Compare providers in one decision flow
We bring pricing notes, model support, OpenAI compatibility, and trust signals into one place so research takes hours, not days.
LLM API provider directory + cost calculator + trust checklist
Find the best LLM endpoint for your AI app.
Compare official LLM APIs, OpenAI-compatible providers, aggregators, pricing, capabilities, and transparency signals in one place.
Start from the decision you need to make
These entry points are built for real evaluation workflows: migration, budget planning, shortlisting, and production review.
Use case
I need a fast shortlist for my app
Answer a few practical questions and get a recommended provider category with tradeoffs.
MigrationI want OpenAI-compatible alternatives
Find providers that keep integration friction low while giving you more cost or routing options.
BudgetI need to estimate cost before launch
Model monthly spend from request volume, tokens, users, and cache assumptions before you ship.
ShortlistI want a safer production shortlist
Start from a curated list of providers with stronger docs, trust signals, and operational fit.
What this site helps you do
LLMEndpoint is a practical LLM API directory for developers comparing official model APIs, OpenAI-compatible endpoints, aggregators, and inference providers. Use it to shortlist vendors, compare pricing, review model support, estimate monthly API cost, and spot public trust or transparency gaps before procurement or launch. The goal is not to flood you with generic AI content. It is to help you make a faster, safer endpoint decision for a real product workflow.
Why teams use LLMEndpoint
The goal is not just more pages. It is helping developers narrow options faster and avoid obvious evaluation mistakes.
Start from your workflow, not hype
The site is organized around launch decisions like migration, cost control, fallback, and provider shortlisting.
Spot gaps before procurement
Every provider includes public-information checks so teams can quickly see where pricing, status, policy, or sourcing details are unclear.
Choose the right LLM API before you ship.
Start with the provider category, then narrow by models, capabilities, pricing, and transparency.
Official APIs
First-party model APIs from major AI labs
Teams that need official access, predictable documentation, enterprise procurement, and lower vendor ambiguity.
Inference ProvidersHosted inference platforms for open and proprietary models
Builders who want open model choice, fast inference, or infrastructure options without running their own GPUs.
LLM API AggregatorsUnified APIs
Teams that need fallback, usage tracking, model routing, provider optionality, or OpenAI-compatible access across vendors.
OpenAI-Compatible APIsThird-party or independent APIs that expose OpenAI-style endpoints for easier migration and lower integration friction.
Developers who want to reuse OpenAI SDKs, test alternatives quickly, or optimize cost while keeping an existing integration shape.
Popular decision paths
Choose the entry that sounds closest to your team, then move from research to a usable shortlist faster.
For indie hackers
Ship faster without overbuilding vendor abstraction
Start with one reliable provider, keep spend bounded, and add cheaper routes only after users and usage patterns are real.
For startupsBuild a shortlist you can defend to product, finance, and security
Use an official API, a compatible option, and a lower-cost fallback to avoid locking the team into one assumption too early.
For agentsPrioritize tools, structured output, and fallback early
Agent workflows break on brittle schemas and hidden operational gaps, so compare more than just model quality.
For RAG and searchControl long-context and retrieval cost before traffic scales
RAG systems can look cheap in demos and expensive in production once long prompts, retries, and verbose outputs stack up.
Featured comparison
A compact view of popular providers across the current dataset.
| Provider | Category | Supported models | OpenAI-compatible | Starting price | Context | Tool calling | Vision | Streaming | Status | Trust | Links |
|---|---|---|---|---|---|---|---|---|---|---|---|
| OpenAI | Official APIs | GPT, reasoning models, embeddings, image | Yes | Budget to premium GPT tiers | Short to very long, model based | Yes | Yes | Yes | Available | 12/15 | |
| Anthropic | Official APIs | Claude, Claude Haiku, Claude Sonnet, Claude Opus | No | Mid to premium Claude tiers | Long context options | Yes | Yes | Yes | Available | 10/15 | |
| Google Gemini | Official APIs | Gemini, embedding models, multimodal models | Yes | Low-cost flash to premium tiers | Short to million-token-class options | Yes | Yes | Yes | Available | 11/15 | |
| Mistral AI | Official APIs | Mistral, Mixtral, Codestral, embeddings | Yes | Open and premium model tiers | Short to long, model based | Yes | No | Yes | Available | 11/15 | |
| DeepSeek | Official APIs | DeepSeek-V4-Flash, DeepSeek-V4-Pro | Yes | Low-cost flash to discounted pro tiers | 1M context, up to 384K output | Yes | No | Yes | Available | 11/15 | |
| xAI | Official APIs | Grok | Yes | Frontier-model pricing tiers | Mid to long, model based | Yes | Yes | Yes | Available | 11/15 | |
| Cohere | Official APIs | Command, Embed, Rerank | No | Enterprise and task-specific tiers | Task and model based | Yes | No | Yes | Available | 10/15 | |
| Together AI | Inference Providers | Llama, Qwen, DeepSeek-V4, Mistral | Yes | Often competitive for open models | Broad open-model range | No | Yes | Yes | Available | 11/15 | |
| Fireworks AI | Inference Providers | Llama, Qwen, DeepSeek-V4, Mistral | Yes | Competitive serverless tiers for open models | Broad open-model range, model specific | No | Yes | Yes | Available | 11/15 | |
| Groq | Inference Providers | Llama, Mixtral, Gemma, Whisper-like speech models | Yes | Speed-oriented model tiers | Selected fast-serving model range, model specific | Yes | No | Yes | Available | 11/15 |
OpenAI-compatible endpoints
Use familiar SDK patterns while evaluating cost, speed, and trust tradeoffs.
Endpoint trust signals
Every listing includes a public-information transparency checklist.
Company name visible
Terms of service available
Privacy policy available
Data retention stated
Pricing page available
Status page available
Rate limits documented
Support channel available
Estimate API costs before launch.
Model monthly spend from requests, input/output tokens, users, and cache hit rate.
If you are migrating from OpenAI
Start with compatible providers, then test real differences before moving production traffic.
If you already have a shortlist
Move from rough research into a decision memo with side-by-side comparisons and cost checks.
Popular guides
Fast, practical explainers for developers choosing endpoints.
Guide
What Is an LLM Endpoint?
A practical definition of endpoints, APIs, models, providers, and where OpenAI-compatible interfaces fit.
GuideOfficial LLM APIs vs OpenAI-Compatible Providers
How to compare official APIs, inference platforms, aggregators, and third-party endpoints.
GuideHow to Choose an LLM API for Your AI App
A developer checklist for quality, cost, speed, context length, tool use, and trust.
GuideLLM API Pricing Explained
Input tokens, output tokens, requests, caching, and the hidden cost drivers.
Popular provider reviews
Start here if you already know which API vendor is in the shortlist.
Popular provider comparisons
Useful when the real decision is choosing between two serious candidates.
FAQ
What is the best LLM API for a new product?
For many teams, the best starting point is one strong official API plus one realistic fallback. The right answer depends on whether your product prioritizes coding, long context, multimodal input, speed, or low-cost scale.
How should I compare LLM API providers?
Compare providers by running the same workflow across pricing, output quality, tool support, latency, OpenAI compatibility, and trust signals. A good shortlist is usually easier to defend than a single-provider guess.
Are OpenAI-compatible APIs safe to use in production?
They can be, but compatibility alone is not enough. You still need to review data handling, rate limits, support, and edge-case behavior before sending real user traffic.
What is the fastest way to shortlist LLM endpoints?
Start with your use case, then narrow by provider category, compare two or three serious candidates, and estimate monthly cost before launch.
Get weekly endpoint updates.
Pricing changes, new models, trust signal updates, and practical shortlist notes for developers choosing production LLM APIs.