Sold by: Takara
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Takara's ds1-code, powering Miru, is a CPU-based code embedding and retrieval model that gives AI coding agents precise, semantic access to a codebase without burning tokens on wasteful search. Delivering over 50% token reduction, 40%+ lower token costs, and up to 60% faster retrieval, Miru works underneath Claude Code, Cursor, Codex, GitHub Copilot, and other coding agents to cut inference spend while preserving generated code quality.
Overview
Takara's ds1-code is a CPU-based embedding model purpose-built for understanding code at the semantic level, underpinning Miru, a code-retrieval infrastructure layer for AI coding agents. Rather than dumping entire code bases into an LLM's context window and hoping it identifies what's relevant, Miru combines ds1-code with hybrid retrieval - BM25, lexical search, vector search, and ranking - to deliver the exact function, module, or dependency chain an agent needs in a single retrieval step. Because ds1-code runs on CPU rather than GPU, it avoids GPU procurement, queuing, reservation planning, and regional rollout complexity, while scaling close to developers globally. The result is over 50% token reduction and 40%+ lower token costs across coding agent workflows, with retrieval tasks up to 60% faster and less turns required than conventional approaches, without any impacting the quality of generated code. Miru can be tried via a public API to benchmark value against real code generation workflows, and deployed as a SageMaker endpoint in a customer's own AWS environment - keeping source code and software IP inside a controlled environment.
NOTE: ds1-code is designed to be exclusively used with Miru (https://github.com/takara-ai/miru-code )
Highlights
- Cut coding agent token spend. Modern AI coding agents burn most of their token budget not on writing code, but on finding it - reading files, re-loading previously seen context. Miru, powered by ds1-code, replaces that brute-force search with precise semantic retrieval, delivering exactly the code an agent needs in a single step, cutting token consumption across real coding workflows. It works underneath the agents teams already use, including Claude Code, Cursor, Codex, and GitHub Copilot.
- CPU-based architecture that scales without GPU bottlenecks. ds1-code is built to run entirely on CPU, eliminating the need for GPU reservation that typically constrain AI infrastructure rollouts. Letting Miru scale without the lead times or cost overhead of GPU-based retrieval, while still delivering search that is up to 60% faster than conventional approaches. This means a lower-cost path to giving every coding agent fast, high-quality retrieval, wherever developers are located.
- Sovereign deployment for code and IP that must stay under your control. Source code is one of an organisation's most sensitive assets. Try Miru instantly via the public API to benchmark real workflows, then deploy as a SageMaker endpoint in your AWS environment. Code and IP stay inside your controlled environment at every stage, giving CIOs and security teams a sovereign deployment model that meets data residency needs without giving up the speed or cost advantages of ds1-code.
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Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.c5.xlarge Inference (Batch) Recommended | Model inference on the ml.c5.xlarge instance type, batch mode | $1.20 |
ml.c5.large Inference (Real-Time) Recommended | Model inference on the ml.c5.large instance type, real-time mode | $0.90 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $2.46 |
ml.c5.4xlarge Inference (Batch) | Model inference on the ml.c5.4xlarge instance type, batch mode | $4.92 |
ml.c5.9xlarge Inference (Batch) | Model inference on the ml.c5.9xlarge instance type, batch mode | $11.04 |
ml.c5.18xlarge Inference (Batch) | Model inference on the ml.c5.18xlarge instance type, batch mode | $22.02 |
ml.t2.medium Inference (Real-Time) | Model inference on the ml.t2.medium instance type, real-time mode | $0.42 |
ml.t2.large Inference (Real-Time) | Model inference on the ml.t2.large instance type, real-time mode | $0.84 |
ml.c5.xlarge Inference (Real-Time) | Model inference on the ml.c5.xlarge instance type, real-time mode | $1.20 |
ml.c5.2xlarge Inference (Real-Time) | Model inference on the ml.c5.2xlarge instance type, real-time mode | $2.46 |
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Refunds are furnished in line with the EULA only. Please contact support@takara.ai for assistance.
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
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This is a first release and has no release notes.
Additional details
Inputs
- Summary
This model is for use with Miru only.
- Limitations for input type
- This model should be accessed and used in conjunction with Miru available for download here: https://github.com/takara-ai/miru-code. Using this model standalone is not supported.
- Input MIME type
- application/json
json
json
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