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Tier: open-weight Context: 131K Cheapest Host: DigitalOcean GPU Droplets ($200 Free Credit)

Llama 3.1 405B Instruct Cloud Pricing, API Cost Calculator & Cheapest Providers

Live unit economics and multi-provider host arbitrage for Llama 3.1 405B Instruct (meta-llama/llama-3.1-405b-instruct). Input tokens cost $0.80/1M, output tokens cost $0.80/1M, and prompt cache reads drop to $0.40/1M.

Input / Output / Cache Read
$0.80 / $0.80
Per 1M tokens · Cache Read: $0.40/1M
Get DigitalOcean GPU Droplets ($200 Free Credit) API Key →
10,000 DAU Monthly Bill
$12,384/mo
Drops to $8,604/mo at 75% prompt cache hit rate
Export CFO .xlsx Audit ($29) →
Speed & Context Spec
92 tok/s
Context: 131,072 · Max Out: 16,384
Claim $200 DigitalOcean GPU Credit → · RunPod H100 GPUs →

1. Cheapest Cloud Providers for Llama 3.1 405B Instruct (2026 Cloud Pricing & Latency Matrix)

Compare the cheapest cloud providers and GPU endpoints hosting Llama 3.1 405B Instruct anchored by input price, output price, cache read discount, Time-to-First-Token (TTFT < 400ms SLA), and throughput speed:

Provider / Endpoint Input / 1M Output / 1M Cache Read / 1M TTFT Latency & SLA Throughput 10K DAU Monthly Bill Action
DigitalOcean GPU Droplets ($200 Free Credit) #1 Recommended Router $0.80 $0.80 $0.40 326ms <400ms SLA ✓ 86 tok/s $12,384/mo Deploy ($200 Free 60-Day Cloud Credit) →
Vultr Cloud GPU ($300 Free Credit) $0.80 $0.80 $0.40 333ms <400ms SLA ✓ 84 tok/s $12,384/mo Deploy ($300 Free GPU Cloud Credit) →
RunPod Serverless vLLM ($5–$500 Bonus) $0.80 $0.80 $0.40 354ms <400ms SLA ✓ 79 tok/s $12,384/mo Deploy ($5–$500 Free Bonus Credit) →
DeepInfra $0.80 $0.80 $0.40 438ms Batch/Async 64 tok/s $12,384/mo Deploy (Get $50 Free API Credits) →
Fireworks AI $3.00 $3.00 $1.50 304ms <400ms SLA ✓ 92 tok/s $46,440/mo Deploy ($50 Free FireAttention Credits) →
Together AI $3.50 $3.50 $1.75 359ms <400ms SLA ✓ 78 tok/s $54,180/mo Deploy ($25 Free Inference Credits) →
⚡ Live Provider Disparity Anchor: DigitalOcean GPU Droplets ($200 Free Credit) delivers Llama 3.1 405B Instruct at $0.80/1M input while maintaining a real-time <400ms TTFT SLA (92 tok/s peak).
📣 Host Llama 3.1 405B Instruct? Pin #1 Router Slot ($299/mo) → ⚡ Claim Global Smart Router Gateway Slot ($499/mo) →

2. Monthly SaaS Spend by DAU Scale & Prompt Cache Hit Rate

Exact monthly spend projections across 1,000 DAU, 10,000 DAU, and 50,000 DAU with 0%, 50%, 75%, and 90% prompt caching:

Active Users (DAU) Uncached (0% Hit) 50% Cache Hit 75% Cache Hit (RAG) 90% Cache Hit (Agents)
1,000 DAU (1.55B tok/mo) $1,238/mo $986.40/mo $860.40/mo $784.80/mo
10,000 DAU (15.5B tok/mo) $12,384/mo $9,864/mo $8,604/mo $7,848/mo
50,000 DAU (77.4B tok/mo) $61,920/mo $49,320/mo $43,020/mo $39,240/mo

3. Frequently Asked Questions (Llama 3.1 405B Instruct)

What are the cheapest cloud providers for Llama 3.1 405B Instruct in 2026?
For Llama 3.1 405B Instruct (meta-llama/llama-3.1-405b-instruct), the cheapest cloud providers and GPU endpoints among 6 verified hosts are DigitalOcean GPU Droplets ($200 Free Credit), Vultr Cloud GPU ($300 Free Credit), RunPod Serverless vLLM ($5–$500 Bonus), starting at $0.80 per 1M input tokens, $0.80 per 1M output tokens, and $0.40 per 1M cached input reads (86 tok/s throughput). Teams can also claim $200 in free DigitalOcean GPU Droplet credits, $300 in Vultr Cloud GPU credits, or $5-$500 in RunPod Serverless vLLM bonus credits.
What is the Llama 3.1 405B Instruct cloud pricing per 1M tokens?
Llama 3.1 405B Instruct (DeepInfra) cloud pricing is $0.80 per 1M input tokens, $0.80 per 1M output tokens, and $0.40 per 1M cached input tokens. Its context window is 131K (131,072 tokens) with 16,384 max output tokens.
How much does Llama 3.1 405B Instruct cost per month at 1,000, 10,000, and 50,000 DAU?
On a standard SaaS production workload (12 requests/user/day, 3,500 input + 800 output tokens), Llama 3.1 405B Instruct costs $1,238/month at 1,000 DAU, $12,384/month at 10,000 DAU, and $61,920/month at 50,000 DAU. Enabling 75% prompt caching reduces the 10,000 DAU bill to $8,604/month.

4. Compare Llama 3.1 405B Instruct Against Alternative Models