Cost Calculator

See what open source saves you

Compare closed-source API spend against open-source models hosted privately on the Shakudo Platform.

$ /mo
What does your workload look like?
Chat & assistants

Long shared context, high cache hits (52% cache hit)

Document processing

Heavy unique input, low cache reuse (17% cache hit)

Agentic workflows

Many chained calls, very high cache reuse (75% cache hit)

Average tokens per request

Enter spend and tokens to estimate monthly request volume

Projected Savings

Enter workload information to calculate your savings.

Savings are estimated based on input and output token usage, selected models, and approximate cache hit rates.

Results are general estimates intended for internal discussion purposes only. Shakudo does not guarantee that use of the Shakudo platform will result in any particular amount of cost savings or other financial benefit. Any pricing shown here is for purposes of example only.

Why Migrate to Open-Source LLMs?

Transitioning from proprietary APIs to privately hosted open-source models unlocks unparalleled cost efficiency, complete data sovereignty, and hardware flexibility.

Zero Vendor Lock-In

Standardize on OpenAI-compatible API routes. Swap models, fine-tune weights, or migrate cloud providers without rewriting your application logic.

Flat-Rate Infrastructure

Replace volatile usage-based per-token billing with predictable, flat-rate GPU hosting. Control your budget even as your user base scales exponentially.

Data Sovereignty

Run models entirely inside your virtual private cloud (VPC). Keep sensitive customer data, prompts, and completions strictly within your secure compliance boundaries.

Tips for Accurate LLM Cost Estimation

01

Audit Your Token Split

Input tokens are typically processed faster and cost significantly less than output tokens. Knowing your exact input-to-output ratio is crucial for pricing accuracy.

02

Estimate Cache Hit Rates

Agentic loops and long system instructions benefit heavily from prompt caching. Map your workload to the appropriate cache profile to see true compound savings.

03

Assess Dedicated vs. Serverless GPU

Workloads spending over $5,000/month generally see immediate cost savings by shifting to dedicated, autoscaling GPU nodes instead of paying per-token.

Frequently Asked Questions

How does this LLM cost calculator estimate savings?

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Our calculator compares proprietary API pricing (e.g., GPT-4o, Claude Sonnet) with the costs of privately hosting equivalent open-source models (e.g., Llama 3, DeepSeek, GLM) on Shakudo. It evaluates your current monthly spend, average request tokens, and caching behavior to estimate your new monthly infrastructure costs and total projected cost reduction.

Why is hosting open-source models cheaper than APIs?

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How does prompt caching lower LLM pricing?

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Is my data secure when hosting models on Shakudo?

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Can I customize or fine-tune my hosted models?

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