Best Open-Source LLM Hosting Providers 2025 — BYOI & ShareAI’s Hybrid Route

TL;DR — There are three practical paths to run open-source LLMs today:

(1) Managed (serverless; pay per million tokens; no infrastructure to maintain),

(2) Open-Source LLM Hosting (self-host the exact model you want), and

(3) BYOI fused with a decentralized network (run on your own hardware first, then fail over automatically to network capacity like ShareAI). This guide compares leading options (Hugging Face, Together, Replicate, Groq, AWS Bedrock, io.net), explains how BYOI works in ShareAI (with a per-key Priority over my Device toggle), and gives patterns, code, and cost thinking to help you ship with confidence.

For a complementary market overview, see Eden AI’s landscape article: Best Open-Source LLM Hosting Providers.

Table of contents

• The rise of open-source LLM hosting
• What “open-source LLM hosting” means
• Why host open-source LLMs?
• Three roads to running LLMs
  • 4.1 Managed (serverless; pay per million tokens)
  • 4.2 Open-Source LLM Hosting (self-host)
  • 4.3 BYOI + decentralized network (ShareAI fusion)
• ShareAI in 30 seconds
• How BYOI with ShareAI works (priority to your device + smart fallback)
• Quick comparison matrix (providers at a glance)
• Provider profiles (short reads)
• Where ShareAI fits vs others (decision guide)
• Performance, latency & reliability (design patterns)
• Governance, compliance & data residency
• Cost modeling: managed vs self-hosted vs BYOI + decentralized
• Step-by-step: getting started
• Code snippets
• Real-world examples
• FAQs (long-tail SEO)
• Final thoughts

The rise of open-source LLM hosting

Open-weight models like Llama 3, Mistral/Mixtral, Gemma, and Falcon have tilted the landscape from “one closed API fits all” to a spectrum of choices. You decide where inference runs (your GPUs, a managed endpoint, or decentralized capacity), and you choose the trade-offs between control, privacy, latency, and cost. This playbook helps you pick the right path — and shows how ShareAI lets you blend paths without switching SDKs.

While reading, keep the ShareAI Models marketplace open to compare model options, typical latencies, and pricing across providers.

What “open-source LLM hosting” means

• Open weights: model parameters are published under specific licenses, so you can run them locally, on-prem, or in the cloud.
• Self-hosting: you operate the inference server and runtime (e.g., vLLM/TGI), choose hardware, and handle orchestration, scaling, and telemetry.
• Managed hosting for open models: a provider runs the infra and exposes a ready API for popular open-weight models.
• Decentralized capacity: a network of nodes contributes GPUs; your routing policy decides where requests go and how failover happens.

Why host open-source LLMs?

• Customizability: fine-tune on domain data, attach adapters, and pin versions for reproducibility.
• Cost: control TCO with GPU class, batching, caching, and locality; avoid premium rates of some closed APIs.
• Privacy & residency: run on-prem/in-region to meet policy and compliance requirements.
• Latency locality: place inference near users/data; leverage regional routing for lower p95.
• Observability: with self-hosting or observability-friendly providers, you can see throughput, queue depth, and end-to-end latency.

Three roads to running LLMs

4.1 Managed (serverless; pay per million tokens)

What it is: you buy inference as a service. No drivers to install, no clusters to maintain. You deploy an endpoint and call it from your app.

Pros: fastest time-to-value; SRE and autoscaling are handled for you.

Trade-offs: per-token costs, provider/API constraints, and limited infra control/telemetry.

Typical choices: Hugging Face Inference Endpoints, Together AI, Replicate, Groq (for ultra-low latency), and AWS Bedrock. Many teams start here to ship quickly, then layer BYOI for control and cost predictability.

4.2 Open-Source LLM Hosting (self-host)

What it is: you deploy and operate the model — on a workstation (e.g., a 4090), on-prem servers, or your cloud. You own scaling, observability, and performance.

Pros: full control of weights/runtime/telemetry; excellent privacy/residency guarantees.

Trade-offs: you take on scalability, SRE, capacity planning, and cost tuning. Bursty traffic can be tricky without buffers.

4.3 BYOI + decentralized network (ShareAI fusion)

What it is: hybrid by design. You Bring Your Own Infrastructure (BYOI) and give it first priority for inference. When your node is busy or offline, traffic fails over automatically to a decentralized network and/or approved managed providers — without client rewrites.

Pros: control and privacy when you want them; resilience and elasticity when you need them. No idle time: if you opt in, your GPUs can earn when you’re not using them (Rewards, Exchange, or Mission). No single-vendor lock-in.

Trade-offs: light policy setup (priorities, regions, quotas) and awareness of node posture (online, capacity, limits).

ShareAI in 30 seconds

• One API, many providers: browse the Models marketplace and switch without rewrites.
• BYOI first: set policy so your own nodes take traffic first.
• Automatic fallback: overflow to the ShareAI decentralized network and/or named managed providers you allow.
• Fair economics: most of every dollar goes to the providers doing the work.
• Earn from idle time: opt in and provide spare GPU capacity; choose Rewards (money), Exchange (credits), or Mission (donations).
• Quick start: test in the Playground, then create a key in the Console. See API Getting Started.

How BYOI with ShareAI works (priority to your device + smart fallback)

In ShareAI you control routing preference per API key using the Priority over my Device toggle. This setting decides whether requests try your connected devices first or the community network first — but only when the requested model is available in both places.

Jump to: Understand the toggle · What it controls · OFF (default) · ON (local-first) · Where to change it · Usage patterns · Quick checklist

Understand the toggle (per API key)

The preference is saved for each API key. Different apps/environments can keep different routing behaviors — e.g., a production key set to community-first and a staging key set to device-first.

What this setting controls

When a model is available on both your device(s) and the community network, the toggle chooses which group ShareAI will query first. If the model is available in only one group, that group is used regardless of the toggle.

When turned OFF (default)

• ShareAI attempts to allocate the request to a community device sharing the requested model.
• If no community device is available for that model, ShareAI then tries your connected device(s).

Good for: offloading compute and minimizing usage on your local machine.

When turned ON (local-first)

• ShareAI first checks if any of your devices (online and sharing the requested model) can process the request.
• If none are eligible, ShareAI falls back to a community device.

Good for: performance consistency, locality, and privacy when you prefer requests to stay on your hardware when possible.

Where to change it

Open the API Key Dashboard. Toggle Priority over my Device next to the key label. Adjust any time per key.

Recommended usage patterns

• Offload mode (OFF): Prefer the community first; your device is used only if no community capacity is available for that model.
• Local-first mode (ON): Prefer your device first; ShareAI falls back to community only when your device(s) can’t take the job.

Quick checklist

• Confirm the model is shared on both your device(s) and the community; otherwise the toggle won’t apply.
• Set the toggle on the exact API key your app uses (keys can have different preferences).
• Send a test request and verify the path (device vs community) matches your chosen mode.

Quick comparison matrix (providers at a glance)

Provider / Path	Best for	Open-weight catalog	Fine-tuning	Latency profile	Pricing approach	Region / on-prem	Fallback / failover	BYOI fit	Notes
AWS Bedrock (Managed)	Enterprise compliance & AWS ecosystem	Curated set (open + proprietary)	Yes (via SageMaker)	Solid; region-dependent	Per request/token	Multi-region	Yes (via app)	Permitted fallback	Strong IAM, policies
Hugging Face Inference Endpoints (Managed)	Dev-friendly OSS with community gravity	Large via Hub	Adapters & custom containers	Good; autoscaling	Per endpoint/usage	Multi-region	Yes	Primary or fallback	Custom containers
Together AI (Managed)	Scale & performance on open weights	Broad catalog	Yes	Competitive throughput	Usage tokens	Multi-region	Yes	Good overflow	Training options
Replicate (Managed)	Rapid prototyping & visual ML	Broad (image/video/text)	Limited	Good for experiments	Pay-as-you-go	Cloud regions	Yes	Experimental tier	Cog containers
Groq (Managed)	Ultra-low latency inference	Curated set	Not main focus	Very low p95	Usage	Cloud regions	Yes	Latency tier	Custom chips
io.net (Decentralized)	Dynamic GPU provisioning	Varies	N/A	Varies	Usage	Global	N/A	Combine as needed	Network effects
ShareAI (BYOI + Network)	Control + resilience + earnings	Marketplace across providers	Yes (via partners)	Competitive; policy-driven	Usage (+ earnings opt-in)	Regional routing	Native	BYOI first	Unified API

Provider profiles (short reads)

AWS Bedrock (Managed)

Best for: enterprise-grade compliance, IAM integration, in-region controls. Strengths: security posture, curated model catalog (open + proprietary). Trade-offs: AWS-centric tooling; cost/governance require careful setup. Combine with ShareAI: keep Bedrock as a named fallback for regulated workloads while running day-to-day traffic on your own nodes.

Hugging Face Inference Endpoints (Managed)

Best for: developer-friendly OSS hosting backed by the Hub community. Strengths: large model catalog, custom containers, adapters. Trade-offs: endpoint costs/egress; container upkeep for bespoke needs. Combine with ShareAI: set HF as primary for specific models and enable ShareAI fallback to keep UX smooth during bursts.

Together AI (Managed)

Best for: performance at scale across open-weight models. Strengths: competitive throughput, training/fine-tune options, multi-region. Trade-offs: model/task fit varies; benchmark first. Combine with ShareAI: run BYOI baseline and burst to Together for consistent p95.

Replicate (Managed)

Best for: rapid prototyping, image/video pipelines, and simple deployment. Strengths: Cog containers, broad catalog beyond text. Trade-offs: not always cheapest for steady production. Combine with ShareAI: keep Replicate for experiments and specialty models; route production via BYOI with ShareAI backup.

Groq (Managed, custom chips)

Best for: ultra-low-latency inference where p95 matters (real-time apps). Strengths: deterministic architecture; excellent throughput at batch-1. Trade-offs: curated model selection. Combine with ShareAI: add Groq as a latency tier in your ShareAI policy for sub-second experiences during spikes.

io.net (Decentralized)

Best for: dynamic GPU provisioning via a community network. Strengths: breadth of capacity. Trade-offs: variable performance; policy and monitoring are key. Combine with ShareAI: pair decentralized fallback with your BYOI baseline for elasticity with guardrails.

Where ShareAI fits vs others (decision guide)

ShareAI sits in the middle as a “best of both worlds” layer. You can:

• Run on your own hardware first (BYOI priority).
• Burst to a decentralized network automatically when you need elasticity.
• Optionally route to specific managed endpoints for latency, price, or compliance reasons.

Decision flow: if data control is strict, set BYOI priority and restrict fallback to approved regions/providers. If latency is paramount, add a low-latency tier (e.g., Groq). If workloads are spiky, keep a lean BYOI baseline and let the ShareAI network catch peaks.

Experiment safely in the Playground before wiring policies into production.

Performance, latency & reliability (design patterns)

• Batching & caching: reuse KV cache where possible; cache frequent prompts; stream results when it improves UX.
• Speculative decoding: where supported, it can reduce tail latency.
• Multi-region: place BYOI nodes near users; add regional fallbacks; test failover regularly.
• Observability: track tokens/sec, queue depth, p95, and failover events; refine policy thresholds.
• SLOs/SLAs: BYOI baseline + network fallback can meet targets without heavy over-provisioning.

Governance, compliance & data residency

Self-hosting lets you keep data at rest exactly where you choose (on-prem or in-region). With ShareAI, use regional routing and allow-lists so fallback only occurs to approved regions/providers. Keep audit logs and traces at your gateway; record when fallback occurs and to which route.

Reference docs and implementation notes live in ShareAI Documentation.

Cost modeling: managed vs self-hosted vs BYOI + decentralized

Think in CAPEX vs OPEX and utilization:

• Managed is pure OPEX: you pay for consumption and get elasticity without SRE. Expect to pay a premium per token for convenience.
• Self-hosted mixes CAPEX/lease, power, and ops time. It excels when utilization is predictable or high, or when control is paramount.
• BYOI + ShareAI right-sizes your baseline and lets fallback catch peaks. Crucially, you can earn when your devices would otherwise be idle — offsetting TCO.

Compare models and typical route costs in the Models marketplace, and watch the Releases feed for new options and price drops.

Step-by-step: getting started

Option A — Managed (serverless)

• Pick a provider (HF/Together/Replicate/Groq/Bedrock/ShareAI).
• Deploy an endpoint for your model.
• Call it from your app; add retries; monitor p95 and errors.

Option B — Open-Source LLM Hosting (self-host)

• Choose runtime (e.g., vLLM/TGI) and hardware.
• Containerize; add metrics/exporters; configure autoscaling where possible.
• Front with a gateway; consider a small managed fallback to improve tail latency.

Option C — BYOI with ShareAI (hybrid)

• Install the agent and register your node(s).
• Set Priority over my Device per key to match your intent (OFF = community-first; ON = device-first).
• Add fallbacks: ShareAI network + named providers; set regions/quotas.
• Enable rewards (optional) so your rig earns when idle.
• Test in the Playground, then ship.

Code snippets

1) Simple text generation via ShareAI API (curl)

curl -X POST "https://api.shareai.now/v1/chat/completions" \
  -H "Authorization: Bearer $SHAREAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama-3.1-70b",
    "messages": [
      { "role": "system", "content": "You are a helpful assistant." },
      { "role": "user", "content": "Summarize BYOI in two sentences." }
    ],
    "stream": false
  }'

2) Same call (JavaScript fetch)

const res = await fetch("https://api.shareai.now/v1/chat/completions", {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${process.env.SHAREAI_API_KEY}`,
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    model: "llama-3.1-70b",
    messages: [
      { role: "system", content: "You are a helpful assistant." },
      { role: "user", content: "Summarize BYOI in two sentences." }
    ],
    stream: false
  })
});

if (!res.ok) {
  const text = await res.text();
  throw new Error(`ShareAI error ${res.status}: ${text}`);
}

const data = await res.json();
console.log(data.choices?.[0]?.message?.content);

Real-world examples

Indie builder (single nvidia rtx 4090, global users)

BYOI handles daytime traffic; the ShareAI network catches evening bursts. Daytime latency sits near ~900 ms; bursts ~1.3 s with no 5xx during peaks. Idle hours generate Rewards to offset monthly costs.

Creative agency (bursty projects)

BYOI for staging; Replicate for image/video models; ShareAI fallback for text surges. Fewer deadline risks, tighter p95, predictable spend via quotas. Editors preview flows in the Playground before production rollout.

Enterprise (compliance + regions)

BYOI on-prem EU + BYOI US; fallbacks restricted to approved regions/providers. Satisfies residency, keeps p95 steady, and gives a clear audit trail of any failovers.

FAQs

Final thoughts

Managed gives you serverless convenience and instant scale. Self-hosted gives you control and privacy. BYOI + ShareAI gives you both: your hardware first, automatic failover when you need it, and earnings when you don’t. When in doubt, start with one node, set the per-key preference to match your intent, enable ShareAI fallback, and iterate with real traffic.

Explore models, pricing, and routes in the Models marketplace, check Releases for updates, and review the Docs to wire this into production. Already a user? Sign in / Sign up.