@_avichawla: I cut Fable 5 token usage 2.5x with just one change! - Before: 5.5 M tokens · 7 errors · $8.94 - After: 2.3 M tokens · …

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Summary

The author reduced token usage for an AI agent by 2.5x by switching from Firebase to InsForge, an open-source backend platform for agentic coding, cutting tokens from 5.5M to 2.3M and eliminating manual interventions.

I cut Fable 5 token usage 2.5x with just one change! - Before: 5.5 M tokens · 7 errors · $8.94 - After: 2.3 M tokens · 0 errors · $4.17 The final build was the same for both, but the path the agent took wildly differed. In both runs, the agent started with the same thing, i.e., it understood the backend before building anything, like: - Permission policies - Available storage buckets - Auth providers configured - How edge functions are deployed The first run used Firebase, which was built for a human dev using a dashboard. While the dev can read the above state by clicking through tabs, an agent has no dashboard. So it gathered the same info through API calls. And there's no single Firebase call that returned this info. The agent required to query multiple times, and each query over-returned. For instance, when the agent asked how sign-in is configured, Firebase also returned the entire auth surface and every method it supported. This was far more context than what it needed. And it repeated across every part of the backend it inspected. Some states (like which auth providers are active) weren't queryable at all. I provided it myself. Otherwise, the agent would have guessed. Errors further compounded the token usage. When a dev sees "permission denied," they can look at the console and figure out whether it's a rule, a path, or an unauthenticated request. Firebase returned the same string to the agent as well, and it had none of that surrounding context to debug. So it guessed again, picked the most likely cause, and rewrote code, utilizing more tokens. This Firebase setup cost me 5.5M tokens and 7 manual interventions during errors on a full-stack RAG app. But I brought that down to 2.3M tokens and 0 manual interventions by using InsForge as the backend context engineering layer (open-source and self-hostable via Docker). It provides the same primitives as Supabase/Firebase, but structures the entire information layer for agents, instead of dashboards. In one CLI call that consumed ~500 tokens, the agent saw the full backend topology before writing a single line of code. This included auth, database, storage, edge functions, model gateway, micro VMs, and deployment. Also, instead of loading the entire product surface into context on every task, four narrowly scoped skills activated only when relevant to keep cognitive load minimal. And to ensure efficient retries if needed, every CLI operation returned structured JSON with meaningful exit codes, so the agent never guessed what to do next. Here's the InsForge GitHub Repo: http://github.com/InsForge/InsForge… (don't forget to star it ) The video below depicts the final build, comparing Firebase and InsForge. To dive deeper, I recently published a full walkthrough building the same RAG app on both backends and inspected them end-to-end. Read it below.
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I cut Fable 5 token usage 2.5x with just one change!

  • Before: 5.5 M tokens · 7 errors · $8.94
  • After: 2.3 M tokens · 0 errors · $4.17

The final build was the same for both, but the path the agent took wildly differed.

In both runs, the agent started with the same thing, i.e., it understood the backend before building anything, like:

  • Permission policies
  • Available storage buckets
  • Auth providers configured
  • How edge functions are deployed

The first run used Firebase, which was built for a human dev using a dashboard.

While the dev can read the above state by clicking through tabs, an agent has no dashboard. So it gathered the same info through API calls.

And there’s no single Firebase call that returned this info. The agent required to query multiple times, and each query over-returned.

For instance, when the agent asked how sign-in is configured, Firebase also returned the entire auth surface and every method it supported.

This was far more context than what it needed. And it repeated across every part of the backend it inspected.

Some states (like which auth providers are active) weren’t queryable at all. I provided it myself. Otherwise, the agent would have guessed.

Errors further compounded the token usage.

When a dev sees “permission denied,” they can look at the console and figure out whether it’s a rule, a path, or an unauthenticated request.

Firebase returned the same string to the agent as well, and it had none of that surrounding context to debug.

So it guessed again, picked the most likely cause, and rewrote code, utilizing more tokens.

This Firebase setup cost me 5.5M tokens and 7 manual interventions during errors on a full-stack RAG app.

But I brought that down to 2.3M tokens and 0 manual interventions by using InsForge as the backend context engineering layer (open-source and self-hostable via Docker).

It provides the same primitives as Supabase/Firebase, but structures the entire information layer for agents, instead of dashboards.

In one CLI call that consumed ~500 tokens, the agent saw the full backend topology before writing a single line of code.

This included auth, database, storage, edge functions, model gateway, micro VMs, and deployment.

Also, instead of loading the entire product surface into context on every task, four narrowly scoped skills activated only when relevant to keep cognitive load minimal.

And to ensure efficient retries if needed, every CLI operation returned structured JSON with meaningful exit codes, so the agent never guessed what to do next.

Here’s the InsForge GitHub Repo: http://github.com/InsForge/InsForge…

(don’t forget to star it )

The video below depicts the final build, comparing Firebase and InsForge.

To dive deeper, I recently published a full walkthrough building the same RAG app on both backends and inspected them end-to-end.

Read it below.


InsForge/InsForge

Source: https://github.com/InsForge/InsForge

InsForge

The all-in-one, open-source backend platform for agentic coding.

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InsForge

The all-in-one, open-source backend platform for agentic coding. InsForge gives your coding agent database, auth, storage, compute, hosting, and AI gateway to ship full-stack apps end-to-end.

https://github.com/user-attachments/assets/345efbc6-ca63-4189-bde0-12ef3bda561b

How it works

Coding agents interact with InsForge through one of two interfaces:

  • MCP Server (self-hosted and cloud): exposes InsForge’s operations as tools any MCP-compatible agent can call.
  • CLI + Skills (cloud only): a command-line interface paired with Skills that agents invoke directly from the terminal.

Both interfaces let coding agents operate the backend like backend engineers:

  • Read backend context and state: Pull documentation, schemas, metadata (deployed functions, bucket contents, auth config), and runtime logs, so the agent has what it needs to write code, verify what it built, and debug when something breaks.
  • Configure primitives: Deploy edge functions, run database migrations, create storage buckets, set up auth providers, and configure other backend resources directly.
graph TB

    subgraph TOP[" "]
        AG[AI Coding Agents]
    end

    subgraph MID[" "]
        SL[InsForge]
    end

    AG --> SL

    SL --> AUTH[Authentication]
    SL --> DB[Database]
    SL --> ST[Storage]
    SL --> EF[Edge Functions]
    SL --> MG[Model Gateway]
    SL --> CP[Compute]
    SL --> DEP[Deployment]

    classDef bar fill:#0b0f14,stroke:#30363d,stroke-width:1px,color:#ffffff
    classDef card fill:#161b22,stroke:#30363d,stroke-width:1px,color:#ffffff

    class AG,SL bar
    class AUTH,DB,ST,EF,MG,CP,DEP card

    style TOP fill:transparent,stroke:transparent
    style MID fill:transparent,stroke:transparent

    linkStyle default stroke:#30363d,stroke-width:1px

Core Products:

  • Authentication: User management, authentication, and sessions
  • Database: Postgres relational database
  • Storage: S3 compatible file storage
  • Model Gateway: OpenAI compatible API across multiple LLM providers
  • Edge Functions: Serverless code running on the edge
  • Compute (private preview): Long-running container services
  • Site Deployment: Site build and deployment

⭐️ Star the Repository

Star InsForge

If you find InsForge useful or interesting, a GitHub Star ⭐️ would be greatly appreciated.

Quickstart

Cloud-hosted: insforge.dev

InsForge.dev

Self-hosted: Docker Compose

Prerequisites: Docker + Node.js

1. Setup

You can run InsForge locally using Docker Compose. This will start a local InsForge instance on your machine.

Deploy on Docker

Or run from source:

# Run with Docker
git clone https://github.com/InsForge/InsForge.git
cd insforge
cp .env.example .env
docker compose -f docker-compose.prod.yml up

2. Connect InsForge MCP

Open http://localhost:7130

Follow the steps to connect InsForge MCP Server

Connect InsForge MCP

3. Verify installation

To verify the connection, send the following prompt to your agent:

I'm using InsForge as my backend platform, call InsForge MCP's fetch-docs tool to learn about InsForge instructions.

4. Running Multiple Projects

You can run multiple InsForge projects on the same host by using different ports and project names.

# Create a separate env file for each project
cp .env.example .env.project1
cp .env.example .env.project2

Edit .env.project2 with different ports:

POSTGRES_PORT=5442
POSTGREST_PORT=5440
APP_PORT=7230
AUTH_PORT=7231
DENO_PORT=7233

Start each project with a unique name:

docker compose -f docker-compose.prod.yml --env-file .env.project1 -p project1 up -d
docker compose -f docker-compose.prod.yml --env-file .env.project2 -p project2 up -d

Each project gets its own isolated database, storage, and configuration. Manage them with:

docker compose -f docker-compose.prod.yml --env-file .env.project1 -p project1 ps      # status
docker compose -f docker-compose.prod.yml --env-file .env.project1 -p project1 logs -f  # logs
docker compose -f docker-compose.prod.yml --env-file .env.project1 -p project1 down     # stop

One-click Deployment

In addition to running InsForge locally, you can also launch InsForge using a pre-configured setup. This allows you to get up and running quickly with InsForge without installing Docker on your local machine.

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Contributing

Contributing: If you’re interested in contributing, you can check our guide here CONTRIBUTING.md. We truly appreciate pull requests, all types of help are appreciated!

Support: If you need any help or support, we’re responsive on our Discord channel, and also feel free to email us [email protected] too!

Documentation & Support

Documentation

Community

  • Discord - Join our vibrant community
  • Twitter - Follow for updates and tips

Contact

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.


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