Entity Bind vs Senzing
A mature engine you run yourself, at no cost per decision. It resolves what you give it.
Keep Senzing for the matching you already do inside your own estate. Entity Bind takes the rows your columns cannot settle: we go to the register for the deciding fact, refuse below a published floor, and write a receipt somebody can read months later.
Your company master has to survive somebody else’s audit, and every merge in it needs a reason a person outside your team can read. This page compares on that axis: what each side is for, where the alternative is stronger, and what we would do in your place.
Senzing: A mature engine you run in your own estate, priced per record held rather than per decision, and it resolves people too. Entity Bind: Hosted, companies only, and it goes to the register for the evidence your file does not contain — then cites the page it read.
5 of the 12 rows in the table below go to Senzing and 6 go to Entity Bind. The rows on their side are the reason to keep what you already run; the rows on ours are what we would add beside it.
The short answer
Senzing is a real entity resolution engine and one of the few in this category that publishes a price you can read before a call. You deploy it, you feed it your records, and it resolves them inside your own infrastructure with no per-decision charge, no egress and no third party in the path — and it handles people and addresses as well as organisations. For a team that needs an engine over tens of millions of its own rows, nothing here substitutes for it, and the two run together: the engine over the bulk of the file, us on the rows it leaves unresolved. Entity Bind is a different shape: a hosted decision layer that goes out to the register on your behalf, cites the page it read, publishes what it refuses to decide, and hands back a receipt that reopens at a URL. An engine resolves what you give it. The rows that stay hard in most files are hard because what you gave it was not enough, and closing that gap with outside evidence — then declining when the evidence still will not carry a match — is the whole of what we sell.
The answer for your own rows needs your own rows: a few hundred of them, with the matches you already have beside them.
Start freeOn this page
What Entity Bind adds beside Senzing
Run both, and this is the half we do. Each of these is a job the other side of this page is not built for.
- The discriminating evidence is not in your file, and somebody has to go and read the register to settle it.
- A merge has to be defensible a year later from an evidence trail rather than from a match key.
- You want an admission policy you did not write, published, and not quietly movable by whoever owns the deployment.
- You need per-field provenance, licence and freshness carried on the record rather than reconstructed afterwards.
- You want an answer on a real file this week, without standing anything up.
A stewardship queue and a supplier file before a payment run want different halves of that list. Create your Free workspace to inspect your own rows, or contact us for a guided review of the checks your workflow needs.
Start freeThe case for Senzing
Read these before the table. On some evaluations one of them settles the question on its own, and if it settles yours, keep what you have and put us beside it.
- It runs in your estate. No record leaves your network, no model provider ever sees a customer name, and there is no external dependency that can be down in the middle of a decision. For some buyers that settles it, and no argument of ours beats it.
- No cost per decision. Their own published list price is $58,500 a year at ten million records — a vendor figure we have not audited, and unusually one that is published at all. Above a certain volume that beats per-decision pricing outright and it is not close.
- Scope beyond companies: people, organisations and addresses in one engine, with alias, transliteration and household logic that we do not have and are not going to build.
- Real-time incremental resolution at a scale we have not tested at. Entities re-form as new records arrive, across hundreds of millions of rows, in production, for years.
- No training data required. It works on your records as they are, without a labelled sample, a fitting round or a data scientist to own the model.
- Maturity and standing. Long-running deployments, an implementation ecosystem, and an answer to every question a bank's third-party risk function asks. We score ourselves 3 out of 15 on external validation.
Requirements that point to Senzing
- Records cannot leave your infrastructure, and a hosted API is not on the table at any price.
- You are resolving people, households or addresses as well as companies.
- Volume is high enough that a per-record licence beats per-decision pricing, which happens sooner than most buyers expect.
- You want an engine to embed in a system you are building rather than a service to call.
- Procurement needs an established vendor with production references and an assurance package. There is no SOC 2 here and none in progress.
If one of those is your requirement, say so when you write to us. We will tell you where the answer is Senzing, and what running Entity Bind beside it would add: an independent verdict on each match, with the register page that supports it. Cheaper for both of us than finding out in month three.
Entity Bind and Senzing, dimension by dimension
Every row carries a verdict. 5 go to Senzing, 6 to Entity Bind and 1 is even. Both columns are filled in on every row, so a verdict can be read against what each side actually does about it.
| Dimension | Senzing | Entity Bind | Edge |
|---|---|---|---|
| Where the data goes | Your own infrastructure. Nothing leaves it. | Hosted. The no-egress depth setting makes no external call of any kind — no model provider, no registry — but the depths that resolve the hard tail send the record's fields and any free-text context to the model provider. | Senzing |
| Cost model | A per-record licence, published: $58,500 a year at ten million records on their own price list, which we have not audited. Nothing per decision. | Per decision — one credit shallow, five deep, zero for an abstention and zero for a byte-identical replay — with the caveat that usage metering runs in shadow mode and there are no automatic usage or overage charges. | Senzing |
| Entity types | People, organisations and addresses, with the alias and transliteration handling that implies. | Companies and organisations only. Not people, not vessels, not addresses in their own right, and none of our accuracy language transfers to a different ontology. | Senzing |
| Scale demonstrated | Hundreds of millions of records, incrementally, in production, for years. | Fixtures of 317 companies and the continuous-integration gate's own fixture. We have not run at their scale. | Senzing |
| External validation | Years of deployment, an implementation ecosystem, callable references. | Internal fixtures only. No customer holdout, no independent laboratory validation, no third-party penetration test, no SOC 2. | Senzing |
| Getting started | An engine to deploy, tune and operate. | One API call or one MCP tool with a key from your Entity Bind workspace, and a free preview we run over your file that calls nothing and spends nothing. Self-hosting is a conversation rather than a product: there is no managed offering and no hosting region today. | Even |
| Evidence brought to the decision | The records you supply, resolved against each other and against whatever else you loaded. | The register: legal names, previous names, trading names, status, town, legal form and incorporation date, plus registry and web research where the tail needs it, each cited with a URL you can open. | Entity Bind |
| What the confidence number means | An explanation naming the features and principles that fired, which is genuinely more than most engines give. | The same kind of explanation, plus a number with a defined meaning: how often decisions like this one turned out right, read at the cautious end of the range — the 95% Wilson lower bound over that population — recomputed from recorded counts by the test suite so an edited constant fails the build. Confidence bounds depend on the recorded evidence population; there is no universal score for every match. Read match_confidence_basis and the evidence. A 0.99 filter can return some v8 adjudicated matches while excluding valid identifier matches, so validate its coverage cost on your own inputs. | Entity Bind |
| Behaviour on a genuinely ambiguous record | A resolution, or two entities left apart, under rules you configure. | A match below 0.90 confidence is unrepresentable. The record comes back as an abstention with a ranked shortlist instead. The abstention carries a ranked shortlist, a typed cause and the cheapest of your own columns that would have settled it, at zero credits. | Entity Bind |
| Auditability of one decision | The resolved entity and its match keys are the artefact. | The decision is the artefact: a decision id that reopens the features, the thresholds it was judged under, the cited evidence, the model and prompt version where one ran, and a re-scoring from the stored features. | Entity Bind |
| What you may store and resell | Your deployment, your data, your responsibility. | A licence tier on every fact and a refusal on the write path before any write, with resale rights reported per field and carried on the open-licensed subset on the Scale plan. | Entity Bind |
| Published failure numbers | Whatever they publish, we have not audited — and a vendor benchmark on a different denominator is not comparable to ours in either direction. | Every rate with its denominator and its interval, worst row included: truncated names resolve 58.10% (61/105). The blend is 95.75%, published with the warning that belongs to it: A historical 2026-08-29 blend across six input shapes in a preloaded UK catalogue. Its value depends on that mix: truncated names measured 58.10% while the blend measured 95.75%. Neither establishes the current minimum-input target. | Entity Bind |
Three limits sit behind every comparison in this section — what we hold, where we stand and how ready we are — and the comparison hub states all three at full size. The figures in our own column were measured on internal fixtures; the accuracy page carries the method and the population behind each one.
- No SOC 2. It has not been started.
- No independent external validation of the accuracy figures. Every fixture is one we built.
- No production customer holdout. The evidence is internal.
A table cannot weigh those rows against your file, and neither can a fixture of ours — your own records are the only ones that settle it.
Start freeBuilt for, and wrong for
Neither of these depends on which alternative you are weighing. They are the two answers that decide whether the comparison above is worth your time at all.
Teams with a company master they have to defend — an MDM or stewardship queue, a CRM after a migration or a merger, a supplier file before a payment run, or counterparty onboarding that has to survive an audit.
Buying breadth as the product: we hold a fraction of an incumbent’s records and are not building a global company graph. Entity Bind resolves companies and organisations only. It does not resolve people, vessels, bank-account ownership, sanctions exposure, emissions or geolocation, and its results may not be used for eligibility decisions about individuals, including employment, credit and tenancy.
Where the figures on this page come from
Only the numbers that are not ours are listed here. Each one is the vendor’s own published claim. We have not audited anybody’s coverage figure, and as far as we know neither has anybody outside the company that published it.
- Senzing published pricing
The vendor's own published list price, recorded as a claim rather than as verified evidence. We have not audited it, and their pricing may have changed since we read it. Recorded on 2026-09-02.
The method behind our half of this comparison
2 articles that set out how the decision is made and measured, rather than how it compares.
- GuideWhat is entity resolution?Entity resolution decides which records refer to the same real-world thing. What it is, why company identity is hard, deterministic versus probabilistic matching, and why fuzzy matching is not entity resolution.
- GuideHow to measure entity resolution accuracyPrecision, recall, abstention, pairwise versus B-cubed cluster metrics, Wilson intervals and out-of-fold evaluation. Why a single accuracy number for entity resolution is always meaningless, with worked arithmetic.
Prefer to put it to a person? Talk to us instead →
The other options you are weighing
The other 11 comparisons, on the shelves the hub uses. Four are not vendor contests: two are the options that beat most vendors most of the time — the fuzzy match you already run, and building it yourself — and two are categories we refuse rather than compete in.
Data providers and registers
Entity Bind vs OpenCorporates
2/9 rows to themAn excellent register mirror. A register mirror is not a decision.
Entity Bind vs Companies House
3/12 rows to themFree, authoritative, and the right answer for a lot of UK teams. It is one of our sources too.
Entity Bind vs PermID
4/11 rows to themFree permanent identifiers, and largely unrivalled in capital markets. We do not issue them.
Resolution engines and platforms
Entity Bind vs Splink
5/12 rows to themFree, open source, and it weighs the evidence properly: Fellegi–Sunter, done right. Our scoring stage is the same family.
Entity Bind vs AWS Entity Resolution
5/12 rows to themCheap, inside your own account, and it needs no new vendor. It also matches only what you loaded.
Entity Bind vs Tamr
5/12 rows to themMachine-learning mastering at enterprise scale. It learns your labels — including the wrong ones.
Entity Bind vs MDM platforms
5/12 rows to themThey are the system of record. We are a decision layer beside one, and we write nothing.
What you would otherwise do
Not what we are
Questions about Entity Bind and Senzing
Is Senzing better than Entity Bind?
At the job it was built for, often yes. It is a mature engine, it runs where your data already is, it charges nothing per decision and it resolves people as well as companies. The question that separates the two is where the evidence comes from. An engine resolves the records you load; when the discriminating tokens are missing from every one of them, no engine recovers them. We go and read the register, cite what we read, and decline when it still will not decide.
Could we run both?
That is the shape we would suggest to most people who ask. Run the engine over the bulk of your records, where it is fast, cheap and in your estate, and send only the pairs it leaves unresolved or resolves weakly to us. The stateless pairwise endpoint exists for exactly that: two records in, a decision out, nothing stored — with the trade that a stateless comparison mints no receipt, so where you need the audit trail you want a resolve instead.
They publish a price. What does this cost?
The credit rule is published in five lines and served from the same constants that meter, so the number on the pricing page, the number in your usage block and the number you would be charged cannot drift apart. One credit for a decision reached deterministically or by scoring, five for one that needed adjudication or research, zero for an abstention at any stage, zero for a byte-identical replay, and zero for an input that normalised to no identifying signal. Paid subscriptions are purchased through Stripe in workspace Billing. Usage is recorded separately and does not create automatic usage or overage charges.
You both ship an MCP server. Does that not make you the same product?
No. MCP is a transport: it decides how an agent reaches a pipeline, not what the pipeline decides. Several products in this category expose agent routes now, and by itself that is distribution rather than positioning. What is worth comparing is what comes back through it — here, a decision with a calibrated confidence, cited register evidence, a documented refusal below the floor, and a receipt id an agent can hand to a human afterwards.
Settle it against Senzing on your own rows.
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