Head-to-head · updated 13 September 2026
DataShield vs Vectara: one checks the answer, one checks the permission
Vectara calls itself "The Enterprise Agent Platform," and has earned the RAG half of that. They've graded their own answers since 2023. HHEM is their hallucination model. It has about four million downloads on Hugging Face. Their Hallucination Corrector rewrites a bad answer. It tells you why. Their Tool Validator reads an agent's plan. It flags tool calls that don't belong. If your problem is "the bot makes things up," they've spent five years on it. We haven't.
We start one step earlier. At the data. Datasets get tokenized at ingest. Agents query tokenized data over MCP. Turning a token back into real data: that's a locked, logged step. Embeddings run locally, with nomic-embed-text. No chunk of your corpus leaves, to be vectorised. Each checked tool call gets re-checked against the agent's live access. Before it runs. The result gets sealed into a hash chain. You can check it yourself. Below is the split. Some rows: they win.
The short version
Pick DataShield when
- The data going into retrieval has PII or PHI in it. You want it tokenized at ingest, not masked on the way out. How the data plane works.
- Someone will ask you to prove an agent's log was not edited. An examiner, an auditor, or Article 12 of the EU AI Act. Our chain answers with math. Run the verifier.
- You need to pull an agent's access mid-session, and have the very next tool call fail. Not the next token refresh. The next call.
- Six figures a year is not where your first contract starts.
Pick Vectara when
- Answer quality is the whole job. Hallucination scoring, correction. An open-source eval framework. That's their core product. They're good at it.
- You want models included. Boomerang for retrieval, Mockingbird for generation, or bring your own. We ship no models of our own.
- You need retrieval at petabyte scale. Across messy document sets. With a forward-deployed engineer in the contract.
- A packaged chat UI, and an Agent API, get you to a working assistant. In weeks. You'd rather buy that than build it.
Bottom line: Vectara makes the answer right. We make the access provable, instead. Those are different duties. A regulated buyer usually needs both. Run them for retrieval quality, if you like. Run us underneath, where the sensitive columns and the audit trail live.
Feature by feature
Competitor cells describe what Vectara's public site, docs and press releases say. We use the date above. If we got something wrong, email support@myorg.ai. We'll fix it and credit you.
| What matters | DataShield | Vectara | Edge |
|---|---|---|---|
| Retrieval and answer quality | Hybrid retrieval: a keyword leg over tsvector. A vector leg over pgvector, 768 dimensions. It works. We publish no accuracy number, and we run no eval framework. We haven't built one. | The core product, and the best part of it. Boomerang retrieval, Mockingbird generation, rerankers. An open-source RAG check framework, shipped April 2025. | ◇ |
| Hallucination detection | None. We don't score whether an answer is grounded in the source text. | HHEM scores factual match against the source documents. The Hallucination Corrector explains the flag. It rewrites the answer. Five ways to show the change to a user. | ◇ |
| Embeddings and egress | Embeddings are made locally, with nomic-embed-text, at 768 dimensions. No chunk of your corpus is sent to a third party. None gets vectorised there. | Boomerang is theirs. On-prem is offered too. Egress depends on the tier you buy. Bring Your Own Model routes to ChatGPT, Claude or Gemini. That's outside by definition. | ◆ |
| Tokenization and sensitive fields | Datasets get tokenized at ingest. Agents query tokenized data over MCP. Turning a token back into real data: that's a locked, logged step. Tokens are fixed, join-safe, and vault-reversible. Quasi-identifier shrinking covers dates to year, decade, or age band. Plus ZIPs, to 3 or 4 digits. Phones, SSNs and emails get part masked. Each column gets a measured shrink score. | We found no tokenization claim on their site. No PII detection claim. No masking claim, on the price page or the access docs. Ask them where in the pipeline a social security number stops being readable. | ◆ |
| Classification of sensitive data | 129 field classes covering PII, PHI, money data and secrets. That includes all 18 HIPAA Safe Harbor identifiers. Regex plus checksum checks, name lists, and anti-pattern filters. No model, so results are repeatable from a config digest. It shipped days ago. We'd rather say that than fake a long track record. | Not part of the product, as far as their public docs go. | ◆ |
| Prompt path | We don't proxy your LLM traffic. We do gate each value that leaves a checked dataset for a prompt. A PHI dataset refuses an endpoint with no BAA. A tagged column with no set rule gets redacted. Not passed through. | Guardian Agents act on the output side. A Tool Validator reviews planned tool calls. That's after the fact. The corrector fixes the answer, after it's made. Nothing we found gates the values on the way in. | ◆ |
| Agent authorization | Each checked tool call passes a scope ceiling. An access tier. A revocation re-check. Before dispatch. Per-call metering, tied to the agent. The call fails closed. | Role-based access control, in four tiers. API roles. Corpus roles. Agent roles. Platform roles. Their docs say your effective rights are the sum of the roles you hold. Fixed at grant time. That's fixed scoping. Not a per-call decision. | ◆ |
| Audit evidence | A SHA-256 hash chain, with Ed25519-signed checkpoints. Those checkpoints are themselves chained. Checking returns clean, damaged, or tampered. It names the failure. Try the verifier. | The Tool Validator logs its flags and edits, for tracing. We found no cryptographic tamper check in their public docs. | ◆ |
| Break-glass | Scoped, time-boxed emergency access for agents. Admin, plus IP allowlist, plus step-up. Fully logged. It auto-revokes. | Not named in their public docs. | ◆ |
| GDPR erasure | Crypto-shred of per-subject key material, plus ISO 27560 consent receipts. Actor identities in the chain are HMAC-committed. The audit still checks out, after the subject is gone. | Not named in their public docs. For a retrieval product this is a fair question. Chunks and embeddings both hold the subject. | ◆ |
| MCP and agents | More than 200 MCP tools, across Ontology, Auth, Corpus and Lighthouse. Auth issues MCP tool tokens with scope ceilings. Delegation is RFC 8693 token exchange, with a locked ceiling. | A real open-source MCP server. It exposes RAG query, semantic search, hallucination detection, factual-match scoring, as tools. Their own blog talks about checking tokens against an enterprise rule store. As a pattern the protocol allows. We couldn't confirm it's shipped in their server. | ◆ |
| Deployment | Self-hosted, in your own cloud or data center. Or a dedicated single-tenant server we run for you. Docker images for Auth, Ontology, Corpus and Lighthouse. A signed deploy file. Checked by Guardian. Ed25519 log-signing keys stay on your box. In a key folder you control. Rotation is kid-keyed. It fails closed on an unknown key. KMS or HSM custody is a built seam in Auth. Not a shipped link. HMAC tokenization keys sit in your setup. Or come from your machine key today. Not in a KMS. | SaaS, customer-managed cloud, and on-prem, all three sold openly. That's unusually flexible for a managed RAG vendor, and we won't pretend otherwise. | ◈ |
| Maturity signals | Founded later, smaller, and live in production. Guardian and Lighthouse since April 2026. SOC 2 not yet certified, and we say so. | Founded 2020. About $73.5M raised. Broadcom and Anywhere Real Estate as named references. A first-half 2026 release reporting more than 100% new revenue growth. | ◇ |
| Pricing | Published model, scoped instant quote, no sales wall. Our floor is not six figures. | Public, and refreshingly blunt. $100K a year for SaaS. $250K for customer-managed cloud. $500K on-prem. Each covers one deployment. A 30-day trial has all features. | ◈ |
◆ DataShield leads◇ Vectara leads◈ comparable
Vectara claims are drawn from vectara.com, docs.vectara.com and Vectara's own press releases. Last checked 13 September 2026. We link them below rather than work from memory.
Three things you get here that you won't get from a managed RAG platform
The model never sees the raw field
Output checking is a fine layer. But it's also the last one. We take the sensitive columns out of play at ingest. Each value that crosses into a prompt passes a fail-closed gate. On the way in. A PHI dataset refuses an AI endpoint with no BAA on file. See the data plane.
Authority that can change mid-flight
An analyst leaves on a Friday. Their agent is still 20 minutes into a 40-minute job. The next checked tool call gets re-checked against live access. It fails closed. A role table, updated over the weekend, doesn't do that. How Auth does it.
Proof that survives an audit
A log that can be quietly edited: it proves nothing. Ours is a hash chain with signed checkpoints. The verifier tells you what broke. Not just that something did. That's the trait EU AI Act Article 12 reviewers care about. Same for HIPAA §164.312(b). Try it in your browser, no signup.
Where Vectara is genuinely stronger
They have a real technical asset. It's not marketing. HHEM checks a generated answer against the source text. The text it was drawn from. It's been downloaded about four million times. That's a number you can't buy easily. The Hallucination Corrector makes a claim. It pushes models under 7B parameters below a 1% hallucination rate. Their Tool Validator launched December 2025. It cites their own benchmark: open-source agent frameworks land tool-call accuracy anywhere from 5% to 59%. That's a sobering figure. A fair reason to sell a validator. We ship none of that. We also ship no retrieval model. No generation model. No eval framework. Their on-prem tier follows a customer all the way. To an air-gapped room.
The push-back is about what each layer can refuse. A Guardian Agent reviews a plan. It advises. A corrector fixes text after the model wrote it. Both improve the answer, sure. Neither one stops a bad retrieval. One that should never have been allowed. Neither leaves an examiner a record. One they can check without trusting the vendor. Their own access docs describe four tiers of roles. Plus a sum of rights. That's a sensible scoping model. And a fixed one. Ask what happens to a running agent. The moment you pull a role. Ask where a social security number in an indexed document stops being readable. We have answers to both. Plus code you can run.
One more honest note, since it cuts against us. Our own RAG indexes document free text as it arrives. The exception: a standing filter on chunk views, for touchy patterns. Structured dataset columns get the full tokenization and masking treatment. A PDF full of prose doesn't get that. If your corpus is entirely unstructured documents: say so on the call. We'll tell you plainly what we cover.
Questions worth asking both of us
These are the questions we would want answered if we were the ones buying. Ask them on every call, ours included.
Can you cryptographically prove an audit log entry wasn't deleted?
DataShield: yes. Each record ties to the one before it. Checkpoints are signed and chained. Checking tells deletion apart from truncation and from tampering. Run it against a sample chain at /verify. Vectara: their Tool Validator release talks about logging flags and edits. For tracing. We found no tamper-check mechanism in their public docs. Ask to see one.
What happens to a revoked agent mid-session?
DataShield re-checks access on each checked tool call. Revocation lands on the next call. Vectara's docs describe four role tiers. Your rights are the sum of assigned roles. We couldn't find a mid-session re-check in their public docs. Ask how long a revoked agent keeps retrieving data.
How does GDPR erasure interact with the audit trail?
DataShield crypto-shreds per-subject key material, and issues an ISO 27560 consent receipt. Actor identities in the chain are HMAC-committed. The proof still checks out once the subject is gone. Vectara doesn't name an erasure mechanism. None that we could find. For a RAG platform, this question has teeth. A subject lives in the chunks. And in the embeddings. Not just in a row.
Is DataShield a Vectara alternative for enterprise RAG?
Partly. Our RAG is real. Generally available too. Pgvector. Local embeddings. Hybrid keyword and vector retrieval. Ingest from files, URLs, S3-compatible storage. Text gets pulled out of PDF, DOCX, EML. Corpora move between personal and org-visible. That's logged. What we don't have: their hallucination scoring. Their models. Petabyte-scale references. If you're shopping purely on answer quality: they're the safer buy. If you're shopping for checked retrieval over regulated data you self-host: start with us.
Vectara ships an MCP server too. What's different?
Theirs exposes retrieval, generation, hallucination scoring, as tools. It's open source, which is a good thing to do. Ours is where the checked data itself is queried. The tool token carries a scope ceiling. The call gets checked before dispatch. Usage is metered against the agent, too. The result gets sealed into the chain. Their blog on MCP argues for checking tokens against an enterprise rule store. Before running a sensitive tool. We agree with them. We also built it.
Their price starts at $100K a year. What's yours?
Lower, and on a page you can read without a call. Their pricing is public too. We respect that more than the usual "contact sales" wall. $100K SaaS. $250K customer-managed cloud. $500K on-prem. One deployment each. For that money they include a model stack. On larger plans, a forward-deployed engineer too. We include neither. Compare what's in the box, not just the floor.
Does DataShield have SOC 2?
Not yet, and we won't imply otherwise. Auth is live, with a public threat model and a verifier anyone can run. Guardian and Lighthouse have run in production since April 2026. Design-partner terms include source escrow. So a small vendor isn't a single point of failure. Details on the security page.
- Vectara's current positioning: "The Enterprise Agent Platform", "For building AI agents across SaaS, VPC, and On-Prem". - vectara.com, 13 Sep 2026
- Published pricing: $100K/year SaaS, $250K/year customer-managed cloud, $500K/year on-premise, one deployment each, with a 30-day trial including all features. - vectara.com/pricing, 13 Sep 2026
- Authorization model: role-based access control with four tiers (API, corpus, agent, platform); a user's effective permissions are the union of all assigned roles. - docs.vectara.com, 13 Sep 2026
- Hallucination Corrector: reduces hallucination rates for LLMs under 7B parameters to less than 1%; works with HHEM, which has 4 million downloads on Hugging Face. - PR Newswire, 13 May 2025
- Tool Validator Guardian Agent reviews an agent's planned tool calls and flags erroneous or irrelevant ones before execution, citing open-source tool-call accuracy of roughly 5% to 59%. - PR Newswire, 11 Dec 2025
- Open-source RAG evaluation framework for accuracy, reliability and explainability in AI agents. - PR Newswire, 8 Apr 2025
- Vectara's open-source MCP server exposes RAG query, semantic search, hallucination detection and factual-consistency evaluation as MCP tools. - github.com/vectara/vectara-mcp, 13 Sep 2026
Other head-to-heads
DataShield vs Contextual AI
Grounded answers as a platform, versus governed data underneath.
Vector DBDataShield vs Pinecone
A vector index at scale, and the policy it doesn't carry.
SearchDataShield vs Glean
Enterprise search over everything, versus authority per call.
AllEvery comparison
One honest scorecard per vendor.
See both mechanisms run in your browser. Break a live audit chain. Revoke an agent mid-session. Then ask your RAG vendor which of the two they cover. Demo Center access is free, with a work email.
Get free Demo Center accessYou've seen the proof
Ready for a number? Scope your deployment and we'll price it against your own economics.
Get your quote →