaluate the exact model version, endpoint, deployment and account access you need. Availability at a model vendor does not prove that the same model or feature is currently available through every host, gateway or ShareAI account.

Start with the layer you need to buy

• A particular model family or native API feature: begin with the model lab’s direct API and current model documentation.
• Serving supported model weights: compare inference hosts, shared versus dedicated capacity, deployment controls and model licensing.
• Several provider routes: evaluate a gateway or marketplace, including credentials, funding, policies and the actual supported catalog.
• Commercial participation: distinguish buying inference from monetizing application traffic, contributing compute or enrolling model rights.

The model and the endpoint are a pair. The same model name can be served with different versions, quantization, request features, limits and operating conditions. Record the pair you tested so that a later catalog update or alias change does not silently change the basis of the decision.

Model labs: direct access to a model family

OpenAI: evaluate the native API feature set

Best for: applications that need OpenAI models and their documented native API capabilities.

Pros: the official model catalog and API reference make model and endpoint selection explicit. This is the baseline when your application depends on a specific native capability.

Cons and cost check: a familiar client library does not guarantee identical behavior on another provider. Review model, processing and tool pricing and the data controls for the actual endpoint. Account access and optional data controls have their own conditions.

Anthropic: direct Claude integration

Best for: workloads whose quality or workflow depends on Claude and its native API behavior.

Pros: the official model overview ties current model IDs to platform-specific resources. Direct integration provides a clear baseline for evaluating a compatible route elsewhere.

Cons and cost check: map message structures, tool behavior, streaming and versioning explicitly when changing providers. Use current model pricing and account limits; do not assume a chat subscription provides interchangeable API access or unlimited capacity.

Google Gemini: choose the model and API route together

Best for: applications evaluating Google’s models for their required text, visual or other supported workload.

Pros: the Gemini model documentation separates model capabilities and available interfaces, making it possible to shortlist a model for the actual task.

Cons and cost check: review the chosen API route, model release status, quotas and billing relationship. A Google Cloud integration and another API entry point should not be assumed to have identical conditions. Price the exact modality and model using its current terms.

Mistral AI: model access and deployment fit

Best for: teams evaluating Mistral models and the associated developer platform for their application.

Pros: its official documentation distinguishes model/inference, developer console and administration workflows. That helps separate API integration from organization and deployment needs.

Cons and cost check: check the exact model’s supported features and license when serving weights or redistributing access. A hosted API contract, model serving rights and the cost of owned infrastructure are separate decisions; use the current model and service terms.

Inference hosts: select how the model is served

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.

Groq: test the supported inference route

Best for: teams evaluating the available hosted models for a latency-sensitive application.

Pros: the official documentation provides model, API, compatibility and limit references, so a candidate route can be checked against the application’s requirements.

Cons and cost check: the desired model and request features must be supported by the actual route. Published throughput is not a prediction for every workload. Measure successful tasks, first-token time, total response time, rate limits and actual cost with representative inputs.

DeepInfra: API usage and private compute are separate offers

Best for: teams comparing hosted inference with private model deployment or GPU infrastructure options.

Pros: the official documentation exposes these as separate choices, with model APIs, private deployment and GPU resources documented independently.

Cons and cost check: an OpenAI-compatible interface does not prove every model parameter or native platform feature transfers unchanged. Compare the actual model tariff, private deployment or infrastructure allocation. A GPU rental bill and a per-model API usage bill need different utilization assumptions.

Gateways and marketplaces: route, govern or commercialize usage

Hugging Face Inference Providers: distinguish routing from model hosting

Best for: teams exploring model/provider combinations through a common integration.

Pros: the official Inference Providers guide explains the integration around participating providers. It can simplify comparing available serving routes without treating every model repository as an active API.

Cons and cost check: a model card, an Inference Providers route and a dedicated Inference Endpoint are different things. Confirm the selected model/provider pairing, account billing and request features before assuming that repository availability means hosted capacity.

OpenRouter: managed model procurement

Best for: applications that want hosted access to several model providers through a managed account.

Pros: provider selection and account funding become part of a service rather than another proxy for your team to operate. See current pricing and BYOK terms.

Cons and cost check: review actual provider/model support and the funding basis. Its Enterprise Private Endpoints exist with provider and catalog requirements. An account funding fee is different from upstream token pricing or application surcharge earnings.

Portkey: govern requests and retain evidence

Best for: teams that need gateway policies and observability across provider calls.

Pros: routing, logging and related controls can be evaluated together using the gateway documentation.

Cons and cost check: match retained logs, private deployment and governance to the chosen plan; upstream inference remains relevant. Review recording allowances, retention and charging units rather than comparing only a subscription headline.

ShareAI: application monetization and eligible infrastructure

Best for: application owners who want approved routed AI traffic to carry a configured surcharge, plus eligible infrastructure contributors. ShareAI provides routing, usage, customer payment and account-specific settlement; the application is built and hosted elsewhere.

Pros: customers pay ShareAI for routed usage, while the approved Builder’s margin is attributed to application traffic. Inference-service onboarding and the Prosumer path provide a separate way to evaluate eligible owned compute. See AI Gateway and connecting your application.

Cons and cost check: models, infrastructure and commercial paths need eligibility checks. Validated company Builders have a 45-day review period before payout; individuals currently receive inference value inside ShareAI rather than cash-out. A margin is not guaranteed revenue. Inspect current model offers, applicable fees and account terms instead of relying on a generic reward percentage.

Compare costs and requirements using the actual request

Use the exact model version, endpoint and account access as the comparison key. Record input, output, cached and other billable units where applicable, then add tools, storage, gateway charges, funding fees and failed work. A cheaper input token price can lose its advantage when the task needs longer output, retries or expensive tools. Compare accepted business outputs and total operating effort.

Keep data questions equally concrete: application, gateway, inference host and telemetry store each have their own policies and destinations. Review region, retention and access requirements for the chosen route. A company’s headquarters, a self-hosted proxy or a provider key does not establish the location of every downstream operation.

• Compatibility: check streaming, tools, structured output, context limits and errors for the exact model/endpoint pair.
• Capacity: check account quotas, burst behavior, queueing, availability commitments and what happens when the desired route is unavailable.
• Change control: record version IDs, aliases and deprecation policies. Retest when the serving configuration changes.
• Commercial rights: separate buying API usage, hosting model weights, contributing compute and distributing or licensing a model.

Owned endpoints, Builder earnings and Provider contribution

Bringing an inference endpoint is a technical connection; it is not automatically a compute contribution program or a customer-payment relationship. LiteLLM and other gateways already support custom endpoint configurations, and some support configurable cost margins. ShareAI’s approved Builder flow adds customer approval, payment and settlement around an application’s routed usage. Provider incentives come from eligible compute contribution, while Creator arrangements govern model participation and serving rights.

A hypothetical $1,000 inference cost with a 15% Builder surcharge produces $150 gross surcharge and a $1,150 customer charge before applicable adjustments. That arithmetic is not a guaranteed profit or a current model price. Own hardware also has acquisition, energy, availability and operating costs; it should be evaluated using realistic utilization and eligibility.

Run a small, observable integration test

Choose a representative task, its required output and the acceptance criteria first. Record the current route and costs. For a ShareAI evaluation, use https://api.shareai.now/api/v1 and a model actually available to the account. Keep the key on the server. These Chat Completions examples demonstrate one request contract; they do not establish parity with every model lab’s native API or hosted tools.

curl --fail-with-body https://api.shareai.now/api/v1/chat/completions \
  -H "Authorization: Bearer $SHAREAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"YOUR_AVAILABLE_MODEL","messages":[{"role":"user","content":"Reply with one short greeting."}],"stream":false}'

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());

• Normal behavior: verify model identity, response shape, output quality and usage accounting.
• Required features: test streaming, tools, structured output, cancellation and any modality separately.
• Failures and controls: test invalid keys, disallowed models, timeouts, unavailable capacity and budget behavior.
• Measure and release: compare completed-task cost, errors, first-token time and total response time. Move a small traffic share and retain rollback.

The request examples were syntax-checked against the implemented ShareAI endpoint; no paid provider benchmark was run. Use the current documentation and live catalog, and record the exact tested configuration. Compatible HTTP shapes reduce integration work but still require application-level verification.

Frequently asked questions

Choose the layer, test the route, then commit traffic

Start with the model and business task, then decide who should own serving, routing, evidence and payment. Keep the options that meet the acceptance criteria rather than chasing one overall winner. For approved application AI monetization, open ShareAI Builder. For eligible owned capacity, explore AI Prosumer.