Modular
drop AI features into your app with two function calls
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What is Modular?
Modular is an AI platform that lets developers drop full AI features into existing applications with minimal plumbing. Instead of building custom infrastructure for context, embeddings, chat history, and retrieval, developers register their data sources as functions and then call two simple methods—ai.run() for one‑shot features and ai.chat() for stateful, data‑backed conversations. Modular automatically handles pulling the right data, fitting it into the model’s context window, managing chat sessions, and routing between different underlying models.
Key features include automatic context management so developers do not need to manually count tokens or truncate data, built‑in chat history storage and replay across requests, smart retrieval that invokes data functions only when needed and only fetches relevant content, and model routing that lets you switch between models such as Claude, GPT‑4o, and Gemini via configuration rather than code changes. The platform also provides built‑in retries and fallbacks for rate limits, timeouts, and errors, plus first‑class support for MCP‑style tools so existing MCP servers can be connected in a single line.
Modular is designed for startup engineers who lack a dedicated ML team but need to ship AI features quickly, for dev agencies building AI into multiple client apps, and for lead engineers who want to give their teams a clean, reusable abstraction layer instead of asking them to become AI infrastructure specialists. It is aimed at teams that already have business logic and data in code and want to front‑end that logic with AI‑driven features without rewriting or spinning up additional databases or vector stores.
Modular pricing
Pricing model: Freemium
Modular is currently in early access and does not list publicly available pricing tiers on its landing page; instead, visitors are asked to join a waitlist and will be contacted personally when the product launches. The site indicates that the platform will provide access to AI features via an API‑key‑based client, but specific details on free tiers, paid plans, usage limits, or feature‑based pricing are not disclosed at this time.
Modular pros
- Eliminates the need to build custom AI infrastructure from scratch
- Abstracts away token math and context‑window truncation bugs
- Handles embedding, retrieval, and context assembly automatically
- Manages chat history and session state across requests
- Supports one‑shot features via ai.run() in a single call
- Supports stateful, data‑backed conversations via ai.chat()
- Allows switching between models like Claude, GPT‑4o, and Gemini with config changes
- Provides built‑in retries and fallbacks for rate limits and errors
- Integrates natively with existing MCP‑style tools in one line
- Reuses your existing data functions instead of requiring a separate vector database
- Offers a tiny API surface with only two core methods to learn
- Designed specifically for teams without a dedicated ML or AI SRE team
- Simplifies shipping AI features across multiple client applications
- Reduces the time from feature idea to shipped AI‑powered experience
- Keeps developers focused on business logic instead of AI plumbing
Modular cons
- Only available in early access and not yet generally available
- Requires using Modular’s abstraction layer rather than raw model APIs
- May introduce vendor‑specific configuration and patterns over time
- Limited flexibility for deeply custom prompt‑engineering workflows
- Tight coupling between your data functions and Modular’s runtime
- Potential latency overhead from automatic retrieval and routing logic
- No public details on enterprise‑scale SLAs or uptime guarantees yet
- Early product may lack advanced debugging and observability tooling
Frequently asked questions about Modular
What problem does Modular solve?
Modular solves the problem of developers who want to ship an AI feature but end up spending months building and maintaining AI infrastructure. Instead of wiring up model APIs, managing prompts, hosting vector databases, chunking and embedding data, and handling retries and rate limits, Modular lets you register your data functions and then call two simple methods to get production‑ready AI features backed by your own data.
How do I integrate Modular into my app?
To integrate Modular, you instantiate a Modular client with your API key, then decorate your data‑fetching functions with @ai.datasource to tell Modular how to get your data. After that, you call ai.run() for one‑shot AI features or ai.chat() for stateful chat sessions, and Modular handles fetching the right data, fitting it into context, routing to the underlying model, and returning a clean result.
What does Modular do with my data?
Modular uses your registered functions to pull data on demand, then embeds, chunks, and retrieves only the relevant portions needed for each request so that it fits comfortably into the model’s context window. It also stores chat history and session state so that follow‑up messages can build on past conversations without requiring you to maintain a separate database for chat history.
Which models can I use with Modular?
Modular supports routing between major models such as Claude, GPT‑4o, and Gemini, and you can switch between them with a single configuration change rather than rewriting your integration code. The platform handles the underlying API calls, retries, and fallbacks so your application can keep using the same run or chat interface regardless of the model in use.
Do I need to set up a vector database?
No, you do not need to set up or manage a separate vector database. Modular handles embedding your data, storing the embeddings, and performing retrieval automatically, so you can continue to use your existing data functions and database queries without adding another storage layer or sync pipeline.
Can I use Modular if I already have MCP tools?
Yes, Modular is designed to be MCP‑native. If you already have an MCP server, you can connect it to Modular in one line of code, allowing Modular to coordinate calls between your existing tools and the underlying AI models while still providing the same ai.run() and ai.chat() interface.
What kind of applications is Modular best suited for?
Modular is best suited for software teams that already have business logic and data in code and want to quickly add AI features such as data‑backed summaries, Q&A over internal data, or chat assistants that understand user context. It is particularly helpful for startups without a dedicated ML team, dev agencies building AI features for multiple clients, and lead engineers who want to standardize AI integrations across their organization.
How does Modular manage chat history between requests?
Modular stores and manages chat history for each session identifier you provide, so when you call ai.chat() with the same session ID, it automatically retrieves prior messages, keeps the conversation coherent, and passes the right window of context to the model. This frees you from having to design and maintain a separate chat‑history database or logic for masking or truncating transcripts.
Is there a free tier or usage limit?
Modular’s website does not currently list a public free tier or explicit usage limits because the product is in early access. Instead, visitors are invited to join a waitlist and will be contacted personally when the service launches, at which point more detailed pricing and any free or trial tiers will be communicated directly.
What happens if the underlying model is rate‑limited or fails?
Modular provides built‑in retries and fallbacks for rate limits, timeouts, and other errors from the underlying model providers. When an issue occurs, Modular handles the retry logic and fallback behaviors so that your application can keep serving requests without exposing raw API errors or requiring you to implement complex circuit‑breaker or retry logic yourself.