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 have been grading their own answers since 2023. HHEM, their hallucination model, has about four million downloads on Hugging Face. Their Hallucination Corrector rewrites a bad answer and tells you why. Their Tool Validator reads an agent's plan and flags tool calls that do not belong. If your problem is "the bot makes things up," they have spent five years on it and we have not.
We start one step earlier, at the data. Datasets are tokenized at ingest; agents query tokenized data over MCP; detokenization is a privileged, audited operation. Embeddings run locally with nomic-embed-text, so no chunk of your corpus leaves to be vectorised. Every governed tool call is re-checked against the agent's current authority before it runs, and the decision is sealed into a hash chain you can verify yourself. Below is the split, rows they win included.
The short version
Pick DataShield when
- The data going into retrieval has PII or PHI in it and 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 access 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 authority 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, and an open-source eval framework are their core product, and they are 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, and you would rather buy that than build it.
Bottom line: Vectara makes the answer right. We make the access provable. Those are different obligations, and a regulated buyer usually has 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 as of the date above. If we have mischaracterised something, email support@myorg.ai and we will correct it, credited.
| What matters | DataShield | Vectara | Edge |
|---|---|---|---|
| Retrieval and answer quality | Hybrid retrieval: a keyword leg over tsvector and a vector leg over pgvector, 768 dimensions. It works. We publish no accuracy number and we run no eval framework, because we have not built one. | The core product, and the best part of it. Boomerang retrieval, Mockingbird generation, rerankers, and an open-source RAG evaluation framework shipped in April 2025. | ◇ |
| Hallucination detection | None. We do not score whether an answer is grounded in the source text. | HHEM scores factual consistency against the source documents. The Hallucination Corrector explains the flag and rewrites the answer, with five ways to surface the change to a user. | ◇ |
| Embeddings and egress | Embeddings are generated locally with nomic-embed-text at 768 dimensions. No chunk of your corpus is sent to a third party to be vectorised. | Boomerang is theirs, and on-prem is offered, so egress depends on the tier you buy. Bring Your Own Model routes to ChatGPT, Claude or Gemini, which is external by definition. | ◆ |
| Tokenization and sensitive fields | Datasets are tokenized at ingest; agents query tokenized data over MCP; detokenization is a privileged, audited operation. Tokens are deterministic, join-preserving and vault-reversible. Quasi-identifier generalization covers dates to year, decade or age band, ZIPs to 3 or 4 digits, and partial phones, SSNs and emails, with a measured cardinality-reduction score per column. | We found no tokenization, PII detection or masking claim on their site, pricing page or the authorization docs. Ask them where in the pipeline a social security number stops being readable. | ◆ |
| Classification of sensitive data | 129 field classes covering PII, PHI, financial data and secrets, including all 18 HIPAA Safe Harbor identifiers. Regex plus checksum validation, column-name lexicons and anti-pattern suppressors. No model, so verdicts are reproducible from a config digest. It shipped days ago, and we would rather say that than imply a long track record. | Not part of the product as far as their public material goes. | ◆ |
| Prompt path | We do not proxy your LLM traffic. We do gate every value that leaves a governed dataset for a prompt, and a PHI dataset refuses an endpoint without a BAA. A classified column with no configured treatment is redacted, not passed through. | Guardian Agents act on the output side: a Tool Validator reviews planned tool calls, and the corrector fixes the generated answer. Nothing we found gates the values on the way in. | ◆ |
| Agent authorization | Every governed tool call passes a scope ceiling, an authority tier, and a revocation re-check before dispatch, with per-call metering attributed 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 effective permissions are the union of the roles you hold. That is static scoping, not a per-call decision. | ◆ |
| Audit evidence | SHA-256 hash chain with Ed25519-signed checkpoints that are themselves chained. Verification returns clean, attested damage, or tampered, and names the failure. Try the verifier. | The Tool Validator logs its flags and adjustments for traceability. We found no cryptographic tamper evidence in their public docs. | ◆ |
| Break-glass | Scoped, time-boxed emergency access for agents. Admin plus IP allowlist plus step-up, fully audited, and it auto-revokes. | Not described in their public material. | ◆ |
| GDPR erasure | Crypto-shred of per-subject key material plus ISO 27560 consent receipts. Actor identities in the chain are HMAC-committed, so the audit still verifies after the subject is gone. | Not described in their public material. 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, and delegation is RFC 8693 token exchange with an enforced ceiling. | A real open-source MCP server exposing RAG query, semantic search, hallucination detection and factual-consistency scoring as tools. Their own blog describes token validation against an enterprise policy store as a pattern the protocol enables. We could not confirm it is shipped in their server. | ◆ |
| Deployment | Self-hosted in your own cloud or data center, or a dedicated single-tenant server we operate. Docker images for Auth, Ontology, Corpus and Lighthouse, with a signed deploy manifest Guardian verifies. Ed25519 audit-signing keys can live in your KMS or HSM. HMAC tokenization keys sit in your environment or derive from your machine key today, not in a KMS. | SaaS, customer-managed cloud, and on-premise, all three sold openly. That is unusually flexible for a managed RAG vendor and we will not 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, and 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. | Published and refreshingly blunt: $100K a year for SaaS, $250K for customer-managed cloud, $500K on-premise, each covering one deployment. A 30-day trial includes 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. It is also the last one. We take the sensitive columns out of play at ingest, and every value that crosses into a prompt passes a fail-closed gate on the way. A PHI dataset refuses an AI endpoint that has 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 retrieval job. With DataShield the next governed tool call is re-checked against current authority and fails closed. A role table updated at the weekend does not do that. How Auth does it.
Proof that survives an audit
A log that can be quietly edited proves nothing. Ours is a hash chain with signed checkpoints, and the verifier tells you what broke, not just that something did. That is the property EU AI Act Article 12 and HIPAA §164.312(b) reviewers care about. Try it in your browser, no signup.
Where Vectara is genuinely stronger
They have a real technical asset and it is not marketing. HHEM compares a generated answer against the source text it was drawn from, and it has been downloaded about four million times, which is the kind of number you cannot buy. The Hallucination Corrector claims it pushes models under 7B parameters below a 1% hallucination rate. Their Tool Validator, launched in December 2025, cites their own benchmark showing open-source agent frameworks landing tool-call accuracy anywhere from 5% to 59%, which is a sobering figure and a fair reason to sell a validator. We ship none of that. We also ship no retrieval model, no generation model, and no eval framework, and 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 and advises. A corrector fixes text after the model wrote it. Both improve the answer. Neither stops a retrieval that should never have been allowed, and neither leaves an examiner an artefact they can check without trusting the vendor. Their own authorization docs describe four tiers of roles and a union of permissions, which is a sensible scoping model and a static one. Ask what happens to a running agent at the moment you revoke a role. Ask where a social security number in an indexed document stops being readable. We have opinions on both, and code you can run.
One more honest note, since it cuts against us. Our own RAG indexes document free text as it arrives, apart from a standing sensitive-pattern filter on chunk views. Structured dataset columns get the full tokenization and masking treatment; a PDF full of prose does not. If your corpus is entirely unstructured documents, say so on the call and we will 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 commits to the one before it, checkpoints are signed and chained, and verification tells deletion apart from truncation and from tampering. Run it against a sample chain at /verify. Vectara: their Tool Validator release describes logging flags and adjustments for traceability. We found no tamper-evidence mechanism in their public docs. Ask them to show one.
What happens to a revoked agent mid-session?
DataShield re-checks authority on every governed tool call, so revocation lands on the next call. Vectara's docs describe four role tiers and say effective permissions are the union of assigned roles. We could not find a mid-session re-check in their public material. Ask how long a revoked agent keeps retrieving.
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, so the evidence still verifies once the subject is gone. Vectara does not describe an erasure mechanism that we could find. For a RAG platform the question has teeth, because 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 and generally available: pgvector, local embeddings, hybrid keyword and vector retrieval, ingest from files, URLs and S3-compatible storage, with text pulled out of PDF, DOCX and EML. Corpora promote and demote between personal and org-visible, and that is audited. What we do not have is their hallucination scoring, their models, or petabyte-scale references. If you are shopping purely on answer quality, they are the safer buy. If you are shopping for governed retrieval over regulated data you self-host, start with us.
Vectara ships an MCP server too. What's different?
Theirs exposes retrieval, generation and hallucination scoring as tools, and it is open source, which is a good thing to do. Ours is where the governed data itself is queried. The tool token carries a scope ceiling, the call is authorized before dispatch, usage is metered against the agent, and the decision is sealed into the chain. Their blog on MCP argues for validating tokens against an enterprise policy 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, which we respect more than the usual "contact sales" wall: $100K SaaS, $250K customer-managed cloud, $500K on-premise, one deployment each. For that money they include a model stack and, on larger plans, a forward-deployed engineer. We include neither. Compare what is in the box, not just the floor.
Does DataShield have SOC 2?
Not yet, and we will not imply otherwise. Auth is live with a public threat model and a verifier anyone can run. Guardian and Lighthouse have been in production since April 2026. Design-partner terms include source escrow, so a small vendor is not 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
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