LLM Providers 6 products

How to choose an LLM provider without surprises?

Hosted frontier APIs win for speed and general capability. Open-weight models win for deployment control and vendor flexibility—but require ops and eval discipline.

How to use this page — start with the category truths, then open a product brief, and only compare once you have two candidates.
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Constraints first Pricing behavior Trade-offs

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LLM provider decision finder

Choose based on deployment constraints and cost drivers first. Then test 2–3 candidates with your eval harness and real prompts.

Decision finder

Do you have strict deployment controls?

What’s the primary workload?

What’s your main cost risk?

Pick answers to see a recommended starting path

This is a decision brief site: we optimize for operating model + cost/limits + what breaks first (not feature checklists).

Build your shortlist

Narrow your LLM provider shortlist by deployment model, primary workload, and cost sensitivity.

Select at least one filter

Freshness

Last updated: 2026-02-09T02:34:35Z
Dataset generated: 2026-01-14T00:00:00Z
Method: source-led, decision-first (cost/limits + trade-offs)

2026-02-09 — SEO metadata quality pass

Refined SEO titles and meta descriptions for search quality. Removed mirrored Claude vs OpenAI comparison (kept canonical direction).

2026-02-06 — Added LLM decision finder and freshness

Added a decision finder centered on deployment constraints and cost drivers (context + retrieval), plus a freshness section for trust.

2026-01-14 — Reframed category around deployment + pricing mechanics

Shifted the category verdict away from ‘best model’ language toward controllability, deployment constraints, and eval discipline.

See all updates →

Top picks in LLM Providers

These are commonly short‑listed options based on constraints, pricing behavior, and operational fit — not review scores.

OpenAI (GPT-4o)

Frontier model platform for production AI features with strong general capability and multimodal support; best when you want the fastest path to high-quality re…

Anthropic (Claude 3.5)

Hosted frontier model often chosen for strong reasoning and long-context performance with a safety-forward posture for enterprise deployments.

Google Gemini

Google’s flagship model family, commonly chosen by GCP-first teams that want cloud-native governance and adjacency to Google Cloud services.

Meta Llama

Open-weight model family enabling self-hosting and vendor flexibility; best when deployment control and cost governance outweigh managed convenience.

Mistral AI

Model provider with open-weight and hosted options, often shortlisted for portability, cost efficiency, and EU alignment while retaining a managed path.

Perplexity

AI search product focused on answers with citations, often compared to raw model APIs when the decision is search UX versus orchestration control.

Pricing and availability may change. Verify details on the official website.

Most common decision mistake: Comparing LLM providers on benchmark scores instead of the rate limits, token pricing at volume, latency percentiles, and fine-tuning constraints that determine production viability.

Popular head-to-head comparisons

Use these when you already have two candidates and want the constraints and cost mechanics that usually decide fit.

Both are default hosted frontier APIs; buyers choose based on capability profile, safety posture, tooling, and cost behavior under…
Buyers compare OpenAI and Gemini when choosing a hosted provider and balancing general API portability against GCP-native governance and…
Buyers compare hosted OpenAI APIs to Llama when deployment constraints or vendor flexibility become more important than managed convenience
Buyers compare Claude and Gemini when choosing a hosted provider and weighing reasoning behavior and safety posture against cloud-native…
Buyers compare Llama and Mistral when choosing an open-weight model direction and evaluating capability, portability, and ops ownership
Buyers compare OpenAI and Mistral when they want frontier quality but are exploring open-weight or portability-driven alternatives for…
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How to choose the right LLM Providers platform

Hosted frontier APIs vs open-weight deployment control

Hosted APIs ship fastest with managed reliability, but constrain deployment and increase vendor dependence. Open-weight models increase control, but shift infra, safety, and evaluation onto your team.

Questions to ask:

  • Do you need VPC/on-prem or strict data residency constraints?
  • Can your team own inference ops, monitoring, and model upgrades?
  • Do you have an eval harness to catch regressions across changes?

Pricing mechanics (context + retrieval) and controllability

Token spend is often driven by long context, retrieval, tool traces, and verbose outputs. Some products optimize for AI search UX; raw APIs maximize orchestration control but require more engineering.

Questions to ask:

  • What drives your cost: context length, retrieval size, tool calls, or volume?
  • Do you need strict structured outputs and deterministic automation?
  • Is your product goal AI search UX or a custom agent/workflow?

How we evaluate LLM Providers

🛡️

Source-Led Facts

We prioritize official pricing pages and vendor documentation over third-party review noise.

🎯

Intent Over Pricing

A $0 plan is only a "deal" if it actually solves your problem. We evaluate based on use‑case fitness.

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Durable Ranges

Vendor prices change daily. We highlight stable pricing bands to help you plan your long-term budget.