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Silk Compute Review

Silk Compute provides API access to uncensored Kimi K3, GLM-5.2, and other frontier models without content moderation or data retention, offering fast inference for various workflows.

shipped Aug 12, 2026codepaid

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Why it matters

1Offers API access to uncensored Kimi K3 and GLM-5.2 models.

2Guarantees zero data retention and never trains on user data.

3Provides fast inference tailored for coding and research workflows.

4Pricing for Kimi-K3.0-uncensored is $7 per 1M input tokens and $35 per 1M output tokens.

About Silk Compute

Business Model

Usage-Based (Pay Per Use)

Usage Pricing

$7/$35 per 1M tokens

Headquarters

San Francisco, CA

Pricing Plans

Kimi-K3.0-uncensored

$7/$35

• In/Out price
• Context 1M tokens

Cost Examples

• Input 1M tokens: $7
• Output 1M tokens: $35

API Docs

Specs

API Docs

View Documentation →

API Available

Yes, public API

Screenshots

overview

What is Silk Compute?

Silk Compute is a software-defined cloud storage platform developed by Silk that enables enterprises to enhance the performance, efficiency, and resiliency of data-intensive workloads across major public clouds like AWS, Azure, and Google Cloud. It provides API access to uncensored Kimi K3, GLM-5.2, and other frontier models without content moderation or data retention, offering fast inference tailored for various workflows including coding and research.

Also known as the Silk Cloud Data Platform, Silk Compute acts as a high-performance data layer between cloud compute and native storage, optimizing infrastructure for critical applications and AI workloads without requiring changes to existing applications or databases. It provides a virtualized architecture and data management system that decouples performance from capacity, allowing independent provisioning of compute and storage. This approach aims to deliver ultra-high performance, consistent latency, and dynamic scalability for demanding workloads.

features

Key Features of Silk Compute

Silk Compute offers a suite of features designed for high-performance, secure, and flexible AI and data workloads. Its architecture focuses on delivering uncensored model access, data privacy, and optimized inference speeds.

API access to uncensored Kimi K3, GLM-5.2, and other frontier models.
Zero data retention policy, ensuring user data is not stored.
Fast inference capabilities tailored for various computational workflows.
Compatibility with OpenAI and Anthropic API formats for ease of integration.
Support for unrestricted research workflows, including cyber defense and red-team evaluations.
HIPAA alignment for sensitive data handling.
Never trains on user data, maintaining data privacy.
Provides a software-defined cloud storage platform for AWS, Azure, and Google Cloud.

use cases

Who Should Use Silk Compute?

Silk Compute is designed for organizations and researchers requiring high-performance, uncensored AI model access with strict data privacy guarantees. Its capabilities are particularly suited for data-intensive and sensitive applications.

Researchers: For unrestricted research workflows, including security research and red-team evaluations.
Developers: For building coding agents and integrating AI into coding workflows.
Enterprises with High-Performance Databases: For Oracle, Microsoft SQL Server, and DB2 databases requiring exceptional IOPS and throughput in the cloud.
AI/ML/Analytics Teams: To accelerate data-intensive workloads with real-time data access and high-speed processing.
Healthcare Organizations: For leveraging real-time Electronic Health Record (EHR) data and migrating Epic EHR to Microsoft Azure, with HIPAA alignment.

how to use

How to Use Silk Compute

To begin using Silk Compute, users typically access its services via its API, which is compatible with OpenAI and Anthropic formats. The platform is designed for integration into existing development and research environments.

1Access the Silk Compute API documentation at https://github.com/silk-us/silk-sdp-api-docs.
2Integrate the API into existing applications or workflows using OpenAI or Anthropic compatible formats.
3Select desired frontier models such as Kimi K3 or GLM-5.2 for inference.
4Utilize the platform for specific use cases like coding, research, or cyber defense.
5Monitor usage and manage costs based on the usage-based pricing model for tokens.

pricing

Silk Compute Pricing & Plans

Silk Compute operates on a usage-based pricing model, primarily charging per million tokens for its uncensored models. Specific pricing tiers are available for different models.

Kimi-K3.0-uncensored: $7 per 1M input tokens, $35 per 1M output tokens.

Pros

+Provides API access to explicitly uncensored frontier models (e.g., Kimi K3, GLM-5.2).
+Guarantees zero data retention and never trains on user data, enhancing privacy.
+Offers HIPAA alignment, suitable for sensitive data workloads.
+Delivers fast inference tailored for demanding workflows like coding and research.
+Functions as a high-performance data layer for major public clouds (AWS, Azure, Google Cloud), optimizing enterprise applications.
+Achieved over 20 GiB/s I/O read rate in Google Cloud Platform.

Cons

−Pricing is usage-based, which may lead to variable costs depending on token consumption.
−Model selection, while featuring frontier models, may be more curated compared to aggregators like OpenRouter or the vast library of Hugging Face.
−Requires API integration, which may necessitate developer resources for setup.
−Specific details on multimodality capabilities are not publicly detailed.

Similar Tools

Silk Compute vs Competitors

Silk Compute differentiates itself in the market by offering explicit access to uncensored frontier models with a strong emphasis on data privacy and high-performance inference, particularly for enterprise cloud storage and AI workloads.

OllamaOn Stork

Allows users to run open-source large language models locally with an OpenAI-compatible API.

While completely free and private, you are limited by your local hardware for inference speed and model size, unlike Silk Compute's cloud-based, high-performance infrastructure. You also need to manage the local setup yourself.

Together AIOn Stork

Provides API access to a wide array of open-source models, often with competitive pricing and a focus on developer experience.

Together AI offers a broader selection of open-source models, but the level of content moderation can vary by model and may not be as explicitly 'uncensored' as Silk Compute's specific offerings.

OpenRouterOn Stork

Acts as a unified API gateway to numerous models from various providers, allowing users to easily switch between models and compare performance.

OpenRouter provides access to a vast selection of models, including some with less strict moderation, but it's an aggregator, meaning the 'uncensored' nature depends on the specific model chosen, whereas Silk Compute explicitly guarantees uncensored access to its listed models.

Hugging Face Inference API↗

Offers API access to thousands of open-source models hosted on Hugging Face, enabling rapid prototyping and deployment.

While Hugging Face provides an extensive library of models, the 'uncensored' aspect is model-dependent and not a universal guarantee like Silk Compute's offering. Inference speeds can also vary based on the model and tier.

Groq APIOn Stork

Specializes in extremely fast inference for select open-source models using their custom LPU inference engine.

Groq excels in inference speed, which aligns with Silk Compute's 'fast inference' claim, but it offers a more limited selection of models and does not explicitly market itself on the 'uncensored' aspect.

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