zkDatabase turns every row in a database into a self-verifying asset. Regulators, partners, and on-chain programs now trust the math, not the operator. With Zero-Knowledge Proof and Proof-Carrying Data, we allow the whole dataset to be verifiable in real-time at a minimal cost (< 500ms). The Pain Points of the Market Recently
One of the major challenges in today’s decentralized ecosystems is the inability of data consumers to verify the authenticity and integrity of the data they rely on. This vulnerability opens the door to fraud and manipulation directly at the data source — a critical flaw in many supposedly trustless systems.
Data consumers can not verify the authenticity/integrity of the data, which leads to fraud and manipulation at the data source.
Let’s take Hyperliquid as an example. A trader shorted 430 million JELLYJELLY tokens, causing a $140 million USDC outflow and significant HLP vault losses. Hyperliquid manipulated its Decentralized Oracle to force liquidation of traders’ positions. This incident highlighted oracle pricing and insurance mechanism weaknesses despite Hyperliquid’s audited smart contracts and HyperBFT consensus.
Data providers are always able to manipulate data sources without any restriction that reminded us, many Decentralized Protocols build on top on fraud and corruption.
Data verification is inefficient in a traditional Database Management System (DBMS)
The verification process in traditional database management systems (DBMS) incurs costs primarily due to the computational and resource-intensive tasks involved in ensuring data integrity, consistency, and security.
These tasks include validating data against predefined constraints, running complex queries for consistency checks, and performing authentication or access control to prevent unauthorized access. This process would cost several hours or day to verify.
Lack of security/guarantee in the interoperability
Cross-communication between different systems without Proof-Carrying Data (PCD) is risky because it lacks a mechanism to cryptographically verify the integrity and correctness of data exchanged across systems.
Without PCD, systems must trust incoming data, which can be manipulated or corrupted, leading to errors, security breaches, or unauthorized actions.
Why This Market Is Compelling
At Orochi, we see six concentric circles of spend converging on the same pain points — Provable Data Integrity:
Core cryptography tools: Zero-Knowledge Proofs (ZKPs), Proof-Carrying Data (PCD), Fully Homomorphic Encryption (FHE).
Data pipelines & databases: the budgets zkDatabase can replace or augment.
Regulated, trust-craving verticals: finance, critical IoT, identity, and RegTech, where proof requirements are exploding.
Data bridge for blockchains: Securing cross-chain interoperability with Proof-Carrying Data.
Replace the need for Oracle: zkDatabase can do better than what Oracle did, with a cryptographically verifiable outcome.
Unlimited off-chain storage: Allowing a chain system to access an nlimited amount of data securely.
Together, those circles already exceed $120B in annual spend and are compounding faster than the general IT sector.
We believe that the broad adoption of zkDatabase is inevitable
Initially, we see the core limitation of current Web2 databases. Traditional relational or NoSQL systems protect data with access controls, encryption at rest, replication, and audit logs. Those measures secure availability and confidentiality, but they do not produce an independently verifiable guarantee that:
Every record originated from an authenticated source.
Every update followed defined rules.
Every query result reflects the authorised state at the time of the request.
Auditors, partners, and regulators must therefore rely on operator-controlled logs, point-in-time exports, or SOC-style attestations, all of which assume good faith from the database administrator.
With zkDatabase, organizations move from “asserted integrity” to provable integrity. Compliance shifts from periodic document audits to continuous, machine-verifiable proofs, reducing reporting overhead while meeting emerging regulatory mandates in finance, healthcare, IoT, and digital identity.
Our market thesis is clear - Organizations will transition from mainstream Cloud databases to zkDatabase.
Regulatory mandates now require independently verifiable data trails
MiCA (EU Regulation 2024/1689) instructs crypto-asset service providers to maintain “verifiable” order-book records and solvency proofs that external parties can check. EU GMP Annex 1 (sterile drug manufacture) requires continuous, tamper-evident monitoring of environmental and temperature data. The draft EU AI Act places provenance obligations on any “high-risk” AI system before it can enter the Union market.
Conventional services (AWS Aurora, Azure Cosmos DB, Google Spanner, MongoDB Atlas, Oracle Autonomous DB) offer encryption and access logs but cannot hand regulators one cryptographic artifact that proves every ingest, update and query complied with policy. zkDatabase is built to supply exactly that artifact.
Zero-Knowledge Proofs and Proof-Carrying Data can be regulated as the same way regulators did with Digital Signature Algorithm and Has Function. It turns out you can generate proof for any kind of statement based on your data without any harm to your privacy or controls. The acceptance of ZKPs and PCD will be a boost for zkDatabase’s adoption.
Budget is already earmarked for solutions that prove integrity
Proof technology is mature and affordable
The market for Zero-Knowledge Proof as a software and technology is gaining mainstream analyst coverage and is expected to clear $8 billion in annual revenue before 2033, driven by lower proof costs and hardware acceleration. Falling unit costs remove the financial barrier to embedding proofs in every transaction. Also, a single, constant-size proof replaces full data exports and privileged log reviews, cutting external-audit effort from days to seconds. Business partners validate data themselves, eliminating lengthy negotiations over read-only replicas or bespoke APIs.
Because the proof reveals nothing about the underlying records, organisations satisfy audit demands without exposing personal or commercially sensitive data, a requirement that standard log dumps cannot meet.
These lines of spend already exceed $120 billion a year and are all compounding faster than general IT budgets. Hence, regulations now expect cryptographic evidence, budgets are shifting to pay for it, and incumbent Web2 databases cannot deliver it. zkDatabase is the first drop-in solution that covers the entire data life-cycle with one independently verifiable proof, making replacement both necessary and economically rational.
Hence, these build up on our R&D efforts.
They prove there is now a sizeable, budgeted requirement for verifiable data integrity and audit.
They expose a gap none of the incumbent platforms fill.
zkDatabase is designed specifically to cover the entire data lifecycle—ingest, transform, store and query—with one constant-size, independently verifiable proof.
That end-to-end guarantee is exactly what the converging budget lines now require, and it is not available from any existing Web2 database or partial-proof alternative.
There are visible quantified demand drivers, and we believe that a single-digit share of these overlapping pools yields a serviceable market >$4B for zkDatabase by the end of the decade.
Spend pool | 2025 baseline | 2030-range outlook | CAGR |
|---|
| Data-pipeline tooling | $12.26 billion (2025) | $43.61 billion (2032) | 19.9% |
| Cloud & NoSQL DBMS | ≈ $90 billion (2025) | $225 billion (2032) | ≈ 11% |
| Zero-knowledge proof software | $1.37 billion (2024) | $9.08 billion (2033) | 21.5% |
| RegTech & blockchain identity | $15.80 billion (2024) | $85.92 billion (2032) | 23.6% |
IDC and Deloitte show that database, data-integration, and compliance software will exceed $350 billion by 2030. Even after subtracting overlap, more than $130 billion is directly tied to data-integrity tasks that current DBMS and data-availability layers cannot fulfill.
Regulations now require mathematical proofs, and proof costs are falling 10x per hardware cycle. Capturing 1–2% of this mandatory spend yields $1.3–2.6 billion in annual revenue potential for zkDatabase, validating Orochi’s focus and venture-scale upside.
Data-pipeline tooling: Every change-data-capture (CDC) or ETL job that regulators now require to emit a mathematically verifiable proof is an incremental demand for zkDatabase’s Verifiable Data Pipeline. The 20%+ CAGR shows budgets are already expanding for modernization; adding proof output is an upsell, not an entirely new cost line.
DBMS spend. IDC confirms that more than 60% of new DBMS dollars are cloud-delivered. As banks and pharma labs re-platform, they must pick either
Traditional logs plus manual audits or Proof-native database: Even a 5% share of these regulated key-value deployments is a multibillion-dollar revenue ceiling for zkDatabase.
ZKP software. DataIntelo’s segmentation counts framework licences and proof-as-a-service APIs. zkDatabase is not a framework reseller; it is itself a high-volume proof generator. As proof volume rises, the database captures a headline portion of this fast-growing metered spend.
RegTech & blockchain identity. Fortune Business Insights shows compliance software jumping from sub $10 billion to mid $80 billion. Regulators are shifting from PDF audits to cryptographic evidence; zkDatabase supplies that evidence for both transactional books-and-records and privacy-preserving ID storage.
These four pools already exceed $120 billion in annual spend and are compounding faster than general IT budgets. Credible sources therefore support a material serviceable market for zkDatabase (well into the billions) even before considering long-tail adoption in unregulated sectors.
Where Proofs Are Already Mandatory
Capital-markets data: Post-FTX and MiCA, venues must prove order-book and solvency correctness.
Pharma & critical IoT: EU GMP Annex 1 now requires signed sensor logs.
AI & media authenticity: Deepfake risk is funding on-chain attestation pilots.
Digital identity: Portable KYC proofs need data-layer integrity without exposing PII.
These verticals already channel tens of billions in IT/security spend; even modest penetration feeds a nine-figure revenue line.
Capital on hand is not the main constraint
Celestia has raised $55 million for its modular data availability network and is sitting on $2.22 billion FDV on its token.
Avail has secured $43 million in Series A funding (after a $27 million seed round) to offer DA services and is sitting on $203 million FDV on its token.
EigenLayer and its EigenDA service have attracted well over $200 million across several rounds and is sitting on a $2.69 billion FDV on its token.
These sums are significant, but they target a different problem, guaranteeing that large blocks of rollup data remain retrievable. Money alone does not bridge the architectural gap outlined below, and hence we have spent years on R&D.
Our competitive edge lies in:
Proof-native demos (Pickles, Plonky3, ZK-STARK) are early-stage or single-stack.
Data-availability chains (Celestia, EigenDA) secure blobs, not mutable key-value workloads or structured data like collections/documents.
Cloud DBs with audit logs (AWS QLDB, MongoDB Enterprise) provide checksums, not end-to-end Proof-Carrying Data.
zkDatabase’s moat a regulator can verify (within milliseconds) that the entire pipeline (sampling → processing → storage → query) followed the agreed spec. Competitors validate only isolated steps.
To be more precise, we differentiate as core design goals diverge
Building a pipeline that certifies state correctness and *query accuracy (*not merely data presence) requires an entirely separate proof stack (circuits, recursion, Proof-carrying-data composition) that current DA networks do not maintain.
We’ve spent 5 years on R&D and believe that there are significant technical barriers to replication:
Recursive proof engineering: Orochi already supports multiple proof systems, namely Pickles, Halo2, and Plonky, 2 to keep proofs constant-size and verifiable in milliseconds. DA chains would need to design, audit, and optimise similar circuits for mutable databases; work that typically runs 18-24 months even with a specialised team.
zkDatabase chains proofs across ingestion, processing, storage, and query. No DA project today exposes APIs or runtimes that accept, compose, and re-emit proofs at each stage. Adding PCD means re-architecting their data path, not a feature toggle.
DA systems deliver blobs, they do not expose key-value look-ups, range queries, or JSON transformations. Supporting those operations with integrity proofs demands a new execution layer and client interface.
Celestia and Avail pay validators to sample and serve data. They have no staking economics that reward the heavy computation needed for zero-knowledge query proofs. Aligning incentives would require contract and token‐design changes.
Hence, we believe that funding alone cannot bridge the architectural, cryptographic, and economic gaps between a data availability layer and a verifiable, mutable database.
Celestia, Avail, and EigenDA excel at making data retrievable for roll-ups,
zkDatabase is purpose-built to prove that every piece of enterprise data is authentic and every query is correct. That difference makes direct replication unlikely in the near term.
And because zkDatabase’s proof chain spans the entire data lifecycle, it already meets new rules in finance (MiCA), life sciences (EU GMP Annex 1), and AI provenance, areas where DA layers are not competing. Replicating that capability is a multi-year effort that would force DA providers to leave their own road maps and re-enter a market Orochi is already addressing.
Here’s our take on how we expect Orochi to compete with web2 infra services for mainstream adoption:
We have first matched our business mechanics (revenue engine, cost drivers) and then cross-checked with technology stack motif (the design patterns that determine scalability, security, developer adoption, and compliance posture). Snowflake, MongoDB Atlas, Confluent, Datadog, and Elastic qualify on both criteria:
Because Orochi sits at the intersection of data infrastructure and compliance, the peer set captures the same buyer persona and architectural constraints. DA or execution-only crypto projects address different pain points (throughput, finality) and therefore do not inform enterprise ARR multiples.
Snowflake ↔ zkDatabase & zkDA Layer
Snowflake demonstrates that enterprises accept relinquishing physical data custody if the platform adds a Atlasnon-linear benefit (cost/performance).
Orochi adds a regulatory compliance benefit of similar magnitude, validating its architectural gamble.
MongoDB Atlas & Field-Level Encryption ↔ Proof-Carrying Data
Atlas adoption of FLE (>1,500 paying customers within 18 months, MongoDB Q4-2024 earnings call) shows that. Client-side augmentation can coexist with managed services. PCD leverages the same trust model but substitutes integrity for confidentiality, so our TAM assumptions mirror Atlas FLE attach rates.
Confluent Kafka Exactly-Once Semantics ↔ Orocle/Orand
Streaming buyers pay Confluent 8× EV/Revenue (public multiple) specifically for exactly-once compliance.
Orochi delivers stronger guarantees (cryptographic) at similar ingest throughput.
Therefore, a valuation premium over Confluent’s 8x baseline is technologically defensible.
Datadog Edge Pipelines ↔ ONProver
Datadog’s market cap (>$42B) is premised on agent ubiquity and low-latency enrichment.
ONProver duplicates that edge pattern for proofs, signaling potential for comparable adoption curves and expansion ACV.
Elastic shard architecture ↔ zkDA Layer
Elastic’s shift from search to observability leveraged its shard retrieval guarantees.
zkDA replicates those semantics but adds a cryptographic availability proof, lifting reliability from “best-effort” to “provably retrievable”, a qualitative upgrade investors can appreciate.
These five public-market systems each embody a critical architectural motif that Orochi employs
Because Orochi re-uses these proven motifs, investors can reliably translate technical traction metrics—ingest throughput, proof latency, shard recovery—to financial KPIs already priced into Snowflake, MongoDB, Confluent, Datadog, and Elastic multiples.
Hence, apart from competing with web3 DA layers, we view Orochi not as “another blockchain infra play” but as the intersection set of five public-company architectures; each a multi-billion-dollar franchise in its own right.
By exporting Snowflake-grade elasticity, MongoDB-style client augmentation, Kafka-class exactly-once ingestion, Datadog-light edge processing, and Elastic-level retrieval guarantees (then adding cryptographic proofs) Orochi's existing, investable tech motifs into a compliance-first product that existing SaaS benchmarks simply cannot address.
Given that we compete with web2 and web3 services complementarily, we defend our product services as a “one connected, end‑to‑end stack.”
We label our products separately (Proof‑Carrying Data (PCD) with zkDatabase, Orocle (oracle), Orand (verifiable randomness), zkDA Layer, and zkVM), yet each component fulfils a specific stage in the same data‑integrity pipeline.
All modules interlock to deliver verifiable data infrastructure, which is Orochi’s stated mission. Removing any layer breaks the chain:
Without Orocle/Orand, there is no trusted entry point for off‑chain facts.
Without PCD, proofs stop traveling with the data, and downstream systems must trust intermediaries.
Without zkDatabase, updates become unverifiable, forcing a re‑trust of traditional storage.
Without zkDA, data might be proven correct but turn unavailable, blocking audits.
Without zkVM, computations on the data would need separate verification mechanisms.
Hence, “doing a lot of things” is not diversification for its own sake; it is the required architecture to satisfy regulators and enterprises that need one certificate covering origin, storage, processing, and retrieval. Each Orochi product targets exactly one of those steps, and together they form a closed, verifiable loop.
Our modularity approach allow us to developed shared components that can be reused among different products. For instance, Verifiable Data Pipeline and zk-data-rollups can be shared between zkDA, zkDatabase, and Orocle. This approach allowed us to minimize R&D cost and increase the diversity of products to cover a wide range of markets.
Orochi’s portfolio is not a scatter of experiments; it is a deliberately layered architecture where every component amplifies the next, culminating in a single cryptographic certificate that regulators and smart contracts can trust. This integrated approach differentiates Orochi from DA‑only or DB‑only providers and positions the project as a comprehensive solution for the emerging verifiable‑data mandate.
Conclusion
Regulators have set immovable deadlines, budgets are migrating from assertion-based controls to cryptographic evidence, and incumbent databases cannot meet the new requirements. zkDatabase is the only store that:
Proves every operation (ingest, transform, store, query) with one constant-size certificate.
Drops into existing stacks without forcing developers to learn new query languages.
Delivers immediate ROI by shrinking external-audit effort from days to seconds while preserving privacy.
With a serviceable opportunity exceeding $4 billion by 2030 and no direct, full-pipeline competitor, zkDatabase is positioned to become the default data layer wherever provable integrity is mandatory.
Credit: Arhat & Orochi Team
Orochi Network, is a proof-agnostic, Verifiable Data Infrastructure that transforms raw data into verifiable data, built for Web3, AI, DePIN, and real-world asset tokenization. With over 300K daily users, 1.5M monthly users, and 160M+ transactions, it currently powers more than 40 dApps and blockchains. Backed by $12M in funding from the Ethereum Foundation and leading VCs, Orochi also supports a growing community of 500K+ members. Its zkDatabase has been adopted by 20+ blockchains, while Orand and Orocle extend verifiability across 49+ chains. By combining Proof-Carrying Data with ZK systems like Halo2, zk-STARK, and Plonky3, Orochi delivers audit-grade integrity and slashes Ethereum data costs from ~$25 to ~$0.002 per KB. 
