# Llama 3.1 API Rate Limits, Pricing & Performance (July 2026)

- Tool: AI API Limits & Performance Matrix
- Last updated: July 2026

## TL;DR

Llama 3.1 is a legacy open-weights model with no first-party API. Access is via third-party hosts (Groq, Together AI, DeepInfra, Fireworks, Amazon Bedrock). Databricks retired the 405B variant on February 15, 2026. 70B and 8B remain callable, but host availability is thinning. Migrate to Llama 4 Scout ($0.08/MTok on DeepInfra, 10M native context) for new projects.

## Frequently asked questions

### Is Llama 3.1 still available in 2026?

Llama 3.1 8B and 70B are still callable on major hosts including Groq, Together AI, DeepInfra, Fireworks, and Amazon Bedrock as of July 2026. However, the 405B variant was retired by Databricks on February 15, 2026, and availability of all variants is thinning. For new projects, Llama 4 Scout or Maverick are the recommended choices.

### Is Llama 3.1 API free?

There is no first-party Meta-hosted Llama 3.1 API. Groq offers a free tier with approximately 30 RPM for Llama 3.1 8B and 70B. Paid hosts like Together AI, DeepInfra, and Fireworks charge per million tokens — rates vary from ~$0.06/MTok (8B on Fireworks) to ~$0.88/MTok blended (70B on Together, per third-party trackers; confirm in your dashboard).

### How do I increase Llama 3.1 rate limits?

Rate limits for Llama 3.1 are set by your inference host, not Meta. To increase limits: upgrade from Groq free to a paid plan; add credit balance on Together AI, DeepInfra, or Fireworks; or request a quota increase on Amazon Bedrock via AWS Service Quotas (1–2 business days). For the highest and most stable limits, migrate to Llama 4 Scout or Maverick which have broader host support.

### What happened to Llama 3.1 405B?

Databricks retired Meta-Llama-3.1-405B-Instruct on February 15, 2026. Some other hosts still list it, but availability is thinning. If you need 405B-class reasoning capability, Llama 4 Maverick (400B total parameters, 17B active via MoE architecture) is the recommended replacement — it outperforms the dense 405B on many benchmarks at lower per-token cost.

### Llama 3.1 vs Llama 4 Scout: which should I use?

Llama 4 Scout for almost all new projects. It costs ~77% less on input ($0.08 vs ~$0.35 per MTok on DeepInfra), supports a 10M native context window (vs 128K for 3.1), and adds multimodal (text + image) support. Llama 3.1 has a slight edge only in existing integrations where migration cost is not justified yet.

### Does Llama 3.1 support caching?

No native cross-host prompt caching exists for Llama 3.1. Amazon Bedrock does not offer Llama-specific prompt caching. You can implement application-layer caching by storing and reusing system prompt tokens in your backend, typically reducing repeated token spend by 20–40%.

### What does a ThrottlingException mean on Amazon Bedrock for Llama 3.1?

ThrottlingException on Bedrock means your account has reached its per-model throughput quota. To fix it: go to AWS Console → Service Quotas → Amazon Bedrock → find the Llama 3.1 model → request a quota increase. This typically takes 1–2 business days. In the meantime, implement exponential backoff and honor the Retry-After header. If you need help sizing your Bedrock quota correctly, RapidDev offers free scoping calls at rapidevelopers.com/contact.

---

Source: https://www.rapidevelopers.com/ai-api-limits-performance-matrix/llama-3-1
© RapidDev — https://www.rapidevelopers.com/ai-api-limits-performance-matrix/llama-3-1