There is no single "Llama API price”, and the reason matters. Meta has wound down its own first-party Llama API and it now points developers to third-party providers that host the models instead. So the real question behind "llama api pricing" is not what Meta charges. It is what Groq, DeepInfra, Fireworks, Together and the hyperscalers charge to run the same open weights for you.

That is good news for your budget and bad news for a clean answer. The same Llama model can cost three to twelve times more on one host than another and the sticker rate per million tokens is only the start. This guide pulls current rates from each provider's pricing page, shows the per-model spread and walks through where teams lose money no rate card predicts.

Why Llama API Pricing Works Differently

Most model APIs come from the company that built the model. You want GPT, you pay OpenAI. Llama breaks that pattern. Meta releases the weights under an open community license, then leaves hosting to a competitive market of inference providers. That split is the foundation of sane finops for ai, because your provider choice, not Meta, sets your rate.

Two paths follow from open weights:

• Hosted API: You rent inference from a provider and pay per token. No infrastructure to manage.

• Self-hosting: You run the open weights on your own GPUs and pay for compute by the hour, whatever the token volume.

Both are legitimate and they bill on different units, which is where the cost confusion starts.

What You Are Actually Paying For

Every Llama host bills in tokens, so the primer on what is a token in ai is worth a read before you compare rates. A token is a chunk of text, roughly four characters or three-quarters of an English word. Hosts count tokens on the way in and on the way out and they price the two streams separately.

Input covers your prompt, system instructions and any conversation history you resend. Output covers the model's response. Output usually costs more, though several Llama hosts charge the same for both, which changes how you optimize. That difference matters enough to deserve its own breakdown in input vs output token pricing, since verbose responses and bloated context drive spend in different ways.

Llama 3.3 70B API Pricing by Provider

Llama 3.3 70B is the workhorse model, the one most teams compare first. The table below shows current published rates per million tokens, pulled from each provider's own pricing page.

Rates come from each provider's own pricing page, cross-checked against a live per-provider Llama price tracker. The spread is the story. DeepInfra runs about ten times cheaper per input token than Together for the same model, while Groq sits mid-price and leads on speed. Teams that route across hosts usually price them through one layer, which the openrouter pricing reference lays out in full.

Pricing Across Llama Model Sizes

Model size drives cost far more than provider choice. An 8B model runs a fraction of a cent per million tokens; 405B can cost a hundred times more. The table below shows the cheapest verified path for each common Llama size.

The 405B model shows the widest gap. The same weights run about $0.80 per million tokens on the cheapest host and around $9.50 on the priciest, roughly a twelvefold spread, so model size and the input/output split sit at the center of token economics for any Llama team. Host choice for a 405B workload beats any prompt optimization. One accuracy note most pages miss: Together has removed 405B from its serverless API, so any $3.50 figure still quoted for it is stale.

Enterprise and Hyperscaler Paths

Regulated teams often need Llama inside AWS or Azure, not a startup provider. Third-party trackers list Llama 3.3 70B on Amazon Bedrock around $0.72 per million tokens and on Azure AI Foundry near $0.59 to $0.79. Confirm the live figure in each console before you forecast, since these pages update quietly.

The hyperscaler premium buys compliance, private networking, and a single vendor, not a cheaper token. On Azure, Llama charges pass through Meta via the Marketplace and may not draw down a committed spend agreement the way native services do, so a model priced through ai gpu pricing mechanics behaves differently on the invoice.

The Costs the Rate Card Does Not Show

The per-token rate is the floor, not the bill. Four line items inflate it, none of them in the headline figure:

• Aggregator markup: Routers add a platform fee on top of the provider rate, so the cheapest listed price is not always the cheapest paid.

• Quantization: The lowest rate is often a compressed FP8 or FP4 endpoint that underperforms the full-precision version, so cheap tokens can buy weaker answers.

• Fine-tuning: Serving a base model is cheap; training a custom one is not. Together lists LoRA fine-tuning from $3 to $8 per million tokens by size.

• Rate limits: The cheapest tier often caps throughput and lifting the cap can mean a commit or a higher rate.

Reading what is inference cost at the endpoint level separates a real quote from a headline. Batch processing pulls the other way and cuts cost hard on non-real-time jobs.

Self-Hosting Llama vs Paying Per Token

Because the weights are open, self-hosting looks free next to a per-token bill. It is not. You trade a usage-based rate for a fixed GPU cost that runs whether you process zero tokens or four billion. A 405B model needs hardware in the class of eight H100s and the meter never stops.

The math turns on utilization. One widely cited break-even sits near 50 million tokens a day. Below that, the rented GPU sits idle most of the month while you pay full price for it. Practitioners make the point bluntly: the "much cheaper to self-host" claim assumes near-total GPU utilization that production never hits. Catching that waste is the job of gpu usage monitoring and it decides whether self-hosting actually saves money.

Estimating Your Llama API Bill With Real Examples

The formula is simple: multiply input tokens by the input rate over one million, do the same for output, then add any tool or fine-tuning cost. The examples below use verified rates and scale linearly with volume.

Example 1: A support chatbot on the cheap path. 500 users, 30 messages each per month, averaging 1,000 input and 300 output tokens (15M input / 4.5M output). On DeepInfra's Llama 3.3 70B Turbo at $0.10 / $0.32:

• Input: 15M x $0.10 / 1M = $1.50

• Output: 4.5M x $0.32 / 1M = $1.44

• Monthly total: about $3

Example 2: The same chatbot on a premium host. Identical 15M input / 4.5M output tokens, run on Together at $1.04 flat:

• Input: 15M x $1.04 / 1M = $15.60

• Output: 4.5M x $1.04 / 1M = $4.68

• Monthly total: about $20, roughly seven times the DeepInfra bill for the same work

Example 3: A 405B reasoning job of 10M tokens. Same model, same tokens, host choice alone:

• Cheapest host (DeepInfra at $0.80): 10M x $0.80 / 1M = $8

• Priciest host (about $9.50): 10M x $9.50 / 1M = $95

Example 4: A real production figure. One developer reported running about 3 million tokens through Llama 3.3 70B for roughly $2.07 total, near half a cent per request. Small Llama models are genuinely cheap at moderate volume.

These examples price inference only. The data layer behind an AI feature, like a managed database, carries a separate bill that belongs in the same forecast, which the mongodb atlas pricing guide breaks down.

How Llama Pricing Compares to Closed Models

Llama's core positioning is price. Its hosted rates sit well below the closed frontier models, which is why cost-sensitive teams reach for it. The comparison below uses published input and output rates per million tokens.

Closed-model output rates run several times higher than Llama's, so verbose and agentic workloads save the most on the open model. Treat the GPT and Claude figures as directional ranges. For the full side-by-side across every option, the llm cost comparison breaks each one down.

How to Control Llama API Costs

Pick the right model and host for each job first. That single decision moves the bill more than anything else. The levers that matter most:

• Route by difficulty: Send bulk work to an 8B model on the cheapest reliable host, reserve 70B for quality-sensitive tasks and keep 405B for genuinely hard reasoning.

• Batch the non-urgent: Groq and Fireworks both cut cost by half on batched, non-real-time jobs.

• Cache stable prompts: A steady system prompt lets cache discounts absorb repeated input.

• Front it with a gateway: Running several Llama hosts behind one gateway lets you fail over on price without rewriting code.

For the tooling side, the llama cost management tools roundup compares the platforms that do this well.

Where Llama Budgets Actually Break

A team ships a Llama feature and forecasts spend off DeepInfra's cheap 70B rate. The first quarter lands higher and nobody can say why. Nothing on the rate card was wrong. Some traffic quietly routed to a 405B endpoint at ten times the token cost. 

A second team ran Llama on Bedrock for compliance, so spend now sits across two dashboards nobody reconciles, the exact blind spot llm cost allocation tools exist to close. Neither dashboard shows cost per customer or per feature, the number a finance partner asks for, which is where most AI budgets leak, in the space between invoices.

How Amnic Handles Multi-Provider Llama Spend

Amnic closes that gap. It pulls Llama usage from every host into one view and ties tokens to cost per customer, per feature and per model, which is what makes it a multi-provider llm cost management tool rather than another dashboard. The number updates as spend happens rather than at quarter close.

Amnic does not tell you to switch models or providers and it does not resell inference. It measures what you already run. Its read-only agents flag a spend spike, a model that drifted to a pricier host, or a budget nearing its cap before the invoice lands. Team-level breakdowns from ai cost visibility tools then turn a confusing multi-provider bill into a unit-cost number the business can act on.

Key Takeaways

• Meta no longer sells a first-party Llama API, so pricing means third-party hosted inference.

• Llama 3.3 70B runs from about $0.10 input on DeepInfra to $1.04 on Together; 405B spreads twelvefold across hosts.

• Model size and host choice drive the bill more than usage.

• Batching, caching and a gateway are the highest-leverage controls.

• The rate card is the floor. Only per-call measurement across every provider tells you the real number.

Frequently Asked Questions

Is the Llama API free?

No. The model weights are free to download under Meta's community license, but running Llama through an API costs money per token. Meta's own hosted API has been retired, so you pay a third-party provider such as Groq, DeepInfra, or Together.

How much does the Llama API cost per token?

It depends on the model and the provider. Llama 3.1 8B starts near $0.02 per million input tokens on DeepInfra. Llama 3.3 70B runs $0.10 to $1.04 input by host. The 405B flagship ranges from about $0.80 to $9.50 per million tokens.

Does Meta host a Llama API itself?

No longer. Meta has wound down its first-party Llama API and now directs developers to third-party providers that host the models. The weights stay downloadable, but there is no official Meta-run paid API to call.

Which provider is cheapest for Llama?

DeepInfra carries the lowest verified per-token rates for most Llama models, near $0.10 input for 3.3 70B. Groq is priced higher but runs fastest. The cheapest listing can be a quantized endpoint, so confirm precision before you commit.

Is it cheaper to self-host Llama or use an API?

Below roughly 50 million tokens a day, an API is usually cheaper because a rented GPU bills whether or not you use it. Above that, with steady utilization, self-hosting can win. The break-even turns on how busy your hardware stays.

Why does the same Llama model cost different amounts?

Providers set their own rates, serve different quantization levels and add markups. A router may charge a platform fee on top of the host's price. The cheapest per-token figure is not always the cheapest real bill once markup and quality are counted.

How do I track Llama API spend across providers?

Pull usage from each host into one view and tag it by feature, customer and model. Provider dashboards alone will not reconcile a multi-host bill. A dedicated cost platform ties tokens to unit cost so you can forecast and allocate spend.