Why Vectros
Build the app in weeks. Skip the backend you’d otherwise build, secure, and operate.
AI coding tools can produce a working front end in a few weeks. Everything underneath it (storage, document ingestion, search, model access, permissions, history) takes real backend and infrastructure expertise to assemble, and more to keep running. Vectros is that layer, ready to use.
What sits under the front end
Nine systems stand between your front end and a working product.
Each one is a project of its own, and they have to work together. Here is what it takes to build each, and what you do instead.
Storage and database
To build it
Choose and provision a datastore, plan capacity before you know your load, hand-write validation, handle concurrent writes, and keep indexes working as fields change.
With Vectros
Declare a schema in one place and get a validated, versioned store. No cluster to provision, no capacity to plan.
See how it works →Document ingestion
To build it
Extraction for every file type, a chunking and embedding pipeline, an async job queue with retries and status, and somewhere to keep structured metadata.
With Vectros
Send text or upload a file. It is indexed for search shortly after, with structured metadata alongside the extracted text.
See how it works →Search and indexing
To build it
A full-text engine, a vector index, a way to fuse the two rankings, and a sync job that keeps both matching your database.
With Vectros
One index and one call: full-text, semantic or hybrid, chosen per data type, or no indexing at all.
See how it works →Model access and grounded answers
To build it
Model access and keys, retrieval, prompt assembly, citations, and a decision about which vendor gets to see your users’ content.
With Vectros
Model calls that stay inside the Vectros perimeter for content on the data plane, and cited answers over your own documents and records.
See how it works →Access control and data isolation
To build it
An authorizer or a check in every handler, keys and tokens, roles, and separation between your customers’ data that has to survive every new query and every refactor.
With Vectros
Declare who can touch what once. The same policy applies to reads, writes and searches, and a call that leaves out the boundary is rejected.
See how it works →Audit history, sensitive data and erasure
To build it
Versioned history that resists tampering, rules for what a sensitive field may reach (history, search, logs), and a way to erase one person’s data when asked.
With Vectros
A tamper-evident version history for record types that keep one, sensitive fields kept out of it, and erasure requests that remove the records, documents and folders a subject solely owns (rows shared with another owner survive) and return a completion report.
Read the compliance guide →Custom logic
To build it
A place to run your own code, on a data event or as an endpoint, with permissions that match everything else.
With Vectros
Your own JavaScript, run as an endpoint or a trigger, under the same access policy.
See how it works →Agent access
To build it
Tools an agent can call, and permissions that limit it to what the person it works for could reach.
With Vectros
An official MCP server, with agents held to the same policy as any other caller.
See how it works →Infrastructure to run all of it
To build it
Provisioning, capacity planning, deployment and scaling for every system above, and someone on call for it.
With Vectros
Serverless: no cluster to provision, no capacity to plan.
See how it works →The expertise it takes
Even with AI writing the code, this needs people who have done it before.
An AI agent will write any one of these pieces on request. The hard part is knowing what to ask for and checking that it holds, and the failures here make no noise.
The skills involved
- Data modeling for a store that has to scale and stay consistent.
- Search indexing and relevance, and the pipelines that feed them.
- Asynchronous jobs: queues, retries, and knowing when a document is ready.
- Authorization design, and proof that it holds on every path.
- Encryption, credentials and key handling.
- Infrastructure as code, deployment, monitoring, and being on call.
What keeps costing after launch
- An index that drifts from your database. A record updates and its embedding does not. Search returns stale or missing results, and nothing throws an error.
- One forgotten filter. Tenant separation written as query filters is one missed WHERE clause from showing one tenant’s data to another, on every new query and every refactor.
- Re-running the parts. Re-embedding when you change models, reconciling the index after a hiccup, re-checking separation after every schema change.
Vectros is the layer you don’t operate, so that work is ours. You pay for the capabilities you turn on; the pricing page shows the credit model.
Getting started
Declare your model once. Start from something that already runs.
Schemas, indexing and access policy live in one file, behind one API with typed SDKs for TypeScript, Python and Java. The platform overview walks through all of it.
Start from a blueprint
An app model as one config file, provisioned in one command, then driven from an agent.
Use cases and blueprints →Fork a reference app
A full customer-facing app with your own sign-in and no backend of your own.
The web app solution →Point your agent at it
An MCP server with twenty-three data-plane tools, and a CLI that provisions without your root key.
Tools →When the stakes are higher
If your customers are banks, hospitals, or their lawyers
Everything above costs more when your customers are regulated. Their security reviews ask how tenants are separated, how sensitive fields are handled and how changes are recorded, and the answers have to be true of the system, not just of a policy. Isolation and scope enforcement are properties of the platform, the same on every tier, and sensitive-field handling and audit history are built in per record type, so the answers don’t depend on how carefully each query was written.
The security page has the specifics, and compliance details are available under NDA.
At a glance
How the alternatives compare.
| Capability | Roll-your-own | Generic backend-as-a-service | Bare vector DB | Vectros |
|---|---|---|---|---|
| Typed records + validation | You build it | Often, but separate from search | No, index only | One declared model |
| Document ingestion + indexing | Extraction, chunking, embedding, queue: all yours | Usually stores the file; extraction and indexing are yours | Extraction, chunking and embedding happen before it | Inline or upload, indexed as it lands |
| Hybrid search over your data | You wire DB + index + sync | Add-on or separate index | Vectors only; not your source of truth | One call over records + documents |
| Per-tenant isolation | Hand-authored WHERE clauses | Row-level rules you author | Your responsibility | Structural, denies access by default |
| Audit + version history when needed | You build and keep compliant | Rarely | No | Tamper-evident version history, kept per record type |
| Grounded RAG with citations | Assemble + maintain a framework | DIY | DIY on top | Built in, citations before generation |
| In-perimeter inference | You operate the path | Typically third-party model API | N/A | Data plane runs in-perimeter |
| Ops burden | Yours, forever | Partial; glue is yours | High: index + pipeline | The layer you don’t operate |
| Compliance path | Build it all, prove it all | Enterprise jump or ineligible layer | N/A | Grow into it, no re-platform |
| Agent access | New auth code per integration | New auth code per integration | N/A | Same policy, a scoped key, no new code |
Competitor columns describe the pattern each class of tool forces, not any one vendor’s specifics or pricing.
Next step
Try it on a real project.
Your own coding agent can check the product claims on this page against public sources in a few minutes.
Self-serve and a free tier are on the way as we open access. For now, the preview is invite-only.