Doubleword

Doubleword is an AI inference API focused on async and batch workloads, designed to lower the cost of running open-weight models at scale. It is positioned as an OpenAI-compatible endpoint so existing applications can route high-volume inference tasks to Doubleword without rewriting their integration code. The service advertises savings of up to 90% compared with real-time closed-source APIs, with lower prices for batch schedules and built-in support for cache reuse. Doubleword runs open-weight models on an efficiency-optimized inference stack. Its homepage highlights an "efficiency frontier" in which Doubleword models provide a better cost-performance trade-off than alternatives at similar intelligence levels. The platform lists models targeting agentic reasoning, coding and maths workflows, multimodal agentic coding, and high-throughput extraction/ETL. Customers are described as token-hungry teams using agents, evals, pipelines, and other automated workloads. The company also emphasizes use cases like long-horizon agents and batch pipelines, and reports metrics including high cache hit rates and lower agentic inference costs than some alternatives. It is used by a range of businesses and institutions, and has been featured in technology and business press. However, public pricing and detailed operational documentation are not provided on the homepage, so teams should consult the docs or sign up to assess fit.

Category: AI API
Pricing: Unknown

Problem solved

High-volume and batch AI workloads are often priced out by real-time proprietary APIs, especially for agents, evals, extraction pipelines and model evaluation. Doubleword offers low-cost OpenAI-compatible async and batch inference for open-weight frontier-class models. It reduces inference cost per task by up to 90%, enabling teams to run larger workloads within budget.

Use cases

Long-horizon autonomous agents needing many inference calls
Large-scale offline batch inference jobs
Evaluation and evals of AI models over many test cases
High-throughput extraction and ETL over unstructured data
Coding and maths workflows using open-weight models
Multimodal agentic coding tasks
Cost-sensitive experimentation with OpenAI-compatible APIs

Audience

Developers
ML Engineers
Data Scientists
Startups
Enterprise

Pros

Up to 90% lower cost than real-time closed-source APIs
OpenAI-compatible endpoint simplifies integration
Optimized for async and batch inference
Efficiency-focused inference stack for open-weight models
High cache hit rates reduce repeated token costs

Cons

Pricing is not published openly on the homepage
Not designed for real-time synchronous latency
Limited operational details in marketing material
Depends on availability of open-weight models
Less established than large cloud AI platforms

Official website: https://doubleword.ai/

Canonical LambdaBase page: https://www.lambdabase.com/tools/doubleword

← Directory

Doubleword

Unknown

• Updated Sep 8, 2026, 03:10 AM

Doubleword is an AI inference API focused on async and batch workloads, designed to lower the cost of running open-weight models at scale.

Use Cases ▼

Long-horizon autonomous agents needing many inference calls

Large-scale offline batch inference jobs

Evaluation and evals of AI models over many test cases

High-throughput extraction and ETL over unstructured data

Coding and maths workflows using open-weight models

Multimodal agentic coding tasks

Cost-sensitive experimentation with OpenAI-compatible APIs

What Problem It Solves ▼

Key Features ▼

✓

Up to 90% lower cost than real-time closed-source APIs

✓

OpenAI-compatible endpoint simplifies integration

✓

Optimized for async and batch inference

✓

Efficiency-focused inference stack for open-weight models

✓

High cache hit rates reduce repeated token costs

Overview

Full Description ▼

Doubleword runs open-weight models on an efficiency-optimized inference stack. Its homepage highlights an "efficiency frontier" in which Doubleword models provide a better cost-performance trade-off than alternatives at similar intelligence levels. The platform lists models targeting agentic reasoning, coding and maths workflows, multimodal agentic coding, and high-throughput extraction/ETL. Customers are described as token-hungry teams using agents, evals, pipelines, and other automated workloads.

The company also emphasizes use cases like long-horizon agents and batch pipelines, and reports metrics including high cache hit rates and lower agentic inference costs than some alternatives. It is used by a range of businesses and institutions, and has been featured in technology and business press. However, public pricing and detailed operational documentation are not provided on the homepage, so teams should consult the docs or sign up to assess fit.