The Short Answer
The self-hosted alternative to Hebbia is the ibl.ai platform, where you own all the code and the data and client financial documents never leave your firm's own servers. You deploy the research-agent stack inside your own VPC, on-premise, or air-gapped environment, run it model-agnostic across any LLM, and pay with no per-seat pricing — so cost tracks usage rather than headcount.
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.
| Model | Pricing shape | Cost @ 200 analysts |
|---|---|---|
| Per-seat enterprise SaaS ~$2,000/seat/yr (publicly reported / approximate) |
Linear with headcount | ~$400,000/yr |
| Per-seat at scale (400 analysts) | Doubles with the firm | ~$800,000/yr |
| ibl.ai (self-hosted) Flat license + GPU + tokens used |
Decoupled from headcount | Flat — no per-seat multiplier |
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 for the isolated-deployment pattern and financial-services solutions 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 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.