---
title: "Hebbia Alternative: Self-Hosted AI for Financial Analysis You Own"
slug: "hebbia-alternative-self-hosted"
author: "Blanca Amigot"
date: "2026-06-09 13:15:00"
category: "Premium"
topics: "Hebbia alternative, self-hosted financial AI, document intelligence AI, on-premise AI for asset management, SEC FINRA compliant AI, owned AI for investment firms, client data on-premise"
summary: "A self-hosted alternative to Hebbia where your firm owns the model and keeps client financial data on its own servers — no per-seat fee, fully model-agnostic."
banner: ""
thumbnail: ""
---

## The Short Answer

**The self-hosted alternative to Hebbia is the ibl.ai platform: you own the code and the model, and client financial documents never leave your firm's own servers.**

Hebbia is enterprise, per-seat, and cloud-hosted (publicly reported / approximate) — documents leave to the vendor for analysis.

ibl.ai inverts that. You deploy the research-agent stack inside your own VPC, on-premise, or air-gapped environment. It is model-agnostic — run Claude, GPT, Gemini, Llama, DeepSeek, or Cohere Command on the same private corpus, and switch anytime.

There is no per-seat fee. You pay for tokens actually consumed, or a flat self-hosted license plus the GPU. For an asset manager or PE firm where data residency is non-negotiable, ownership beats rental.

## How is a self-hosted Hebbia alternative different?

A self-hosted alternative to Hebbia changes who controls the stack. With Hebbia, your firm rents access to a managed cloud service and your documents travel to the vendor's infrastructure for processing (publicly reported / approximate).

With the ibl.ai platform, you receive the full source code and run the entire document-analysis pipeline inside your own network. Connectors to your data rooms, file shares, and research repositories run in-network — nothing is brokered through a third party.

The orchestration layer enforces an Ed25519-signed boundary that documents never cross. Models receive only the context the boundary releases, and every model call is logged. You own the audit trail because you own the system.

## Where does client financial data go?

Nowhere it shouldn't. With a self-hosted deployment, client financial data stays on your firm's own servers for its entire lifecycle — ingestion, embedding, retrieval, and generation all run inside your perimeter.

This is the structural difference from a cloud SaaS like Hebbia. In a managed model, sensitive deal documents, LP correspondence, and portfolio data are transmitted to the vendor (publicly reported / approximate).

For a regulated firm, that is a data-residency and confidentiality exposure.

With ibl.ai, the connectors that reach your data rooms execute inside your network. The signed orchestration boundary guarantees raw documents are never sent off-box to an external API you don't control.

## What does it cost vs per-seat enterprise pricing?

Per-seat enterprise pricing is structurally wrong at scale. A per-analyst license multiplies with headcount whether or not a given analyst runs a single query that month. At 200 analysts, you pay 200 licenses regardless of actual usage.

A self-hosted, owned deployment decouples cost from headcount. You pay a flat license plus your GPU spend, plus tokens actually consumed — so adding analysts does not multiply the bill.

<table style="width:100%; border-collapse:collapse; margin:1.5rem 0; font-size:0.95rem;">
  <thead>
    <tr style="background:#f5f5f0; border-bottom:2px solid #2175C5;">
      <th style="text-align:left; padding:0.75rem; color:#5f6368;">Model</th>
      <th style="text-align:left; padding:0.75rem; color:#5f6368;">Pricing shape</th>
      <th style="text-align:right; padding:0.75rem; color:#5f6368;">Cost @ 200 analysts</th>
    </tr>
  </thead>
  <tbody>
    <tr style="border-bottom:1px solid #e5e7eb;">
      <td style="padding:0.75rem;"><strong>Per-seat enterprise SaaS</strong><br><span style="color:#5f6368; font-size:0.85rem;">~$2,000/seat/yr (publicly reported / approximate)</span></td>
      <td style="padding:0.75rem;">Linear with headcount</td>
      <td style="text-align:right; padding:0.75rem; font-variant-numeric:tabular-nums;">~$400,000/yr</td>
    </tr>
    <tr style="border-bottom:1px solid #e5e7eb;">
      <td style="padding:0.75rem;">Per-seat at scale (400 analysts)</td>
      <td style="padding:0.75rem;">Doubles with the firm</td>
      <td style="text-align:right; padding:0.75rem; font-variant-numeric:tabular-nums;">~$800,000/yr</td>
    </tr>
    <tr style="background:#f0f9ff; border-bottom:1px solid #e5e7eb;">
      <td style="padding:0.75rem;"><strong>ibl.ai (self-hosted)</strong><br><span style="color:#5f6368; font-size:0.85rem;">Flat license + GPU + tokens used</span></td>
      <td style="padding:0.75rem;"><strong>Decoupled from headcount</strong></td>
      <td style="text-align:right; padding:0.75rem; font-variant-numeric:tabular-nums;"><strong>Flat — no per-seat multiplier</strong></td>
    </tr>
  </tbody>
</table>

The gap widens with every analyst you hire. Usage-based and self-hosted pricing is the right shape for a firm that wants AI across the whole research desk.

## How does it satisfy SEC / FINRA recordkeeping?

Recordkeeping rules require firms to capture, retain, and reproduce communications and the basis for decisions. A self-hosted deployment makes this clean because the records live in your own systems.

Every model call — prompt, retrieved context, response, model identity, and timestamp — is logged to your firm's own SIEM. Because the pipeline runs in-network, there is no third party holding the only copy of a regulated record.

The signed orchestration boundary produces a tamper-evident trail of what each agent did. When examiners ask how a research conclusion was reached, you can replay it from logs you control.

See [air-gapped AI](https://ibl.ai/service/air-gapped-ai) for the isolated-deployment pattern and [financial-services solutions](https://ibl.ai/solutions/financial-services) for the broader fit.

## Which models can it run on private documents?

Any of them. The ibl.ai platform is model-agnostic, so you run the LLM that best fits each research workflow on the same private corpus — Claude, GPT, Gemini, Llama, DeepSeek, or Cohere Command.

You can switch models anytime without re-platforming, and run cheaper open-weight models for bulk extraction while reserving a frontier model for high-stakes synthesis. You are never locked to one vendor's model roadmap or pricing.

ibl.ai is family-owned and operated from New York, NY — a U.S.-headquartered, domestically-owned long-term partner. Some document-AI vendors are foreign-owned (for example, Cohere is Canadian); with ibl.ai you stay model-agnostic and U.S.-headquartered.

## How is it deployed (VPC / on-prem / air-gapped)?

You choose the deployment, and it matches your security posture rather than the vendor's. The ibl.ai platform deploys in your own cloud VPC, on-premise in your data center, or fully air-gapped with no outbound connectivity.

It runs on the [Agentic OS](https://ibl.ai/product/agentic-os) core with the OpenClaw and NVIDIA NemoClaw runtimes, so the research agents, guardrails, and connectors all execute inside the boundary you define.

For an asset manager or investment bank, air-gapped is the strongest posture: the model and your documents share one isolated environment, and nothing crosses the wire. Deploy anywhere — your environment, your rules.

## Frequently Asked Questions

### Is ibl.ai a drop-in replacement for Hebbia?

It serves the same job — agentic analysis across large financial document sets — but the model is different. Instead of renting a cloud service, you own and self-host the stack, so client data stays on your servers.

### Can analysts still use frontier models like Claude or GPT?

Yes. Because the platform is model-agnostic, analysts run Claude, GPT, Gemini, Llama, DeepSeek, or Cohere Command on private documents and switch models anytime — without sending anything to an external service.

### Does self-hosting mean we lose vendor support?

No. ibl.ai is a long-term partner, not a license-and-leave vendor. You own the code and data while ibl.ai supports deployment, upgrades, and tuning inside your environment.

### How does this help with an SEC or FINRA exam?

Every prompt, retrieved document, model identity, and response is logged to your own SIEM, and the signed orchestration boundary gives a reproducible trail. You hold the records in systems you control.