Top 12 LLM API Providers in 2025 (ShareAI Guide)
Updated on September 2025 · ~12 minute read
LLM API providers 2025 matter more than ever for production apps. You need reliable, cost-efficient inference that scales, observability that keeps you honest, and the freedom to route traffic to the best model for each job—without lock-in.
This guide compares the top 12 LLM API providers 2025 and shows where ShareAI fits for teams that want one OpenAI-compatible API, people-powered routing across 150+ models, and built-in cost & latency visibility—so you can ship faster and spend smarter. For model discovery, see our Model Marketplace and start building with the API Reference.
Why LLM API Providers 2025 Matter
From prototype to prod: reliability, latency, cost, privacy
Reliability: production traffic means bursts, retries, fallbacks, and SLA conversations—not just a perfect demo path.
Latency: time-to-first-token (TTFT) and tokens/sec matter for UX (chat, agents) and infra cost (compute minutes saved).
Cost: tokens add up. Routing to the right model per task can reduce spend by double-digit percentages at scale.
Privacy & compliance: data handling, region residency, and retention policies are table-stakes for procurement.
What procurement cares about vs. what builders need
Procurement: SLAs, audit logs, DPAs, SOC2/HIPAA/ISO attestations, regionality, and cost predictability.
Builders: model breadth, TTFT/tokens-per-second, streaming stability, context windows, embeddings quality, fine-tuning, and zero-friction model switching. Explore the Docs Home and Playground.
TL;DR positioning—marketplace vs. single provider vs. ShareAI
Single-provider APIs: simplified contracts; limited model choice; potential premium pricing.
Marketplaces/routers: many models via one API; price/perf shopping; failover across providers.
ShareAI: people-powered marketplace + observability by default + OpenAI-compatible + no lock-in.
LLM API Providers 2025: At-a-Glance Comparison
These are directional snapshots to help short-list options. Pricing and model variants change frequently; confirm with each provider before committing.
| Provider | Typical Pricing Model | Latency Traits (TTFT / Throughput) | Context Window (typical) | Breadth / Notes |
|---|---|---|---|---|
| ShareAI (router) | Varies by routed provider; policy-based (cost/latency) | Depends on selected route; auto-failover & regional picks | Provider-dependent | 150+ models; OpenAI-compatible; built-in observability; policy routing; failover; BYOI supported |
| Together AI | Per-token by model | Sub-100ms claims on optimized stacks | Up to 128k+ | 200+ OSS models; fine-tuning |
| Fireworks AI | Per-token; serverless & on-demand | Very low TTFT; strong multimodal | 128k–164k | Text+image+audio; FireAttention |
| OpenRouter (router) | Model-specific (varies) | Depends on underlying provider | Provider-specific | ~300+ models via one API |
| Hyperbolic | Low per-token; discount focus | Fast model onboarding | ~131k | API + affordable GPUs |
| Replicate | Per-inference usage | Varies by community model | Model-specific | Long-tail models; quick protos |
| Hugging Face | Hosted APIs / self-host | Hardware-dependent | Up to 128k+ | OSS hub + enterprise bridges |
| Groq | Per-token | Ultra-low TTFT (LPU) | ~128k | Hardware-accelerated inference |
| DeepInfra | Per-token / dedicated | Stable inference at scale | 64k–128k | Dedicated endpoints available |
| Perplexity (pplx-api) | Usage / subscription | Optimized for search/QA | Up to 128k | Fast access to new OSS models |
| Anyscale | Usage; enterprise | Ray-native scale | Workload-dependent | End-to-end platform on Ray |
| Novita AI | Per-token / per-second | Low cost + quick cold starts | ~64k | Serverless + dedicated GPUs |
Methodology note: reported TTFT/tokens/sec vary by prompt length, caching, batching, and server locality. Treat numbers as relative indicators, not absolutes. For a quick snapshot of LLM API providers 2025, compare pricing, TTFT, context windows, and model breadth above.
Where ShareAI Fits Among LLM API Providers 2025
People-powered marketplace: 150+ models, flexible routing, no lock-in
ShareAI aggregates top models (OSS and proprietary) behind one OpenAI-compatible API. Route per-request by model name or by policy (cheapest, fastest, most accurate for a task), fail over automatically when a region or model blips, and swap models with one line—without rewriting your app. Tour the Console Overview.
Cost control & observability by default
Get real-time token, latency, error, and cost tracking at the request and user level. Break down by provider/model to catch regressions and optimize routing policies. Procurement-friendly reporting includes usage trends, unit economics, and audit trails. Among LLM API providers 2025, ShareAI acts as the control plane with routing, failover, observability, and BYOI.
One API, many providers: zero-switching friction
ShareAI uses an OpenAI-compatible interface so you can keep your SDKs. Credentials stay scoped; bring your own keys where required. No lock-in: your prompts, logs, and routing policies are portable. When you’re ready to ship, check the latest Release Notes.
Try it in 5 minutes (builder-first code)
curl -s https://api.shareai.now/api/v1/chat/completions \
-H "Authorization: Bearer $SHAREAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek-r1:70b",
"messages": [
{"role":"system","content":"You are a concise assistant."},
{"role":"user","content":"Summarize the key trade-offs of LPU vs GPU for LLM inference."}
],
"temperature": 0.2,
"stream": false
}'To trial LLM API providers 2025 without refactors, route via ShareAI’s OpenAI-compatible endpoint above and compare outcomes in real time.
How to Choose the Right LLM API Provider (2025)
Decision matrix (latency, cost, privacy, scale, model access)
Latency-critical chat/agents: Groq, Fireworks, Together; or ShareAI routing to the fastest per region.
Cost-sensitive batch: Hyperbolic, Novita, DeepInfra; or ShareAI cost-optimized policy.
Model diversity / rapid switching: OpenRouter; or ShareAI multi-provider with failover.
Enterprise governance: Anyscale (Ray), DeepInfra (dedicated), plus ShareAI reports & auditability.
Multimodal (text+image+audio): Fireworks, Together, Replicate; ShareAI can route across them. For deeper setup, start at the Docs Home.
Teams short-listing LLM API providers 2025 should test in their serving region to validate TTFT and cost.
Workloads: chat apps, RAG, agents, batch, multimodal
Chat UX: prioritize TTFT and tokens/sec; streaming stability matters.
RAG: embeddings quality + window size + cost.
Agents/tools: robust function-calling; timeout controls; retries.
Batch/offline: throughput and $ per 1M tokens dominate.
Multimodal: model availability and cost of non-text tokens.
Procurement checklist (SLA, DPA, region, data retention)
Confirm SLA targets and credits, DPA terms (processing, sub-processors), region selection, and retention policy for prompts/outputs. Ask for observability hooks (headers, webhooks, export), fine-tune data controls, and BYOK/BYOI options if needed. See the Provider Guide if you plan to bring capacity.
Top 12 LLM API Providers 2025
Each profile includes a “best for” summary, why builders pick it, pricing at a glance, and notes on how it fits alongside ShareAI. These are the LLM API providers 2025 most often evaluated for production.
1) ShareAI — best for multi-provider routing, observability & BYOI
Why builders pick it: one OpenAI-compatible API across 150+ models, policy-based routing (cost/latency/accuracy), auto-failover, real-time cost & latency analytics, and BYOI when you need dedicated capacity or compliance control.
Pricing at a glance: follows the routed provider’s pricing; you choose cost-optimized or latency-optimized policies (or a specific provider/model).
Notes: ideal “control plane” for teams that want freedom to switch providers without refactors, keep procurement happy with usage/cost reports, and benchmark in production.
2) Together AI — best for high-scale open-source LLMs
Why builders pick it: excellent price/performance on OSS (e.g., Llama-3 class), fine-tuning support, sub-100ms claims, broad catalog.
Pricing at a glance: per-token by model; free credits may be available for trials.
ShareAI fit: route via together/<model-id> or let a ShareAI cost-optimized policy choose Together when it’s cheapest in your region.
3) Fireworks AI — best for low-latency multimodal
Why builders pick it: very fast TTFT, FireAttention engine, text+image+audio, SOC2/HIPAA options.
Pricing at a glance: pay-as-you-go (serverless or on-demand).
ShareAI fit: call fireworks/<model-id> directly or let policy routing select Fireworks for multimodal prompts.
4) OpenRouter — best for one-API access to many providers
Why builders pick it: ~300+ models behind a unified API; good for quick model exploration.
Pricing at a glance: per-model pricing; some free tiers.
ShareAI fit: ShareAI covers the same multi-provider need but adds policy routing + observability + procurement-grade reports.
5) Hyperbolic — best for aggressive cost savings & rapid model rollout
Why builders pick it: consistently low per-token prices, quick turn-up for new open-source models, and access to affordable GPUs for heavier jobs.
Pricing at a glance: free to start; pay-as-you-go.
ShareAI fit: point traffic to hyperbolic/ for lowest-cost runs, or set a custom policy (e.g., “cost-then-latency”) so ShareAI prefers Hyperbolic but auto-switches to the next cheapest healthy route during spikes.
6) Replicate — best for prototyping & long-tail models
Why builders pick it: huge community catalog (text, image, audio, niche models), one-line deploys for quick MVPs.
Pricing at a glance: per-inference; varies by model container.
ShareAI fit: great for discovery; when scaling, route via ShareAI to compare latency/cost against alternatives without code changes.
7) Hugging Face — best for OSS ecosystem & enterprise bridges
Why builders pick it: model hub + datasets; hosted inference or self-host on your cloud; strong enterprise MLOps bridges.
Pricing at a glance: free for basics; enterprise plans available.
ShareAI fit: keep your OSS models and route through ShareAI to mix HF endpoints with other providers in one app.
8) Groq — best for ultra-low latency (LPU)
Why builders pick it: hardware-accelerated inference with industry-leading TTFT/tokens-per-second for chat/agents.
Pricing at a glance: per-token; enterprise-friendly.
ShareAI fit: use groq/<model-id> in latency-sensitive paths; set ShareAI failover to GPU routes for resilience.
9) DeepInfra — best for dedicated hosting & cost-efficient inference
Why builders pick it: stable API with OpenAI-style patterns; dedicated endpoints for private/public LLMs.
Pricing at a glance: per-token or execution time; dedicated instance pricing available.
ShareAI fit: helpful when you need dedicated capacity while keeping cross-provider analytics via ShareAI.
10) Perplexity (pplx-api) — best for search/QA integrations
Why builders pick it: fast access to new OSS models, simple REST API, strong for knowledge retrieval and QA.
Pricing at a glance: usage-based; Pro often includes monthly API credits.
ShareAI fit: mix pplx-api for retrieval with another provider for generation under one ShareAI project.
11) Anyscale — best for end-to-end scaling on Ray
Why builders pick it: training → serving → batch on Ray; governance/admin features for enterprise platform teams.
Pricing at a glance: usage-based; enterprise options.
ShareAI fit: standardize infra on Ray, then use ShareAI at the application edge for cross-provider routing and unified analytics.
12) Novita AI — best for serverless + dedicated GPU at low cost
Why builders pick it: per-second billing, quick cold starts, global GPU network; both serverless and dedicated instances.
Pricing at a glance: per-token (LLM) or per-second (GPU); dedicated endpoints for enterprise.
ShareAI fit: strong for batch cost savings; keep ShareAI routing to pivot between Novita and peers by region/price.
Quick Start: Route Any Provider Through ShareAI (Observability Included)
OpenAI-compatible example (chat completions)
curl -s https://api.shareai.now/api/v1/chat/completions \
-H "Authorization: Bearer $SHAREAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek-r1:70b",
"messages": [
{"role":"system","content":"You are brief and accurate."},
{"role":"user","content":"Explain TTFT vs tokens/sec for LLM UX."}
],
"temperature": 0.2,
"stream": false
}'Switching providers with one line
{
"model": "growably/deepseek-r1:70b",
"messages": [
{"role": "user", "content": "Latency matters for agents—explain why."}
]
}To trial LLM API providers 2025 quickly, keep the same payload and just swap the model or choose a router policy.
Benchmark Notes & Caveats
Tokenization differences change total token counts between providers.
Batching and caching can make TTFT look unrealistically low on repeated prompts.
Server locality matters: measure from the region you serve users.
Context window marketing isn’t the full story—look at truncation behavior and effective throughput near the limits.
Pricing snapshots: always verify current pricing before committing. When you’re ready, consult the Releases and Blog Archive for updates.
FAQ: LLM API Providers 2025
What is an LLM API provider?
An LLM API provider offers inference-as-a-service access to large language models via HTTP APIs or SDKs. You get scalability, monitoring, and SLAs without managing your own GPU fleet.
Open-source vs proprietary: which is better for production?
Open-source (e.g., Llama-3 class) offers cost control, customization, and portability; proprietary models may lead on certain benchmarks and convenience. Many teams blend both—ShareAI makes that mix-and-match routing trivial.
Together AI vs Fireworks — which is faster for multimodal?
Fireworks is known for low TTFT and a strong multimodal stack; Together offers a broad OSS catalog and competitive throughput. Your best choice depends on prompt size, region, and modality. With ShareAI, you can route to either and measure real outcomes.
OpenRouter vs ShareAI — marketplace vs people-powered routing?
OpenRouter aggregates many models via one API—great for exploration. ShareAI adds policy-based routing, procurement-friendly observability, and people-powered curation so teams can optimize cost/latency and standardize reporting across providers.
Groq vs GPU Cloud — when does LPU win?
If your workload is latency-critical (agents, interactive chat, streaming UX), Groq LPUs can deliver industry-leading TTFT/tokens-per-second. For compute-heavy batch jobs, cost-optimized GPU providers may be more economical. ShareAI lets you use both.
DeepInfra vs Anyscale — dedicated inference vs Ray platform?
DeepInfra shines for dedicated inference endpoints; Anyscale is a Ray-native platform spanning training to serving to batch. Teams often use Anyscale for platform orchestration and ShareAI at the application edge for cross-provider routing and analytics.
Novita vs Hyperbolic — lowest cost at scale?
Both pitch aggressive savings. Novita emphasizes serverless + dedicated GPUs with per-second billing; Hyperbolic highlights discounted GPU access and fast model onboarding. Test both with your prompts; use ShareAI’s router:cost_optimized to keep costs honest.
Replicate vs Hugging Face — prototyping vs ecosystem depth?
Replicate is perfect for rapid prototyping and long-tail community models; Hugging Face leads the OSS ecosystem with enterprise bridges and options to self-host. Route either via ShareAI to compare apples-to-apples on cost & latency.
What’s the most cost-effective LLM API provider in 2025?
It depends on prompt mix and traffic shape. Cost-focused contenders: Hyperbolic, Novita, DeepInfra. The reliable way to answer is to measure with ShareAI observability and a cost-optimized routing policy.
Which provider is the fastest (TTFT)?
Groq frequently leads on TTFT/tokens-per-second, especially for chat UX. Fireworks and Together are also strong. Always benchmark in your region—and let ShareAI route to the fastest endpoint per request.
Best provider for RAG/agents/batch?
RAG: larger context + quality embeddings; consider Together/Fireworks; mix with pplx-api for retrieval. Agents: low TTFT + reliable function calling; Groq/Fireworks/Together. Batch: cost wins; Novita/Hyperbolic/DeepInfra. Route with ShareAI to balance speed and spend.
Final Thoughts
If you’re choosing among LLM API providers 2025, don’t pick on price tags and anecdotes alone. Run a 1-week bake-off with your actual prompts and traffic profile. Use ShareAI to measure TTFT, throughput, errors, and cost per request across providers—then lock in a routing policy that matches your goals (lowest cost, lowest latency, or a smart blend). When things change (and they will), you’ll already have the observability and flexibility to switch—without refactoring.
