Milliseconds.ai vs Otto by Audos.com: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Milliseconds.ai and Otto by Audos.com — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Milliseconds.ai
CloudRaker
A small decision model served over a REST API that returns typed labels, scores, spans, and JSON fields from text or images in milliseconds.
Key features
- Typed Decision Endpoints: Eight purpose-built routes — yes-no, classify, classify-tree, rate, answer, extract, entities, and verify — each returning structured JSON rather than free text, so application code can branch on the result immediately.
- Sub-Second Latency: A decision returns in about 90 milliseconds, answer calls in 0.3–0.9 seconds, and extraction in 2.5–3.5 seconds at medium detail, making the model usable inside request paths rather than background jobs.
- Calibrated Probabilities: Responses include per-label probabilities and a confidence value, so near-ties surface as uncertainty your application can route to a human instead of acting on silently.
- Schema-Driven Extraction: Send a JSON Schema and get back a filled object — up to five fields per extract call — ready for validation before writing to a record.
- Image Input: Send JPEG, PNG, or WebP images up to 5 MB as bytes, a data URL, or base64, billed as a fixed token count set by the detail level you request, with no image storage retained.
- Answer Spans with Offsets: The answer capability returns the exact text span plus start and end offsets, so an application can highlight where in the source the answer came from.
- SDKs and CLI: Hand-written TypeScript (@cloudraker/milliseconds) and Python (cloudraker-milliseconds) SDKs plus a dm1 CLI, where label names, scale levels, and schemas flow into the result type so a misspelled label is a compile error.
- Free Test Tier: Test keys carry 125 million free input tokens a month with no card required, at 30 requests and 500,000 input tokens per minute, shared across an organization.
Best for
- Support Ticket Routing: Classifying inbound messages into billing, shipping, or technical queues and flagging urgent ones for faster response.
- Invoice and Receipt Processing: Extracting invoice number, vendor, total, and currency from document text or images into validated fields before writing a record.
- Content Moderation and Policy Checks: Verifying whether a return request, listing, or submission satisfies a written policy before it reaches a human reviewer.
- Sentiment and Priority Scoring: Rating customer frustration on a defined scale to sort a support queue by how badly each thread needs attention.
- Entity Recognition in Records: Pulling people, organizations, claim IDs, and dates out of free-text notes for search and record matching.
- Agent Tool Calls: Giving an LLM agent a fast, cheap decision primitive for yes/no and classification steps that do not need a generative model.
O
Otto by Audos.com
surajshetty3416 / Otto (Frappe app)
A Frappe application library that adds LLM capabilities (sessions, model management, queries) to Frappe apps.
Key features
- Frappe Integration: Implements LLM capabilities as a Frappe application backed by DocTypes so model sessions and metadata are stored and managed in the Frappe framework.
- Typed Library Interfaces: Exposes strongly typed modules (otto.lib.types and otto.llm.types) to build custom LLM features with clear type definitions and developer ergonomics.
- Session Management: Provides session-based interaction support (OttoSession) allowing multi-turn conversations and continued context across requests while warning against directly coupling to internal DocType internals.
- Quick One-off Queries: Offers utilities to perform single-shot operations such as document summarization or ad-hoc queries within a Frappe app.
- Model Discovery & Creation: Includes tooling to discover available models and create new model configurations from within the application environment.
- Otto Execution Workflows: Integrates with application-level execution flows so generated outputs and LLM interactions can be incorporated into business processes and custom features.
- Exposes core LLM functionality as a library for Frappe apps
- Session management (OttoSession backed by DocType)
- Model management and discovery
- Typed API definitions via otto.lib.types and otto.llm.types
- Examples for one-off queries, session-based interactions, tool usage, and model creation
- Integrated with Frappe DocTypes (not a standalone package)
- Used internally for Otto Execution and application-level features
- Repository documentation (README) with installation notes and usage examples
Best for
- Document Summarization: Use Otto to add a one-click document summarization feature inside a Frappe app to generate concise summaries from uploaded documents.
- Conversational Assistants: Build session-based chat assistants within ERP/CRM workflows that maintain context across interactions using OttoSession.
- In-App Model Selection: Allow administrators to discover, configure, and switch between available LLM models for different app features (e.g., billing, support, knowledge base).
- Workflow Automation: Embed LLM-driven execution steps into existing Frappe workflows to generate content, draft responses, or extract structured data from text.
- Custom LLM Features: Developers create bespoke LLM-powered capabilities (e.g., guided form-filling, smart search, or code generation helpers) using the typed otto.lib interfaces.
- Tool Integration: Combine Otto's LLM outputs with other Frappe DocTypes and business logic to automate tasks like ticket triage or knowledge base population.
- Add conversational or session-based LLM features to Frappe applications
- Build custom LLM-backed app features (summarization, generation, Q&A) inside Frappe
- Create and manage LLM sessions and models from within a Frappe app
- Instrument application-level execution flows that call LLMs (Otto Execution)
- Prototype tool-usage patterns and model discovery workflows in Frappe