Multimodel Data Platforms

For most of the 2010s the orthodoxy in data architecture was polyglot persistence: use the right database for each job. Relational for transactions, document for flexible schemas, graph for relationships, key-value for caching, time-series for metrics, search for text. Each best-of-breed, each chosen on merit.

Forrester's framing of this category is the counter-argument, and its Q2 2026 evaluation states the cost precisely. Specialised engines produce operational fragmentation, because each one carries its own query language, security model, administrative tooling, and skill requirement.

Five databases means five upgrade cycles, five backup and recovery strategies, five sets of access controls to keep consistent, five failure modes, and five hiring problems. Plus the integration work of keeping data synchronised across them, and the questions nobody can answer because the relevant facts live in three engines that do not join.

Multimodel is the argument that one platform handling several models beats several platforms handling one each. Forrester's original definition described it as the intersection of multiple data models such as document, graph, and relational in a single platform, offering speed, scale, performance, integration, and security over the polyglot persistence model.

That argument has been contested for fifteen years, and 2026 gave it a fresh test.

The first evaluation

The Forrester Wave: Multimodel Data Platforms, Q3 2021 was the firm's first on this market, evaluating fifteen providers against twenty six criteria across current offering, strategy, and market presence: ArangoDB, Couchbase, DataStax, EDB, IBM, InterSystems, MariaDB, MarkLogic, Microsoft, MongoDB, Neo4j, Oracle, Redis, SAP, and SingleStore.

SAP placed as a Leader with maximum scores in seventeen of the twenty six criteria, credited with a mature in-memory platform supporting any multimodel workload and outstanding support for transactions, queries, analytics, and search across hybrid and multicloud environments.

InterSystems also placed as a Leader with maximum scores including deployment, performance, scalability, analytics and search, workloads, and use cases, positioned by Forrester for large companies wanting high-performance multimodel workloads with graph requirements. Its cited value was eliminating the need to integrate multiple technologies, producing less code, fewer system resources, and less maintenance.

That phrase is the multimodel case in its purest form. The value claimed is not capability but the absence of integration.

Inside The Forrester Wave: Multimodel Data Platforms, Q2 2026

The current evaluation covers fourteen suppliers, scored on current offering including multimodel breadth, security, observability, and data quality, and on strategy including vision and innovation.

Four Leaders: EDB, Oracle, Microsoft, and MongoDB.

Couchbase placed as a Strong Performer and was recognised as a customer favourite on reference feedback.

ArangoDB received the highest possible scores in adoption and in unified multimodel architecture.

That last criterion is worth isolating. Unified multimodel architecture distinguishes platforms where the models genuinely share one engine from those where separate engines have been assembled behind a common interface. Both can be described as multimodel and they behave very differently under load, in failure, and when a query spans models.

Forrester's own framing for this edition names the specialist engines that create the fragmentation problem: relational for transactions, document for flexibility, graph for relationships, vector for retrieval, and time-series for telemetry.

Four of those five have been on that list for a decade. The fifth is new, and it is why this category was revisited.

Vector was the fresh test

Retrieval-augmented generation created a requirement almost every enterprise discovered simultaneously in 2023: storing embeddings and searching them by similarity.

The immediate response was a wave of specialist vector databases, and the polyglot argument applied straightforwardly. Vector search is a distinct workload with distinct indexing structures, so it deserves a purpose-built engine.

The counter-argument arrived just as quickly, and it is the same one this category has always made. An organisation adding a vector database adds a sixth engine, with its own query language, security model, operational tooling, and a synchronisation problem: the embeddings derive from source content that lives elsewhere, and keeping them consistent as that content changes is continuous work.

Worse, the failure is silent. A vector index describing documents that have since changed returns confidently wrong retrievals, and nothing errors.

The established platforms responded by adding vector capability to engines that already held the source data, which removes the synchronisation problem entirely because there is nothing to synchronise.

The Leader list in the current evaluation is consistent with that resolution. EDB, Oracle, Microsoft, and MongoDB are all platforms that added vector to existing multimodel engines rather than vector specialists that added everything else.

Whether their vector performance matches a purpose-built engine at extreme scale is a fair question with a real answer, and for the large majority of enterprise retrieval workloads it is not the binding constraint. Data freshness and permission enforcement usually are, and both favour keeping vectors next to the source.

Postgres became the default answer

EDB's Leader placement points at a structural development worth naming: PostgreSQL has become the most common route to multimodel without anyone deciding it should be.

The mechanism is extensibility. Postgres accepts extensions that add capability to the core engine, and the ecosystem has produced document handling through native JSON types, geospatial support, time-series capability, and vector search, alongside its relational foundation.

That gives an organisation multimodel coverage using an engine its people already know, with one security model, one backup strategy, one set of operational tooling, and no licence negotiation.

It also produces a different risk profile. Extensions are maintained by varying parties with varying commercial backing, and the performance of an extension at scale is not the same as a purpose-built engine's. A Postgres deployment covering five models is a coherent architecture and it is also a set of bets on components with different maturity.

Commercial Postgres providers exist to close that gap, offering supported distributions, tested extension combinations, and the operational tooling the open source project does not ship. That is the business EDB is in, and its Leader placement reflects a market where the underlying engine is free and the confidence to run it at scale is not.

The honest counter-argument

Multimodel is not free, and the case against it deserves stating properly.

A general engine is rarely as fast as a specialist at the specialist's job. A dedicated graph database traversing millions of relationships, a purpose-built time-series engine ingesting high-frequency telemetry, or a specialist vector index searching billions of embeddings will generally outperform a general platform doing the same thing.

For most workloads that gap does not matter, because most workloads are nowhere near the limits where it shows. For some it matters enormously, and those organisations correctly run the specialist.

There is a second cost that is less discussed. Consolidating onto one platform concentrates risk. A single engine holding transactional, analytical, document, graph, and vector workloads is a single point of failure for all of them, and its upgrade cycle becomes an event affecting everything rather than one system.

And there is a lock-in dimension. Polyglot architectures are unpleasant to operate and relatively easy to change one component of. A deeply integrated multimodel platform, particularly one using proprietary extensions across several models, is difficult to leave in a way that a collection of standard engines is not.

The reasonable position is that multimodel is right for the large middle of enterprise workloads and wrong at the extremes, and that the extremes are rarer than engineering teams assume when they are choosing.

Where this connects

This category sits next to Forrester's translytical data platforms coverage, which addresses combining transactional and analytical processing in one system, and several vendors appear in both. The arguments are cousins: one is about combining data models, the other about combining workload types, and both are consolidation cases against a fragmented default.

It also sits underneath the lakehouse and AI platform categories. A lakehouse consolidates analytical storage; a multimodel platform consolidates operational data across shapes. An organisation can have both and they solve different halves of the fragmentation problem.

The connection that matters most right now is with retrieval. If agents are going to act on enterprise data, the data they act on has to be current, permission-aware, and consistent with its source. Every additional engine between the source and the retrieval is another place where currency and permissions can diverge.

That is a stronger argument for consolidation than any of the operational efficiency reasoning that has carried this category for fifteen years, and it arrived from a direction nobody in the original polyglot debate anticipated.

Forrester does not endorse any vendor named here, and tier placement should not be read as a recommendation to buy.