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Head-to-head · updated 13 September 2026

DataShield vs Qdrant: the index is fast, but who decided the agent could read that chunk?

Qdrant is a very good vector search engine. Rust, open source, 30,000 plus GitHub stars, and a hero line that tells you exactly what it sells: "High-Performance Vector Search at Scale." Bayer runs 135 million points on it. It raised $50M in March 2026 and now runs everywhere from a managed cloud cluster to your own Kubernetes to a private beta build that sits on a robot. If your problem is recall at scale, buy Qdrant. We are not going to out-index them and we will not pretend we can.

Our problem is the other one. An index is content-blind by design: a Social Security number embeds just as happily as a product description, and qdrant-find will hand it back to whichever agent asked. Datasets are tokenized at ingest; agents query tokenized data over MCP; detokenization is a privileged, audited operation. 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 without trusting us. Below is the honest split, including the rows Qdrant wins outright.

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DataShield vs Qdrant at a glanceEight questions regulated buyers ask us about RAG. Scored from each vendor's public material. DataShield vs Qdrant at a glance Eight questions regulated buyers ask us about RAG. Scored from each vendor's public material. DataShield Qdrant Tamper-evident audit chain you can verify Authority re-checked on every tool call PII and PHI classified and tokenized before indexing GDPR erasure that keeps the chain valid Vector search speed and recall at scale Open-source traction and community On-device and edge retrieval Deployment choice shipped partial / roadmap not offered Sources at the bottom of this page.

The short version

Pick DataShield when

  • The text you want to retrieve has PII or PHI in it, and you would rather it never reached the model in raw form. We classify against 129 field classes, then tokenize at ingest. How the data plane works.
  • Someone will eventually ask you to prove the agent's retrieval log was not edited. Our chain answers with math, not a policy PDF. 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.
  • Embeddings must not leave your building. We embed locally with nomic-embed-text, so no text goes to a third-party embedding API.

Pick Qdrant when

  • Retrieval quality and speed are the project. Qdrant does one thing and does it very well: filtering inside HNSW traversal, native hybrid dense plus sparse search, multivectors, quantization.
  • You are past a hundred million vectors, or you will be. Bayer's search engine runs 135 million points on Qdrant with semantic caching and multitenancy. Our pgvector store is not aimed at that.
  • You want retrieval on the device: a robot, a phone, a machine on a factory floor. Qdrant Edge is an in-process engine for exactly that. We have nothing like it.
  • You want an open-source core with a large community around it, and the option to run it for free forever on your own hardware.

Bottom line: Qdrant governs the index. DataShield governs the data that goes into one and the agent that reads out of it. Most buyers who talk to us about RAG already run a vector database, and keeping it is usually the right call.

Feature by feature

Competitor cells describe what Qdrant's public site, docs and press say as of the date above. If we have mischaracterised something, email support@myorg.ai and we will correct it, credited.

What mattersDataShieldQdrantEdge
Vector search at scalepgvector with 768-dimension embeddings, hybrid keyword plus vector retrieval over a tsvector leg and a vector leg. Fine for a governed corpus. We publish no retrieval benchmark, because we have not run one worth publishing.The whole product, and it is good. Rust core, one-stage filtering during HNSW traversal, native hybrid search, multivectors, quantization down to a fraction of the memory. Published benchmarks against Elastic in July 2026.◇
Sensitive data handlingPII and PHI classification across 129 field classes with deterministic, reproducible verdicts, then deterministic, join-preserving, vault-reversible tokens applied at ingest. Quasi-identifier generalization too: dates to year, decade or age band, ZIPs to 3 or 4 digits, partial phones, SSNs and emails, each with a measured cardinality-reduction score.None. A vector database stores what you give it. Whatever is in the payload gets indexed and returned.◆
What reaches the modelEvery dataset value crossing into an LLM prompt passes a fail-closed egress gate: a PII or PHI column with no configured treatment is redacted, and a PHI dataset refuses an AI endpoint that is not marked BAA or ZDR approved. RAG chunk views also apply read-time masking, and the keyword index is rebuilt from the masked text so search cannot be used to probe for a redacted value.Not their job and they do not claim it. Chunks come back as stored.◆
EmbeddingsLocal nomic-embed-text, 768 dimensions, running on your own hardware. No text leaves for a third-party embedding API.Bring your own vectors, which keeps you in control. Qdrant Cloud also offers managed inference with selected models, and the free tier includes it. Convenient, but then your text is being embedded in their cloud.◆
Access controlEvery governed tool call passes a scope ceiling, a consented-tool allowlist, an authority tier, and a revocation re-check before dispatch. The call fails closed, and usage is metered and attributed to the agent.Real and improving. Admin keys, read-only keys, and granular access keys that scope read or write per collection. It is a database credential model, not a per-call authority decision, and the key does not know which human the agent is acting for.◈
Audit evidenceSHA-256 hash chain with Ed25519-signed checkpoints that are themselves chained. Verification returns clean, attested damage, or tampered, and names the failure: tampering, insertion, deletion, or truncation. Try the verifier.Audit logging arrived in v1.17.0 and a query API in v1.18.0. It writes JSON to a log file, rotates daily or hourly, and deletes the oldest file once the count passes a limit. It is off by default. Good forensics, not tamper-evident proof.◆
Agent authorization and MCPMore than 200 MCP tools across Ontology, Auth, Corpus and Lighthouse. Auth issues MCP tool tokens with scope ceilings, re-checks authority on every call, and delegates by RFC 8693 token exchange with an enforced scope ceiling.An official MCP server with two tools, qdrant-store and qdrant-find, plus a QDRANT_READ_ONLY switch that turns off storing. That is the access model: whoever holds the connection gets the tools.◆
Break-glassScoped, time-boxed emergency access for agents, admin and IP-allowlist gated, step-up authenticated, and it auto-revokes. It cannot be quietly deleted from the log.Not described in their public material.◆
GDPR erasureCrypto-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.You can delete points by filter, which is a real and useful primitive. Embeddings derived from the deleted text are yours to reason about, and the audit log files are yours to rotate.◆
DeploymentSelf-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 that 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.The widest spread in the category: open-source self-host, managed cloud, Hybrid Cloud on your own Kubernetes, air-gapped Private Cloud, and Edge in beta. In Hybrid Cloud they state the cloud receives telemetry over an outgoing connection only, with no access to your database or Kubernetes API.◈
On-device retrievalNothing. Our stack wants a Postgres.Qdrant Edge, an in-process engine for robotics, mobile and IoT, announced 29 July 2025 and still partner-selected private beta.◇
Maturity signalsAuth, Guardian and Lighthouse are live in production, Guardian and Lighthouse since April 2026. SOC 2 not yet certified, and we say so.Founded 2021, roughly $88M raised, $50M Series B in March 2026 with Bosch Ventures on the cap table, 250M plus downloads claimed, and a homepage badge reading SOC2 and HIPAA compliant.◇
PricingPublished model, scoped instant quote, no sales wall.A permanent free tier and a published calculator, which is more than most. Standard is usage-based with no dollar rate on the page, Premium carries a minimum spend, and Hybrid and Private Cloud are both contact-sales.◈

◆ DataShield leads◇ Qdrant leads◈ comparable

Qdrant claims are drawn from qdrant.tech, its documentation, its blog and its Series B coverage, 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 vector database

Tokenized before the model, not after

Redaction after retrieval is a race you lose once. We classify columns first, tokenize at ingest, and keep the tokens deterministic so joins and analytics still work. Detokenization is a separate privileged operation, tenant-scoped, and the lookup is refused if the audit write fails. See the data plane.

Authority that can change mid-flight

An analyst resigns on a Friday afternoon. Their research agent is 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 database API key just keeps working until someone rotates it. How Auth does it.

Evidence, not a log file

A rotating JSON file tells you what probably happened. A hash chain with signed checkpoints tells an examiner what definitely did, and names the break if someone tried. 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 Qdrant is genuinely stronger

Let's be clear about the engineering. Qdrant is written in Rust, filters inside the HNSW traversal instead of before or after it, ships native hybrid dense and sparse search, and quantizes hard enough to fit billions of vectors in memory people can afford. Bayer built an enterprise search engine on it at 135 million points. It beat Elastic's DiskBBQ on throughput and latency in a July 2026 benchmark, and it published the method. The deployment spread is the widest in the category, and Hybrid Cloud is an honest design: the control plane takes telemetry over an outgoing connection and never touches your database or your Kubernetes API. Their security docs are candid too, including the line that a self-hosted open source instance is not secure by default. I trust a vendor more, not less, when they write that down.

Here is the push-back. Everything Qdrant governs stops at the collection boundary. Their access model is a credential: an admin key, a read-only key, or a granular key scoped to a collection. It has no idea which person the agent is acting for, whether that person's access was revoked four minutes ago, or whether the chunk coming back contains a patient identifier. Their audit log is off by default and rotates the oldest file away on a schedule, which is fine for debugging and useless as evidence when a regulator asks for March. None of that is a criticism of a vector database. It is the description of one. The obligation still has to land somewhere, and if you are in a regulated shop, it lands on you.

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. Qdrant: their audit logging, added in v1.17.0, writes JSON files that rotate daily or hourly, with the oldest deleted once you pass max_log_files. There is no tamper-evidence mechanism described. Ask them what stops an operator editing the file.

What happens to a revoked agent mid-session?

DataShield re-checks authority on every governed tool call, so revocation lands on the next call and the context drops to anonymous. Qdrant's model is an API key held by the client. Revoking means rotating or deleting the key, and we could not find a documented mid-session invalidation path. Ask how long a compromised agent keeps querying after you pull its access.

How does GDPR erasure interact with the audit trail?

DataShield crypto-shreds the per-subject key material, which makes every ciphertext for that subject unreadable at once, and issues an ISO 27560 consent receipt. Actor identities in the chain are HMAC-committed, so the evidence still verifies. Qdrant lets you delete points by filter, which handles the stored payload. Ask what happens to embeddings derived from the erased text, and to the audit log entries that named the subject.

Does Qdrant mask or tokenize PII before it is embedded?

No, and they do not claim to. A vector database indexes what you hand it. That means the scrubbing has to happen upstream, in your ingest pipeline, written by you and reviewed by nobody in particular. We do it as a product step: classification against 129 field classes, tokenization at ingest, generalization strategies on quasi-identifiers, and a fail-closed gate on anything heading into a prompt. If you keep Qdrant, this is the layer you are still missing.

We already run Qdrant. Do we rip it out?

Usually not. If you have tens of millions of vectors and a latency budget, keep it. Govern the corpus upstream with us, index the tokenized text in Qdrant, and put the agent's tool calls behind Auth so the retrieval is authorized and the decision is sealed. Where we do replace a vector database is the smaller, nastier case: a regulated corpus where the compliance requirement is the whole reason the project exists and nobody wants a second system to secure.

Qdrant's MCP server is two tools. Is that actually a problem?

It depends who is holding the connection. qdrant-store and qdrant-find with a QDRANT_READ_ONLY switch is a clean design for a personal agent memory. It is thin for a shared corpus, because the tools carry no notion of which subject the caller may see, no per-call decision point, and no audit tie-back to a human. Ours issue MCP tool tokens with scope ceilings and re-check authority per call. Different jobs. Ask them whether store and find are all-or-nothing once a client connects.

Does DataShield have SOC 2?

Not yet, and we will not imply otherwise. Qdrant's homepage carries a SOC2 and HIPAA badge and ours does not. What we publish instead is a threat model, a verifier anyone can run, and design-partner terms that include source escrow so a small vendor is not a single point of failure. Details on the security page.

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