Inference Cost Optimization: How to Cut Your AI Bill by 75%

Three levers: open-source models, cached input, owned infrastructure. Pull all three and your inference bill drops by up to 75%.

That is not a promotional discount or a limited-time offer. It is the structural difference between renting compute from a chain of providers and running on infrastructure owned end to end. Most inference providers rent GPUs from cloud platforms, stack their margin on top, and pass the compounded cost to you in every token. One owned stack, one margin. Price follows physics.

If your inference bill is growing faster than revenue, the problem is not your usage. It is the cost structure underneath it. Here are the three levers that fix it.

Lever 1: switch to open-source inference models

Up to 75% savings versus proprietary alternatives.

The gap between open-source and frontier proprietary models has closed. A curated frontier of open-source models now matches or exceeds proprietary quality on production workloads, from reasoning and function calling to multilingual generation and real-time voice. The quality argument for paying proprietary premiums is yesterday's conversation.

Open weights mean no vendor lock-in. You control the deployment, the fine-tuning, and the data pipeline. No surprise deprecations, no forced upgrades, no terms-of-service changes that redefine what you are allowed to build.

On Telnyx, the switch is a base URL change. The API is OpenAI-compatible, so your existing code, SDKs, and tooling work without modification. You get a curated set of open-source models, each chosen because it is the best at something real: high-throughput reasoning, cost-efficient intelligence at scale, real-time and voice AI workloads. No filler, no legacy weights, no also-ran options padding a dropdown.

Start with the models that fit your workload. Run a side-by-side comparison against your current provider. The quality difference will surprise you, and the cost difference will change your roadmap.

Lever 2: use cached input for inference

Up to 88% off with prompt caching.

If your application sends repeated context, system prompts, few-shot examples, or any fixed prefix across requests, you are paying full price for computation the provider already performed. Prompt caching eliminates that waste.

Cached input stores the computed representation of repeated prefixes and reuses them across requests. The result is up to 88% off the input token cost for cached portions, with no configuration needed on Telnyx. It is automatic. Cache writes are not charged, so building up a cache costs nothing.

The economics compound fast. A voice AI agent with a 2000-token system prompt running 100,000 conversations per month is paying for 200 million input tokens of repeated context. With cached input, that drops by up to 88%. For teams whose prompt templates are the largest token cost line item, this single lever can change the unit economics of the product.

For agents that maintain conversation state across turns, stateful actor keeps memory on the same infrastructure as the GPUs. No cross-provider latency for state retrieval, no extra margin for a third-party memory layer. And KV provides fast key-value storage for context windows and session data on the same network.

The same API call, the same code, the same models. The caching layer handles it. You see the savings on your invoice, not in your integration.

Lever 3: choose an inference provider that owns infrastructure

No stacked cloud margins. One owned stack, one margin.

This is the lever most teams overlook, and it is the one that makes the other two sustainable over time.

Most inference providers do not own GPUs. They rent compute from cloud providers, add their margin, and resell it to you. Some rent from a single cloud. Some orchestrate across ten or more. Each layer in that chain takes a margin, and every margin shows up in your per-token price. When a provider drops their price, it is usually because they negotiated a better cloud deal, not because the underlying cost structure changed. That is a temporary advantage, not a structural one.

This applies to other open-source inference providers too. Together AI, Fireworks, Anyscale, and others serve the same open-source models Telnyx does, but on rented GPU capacity. Their per-token price includes a cloud provider margin stacked on their own. Telnyx owns the GPUs, so the price reflects the actual cost of running compute plus one margin.

Telnyx owns the GPU infrastructure. The models run on hardware we bought, in facilities we operate, on a network we control. There is no cloud provider between us and the silicon. One margin, not a stack of them. When we lower pricing, it reflects the actual cost of running compute, not a promotional cycle we will reverse next quarter.

Ownership also produces the other advantages that make inference cheaper and better:

• Lower latency. Inference runs on our network. No inter-provider hops, no cross-region routing, no TLS handshakes between vendors. Your request travels from your application to the GPU and back on infrastructure we own end to end.
• Better reliability. We control the hardware, the network, and the SLA. One throat to choke. When something breaks, there is no finger-pointing across three vendors.
• In-region compute. Our GPUs are in the Americas, Europe, APAC, and MENA. Data stays where your users are. Not a premium tier, not a configuration option. Just smart architecture.
• Zero data retention. Your prompts and responses are not persisted after inference completes. No training on your data. No logging of request content. Your data is processed and discarded.

A competitor can match a price. They cannot buy the structure that produced it.

Pulling all three inference cost levers

The three levers stack. Switching to open-source models cuts your per-token cost by up to 75% versus proprietary alternatives. Cached input takes another up to 88% off the input portion for repeated context. Owned infrastructure ensures neither of those savings has a hidden margin baked in.

A team spending $50,000 per month on OpenAI or Anthropic can cut that to $12,500 or less by pulling all three, without rewriting their application, without migrating their data pipeline, and without accepting a quality downgrade. The math is straightforward. The switch takes 10 minutes because the API is OpenAI-compatible.

Start by running the numbers. Pull your last month of inference usage, compare it against open-source model pricing, and calculate what cached input saves on your repeated context. The gap between what you pay now and what you could pay is the budget for everything else your team wants to build. Read the efficient frontier model selection guide for a deeper look at choosing the right model per workload.

Then swap your base URL and test. Your existing code works. Sign up free to get started.