Sold by: Takara
Deployed on AWS
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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
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Try this product free for 21 days according to the free trial terms set by the vendor.
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.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
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 |
Dimensions summary
You pay by the host hour based on the compute instance you run, so cost scales with instance size and how long you run it. Pricing splits into two inference modes. Batch mode covers five ml.c5 instance sizes, from xlarge up to 18xlarge, for processing groups of requests. Real-time mode covers eight instances, including ml.t2.medium, ml.t2.large, and ml.c5 sizes from large through 18xlarge, for immediate responses. Larger instances carry more compute per hour. Choose the mode and size that fit your workload, and you are billed only for the hours used.
Top-of-mind questions for buyers
Real-time mode returns embeddings immediately, one request at a time, which suits live search or streaming applications. Batch mode processes groups of requests together, which fits large jobs where instant response is not required. You pick the mode that matches your workload and pay per host hour on the instance you run.
You pay per host hour only while the instance runs the model. Charges stop when you stop the instance. A powered-off instance does not accrue software host-hour charges. Underlying AWS resource fees, such as storage, may still apply separately from the model's host-hour metering.
No. All billed instance types here are CPU-based ml.c5 and ml.t2 families. The DS1 model runs on standard CPU compute, so you do not need scarce GPU instances. This means you choose among CPU instance sizes, and cost scales with the size and hours you run.
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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.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Updated the model to be faster.
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
Support
Vendor support
Please email support@takara.ai for customer support for next day response.
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