The proof.
We used the math behind AI
to build a database.
The matrix operations behind every large language model — the mathematical engine driving the AI revolution — turn out to be a radically better way to store and query data. Not to generate it. Not to predict it. To retrieve exactly what’s there, with perfect accuracy, faster and cheaper than anything else on the market.
The Problem
The cost of data infrastructure
is becoming unsustainable.
Behind every enterprise data budget is a deeper crisis — power, compute, and complexity compounding faster than the value they deliver.
spend per year1
by data centers by 20302
maintain data pipelines1
that fail expectations1
Enterprise data spending has reached $29 million per year on average, with cloud compute and ingestion alone running over $500K per month at many organizations. Engineering teams spend $2.2 million a year just maintaining data pipelines. And despite all of that, 73% of enterprise data initiatives fail to meet expectations.1
Behind those budgets is a deeper problem: power. Data centers are on track to consume 9% of all US electricity by 2030 — triple what they use today.2 Power demand is growing at 15% per year.3 The industry’s own new benchmark for infrastructure value is “tokens per watt per dollar”4 — a metric that Jensen Huang formally introduced at GTC 2026.5
Every watt spent on database overhead is a watt not available for the workloads that actually generate value.
Traditional databases are a major part of this waste. They store your data and then build an equally large shadow of infrastructure around it — indexes, logs, caches, replication layers — until the system is 5 to 10 times larger than the data itself. All of that overhead consumes compute, memory, storage, and electricity. It exists because the old approach to databases requires it.
TETRA doesn’t.
The Solution
One engine. A fraction of the footprint.
TETRA files compress to ~38% of the initial raw data size — and that includes everything needed to search and query it (what other databases call “indexes”). There is no separate index layer. The compressed, post-quantum encrypted file is the query engine. By formal proof, zero data resolution is lost.
Where a traditional system wraps your data in layers of infrastructure — indexes, logs, caches, replication layers — TETRA’s structure is the query engine. Nothing is wasted.
That efficiency cascades into every cost line:
Smaller servers. Lower cloud bills. Lower electricity. A multi-server cluster workload runs on a single standard machine.
Replaces separate systems for structured records, relationships, search, and analytics. Fewer systems, fewer teams, fewer integration points.
No indexes, logs, caches, or replication layers to maintain. The savings compound from the storage layer through the power grid.
Datacenter, edge, or a device in the field. Intelligence moves to where the data lives, without a round trip to a central server.
A workload that required a multi-server cluster — with all the licensing, staffing, cooling, and power that implies — runs on a single standard machine.
Everyone else used this math to build language models. We used it to build a database.
AI + Data
It makes AI accurate
instead of approximate.
The biggest unsolved problem in enterprise AI is accuracy. Models hallucinate. The proven fix is giving them structured knowledge to reason over — not documents to guess from.
better accuracy on complex business questions when LLMs reason over structured knowledge graphs vs. vector-only retrieval.6
The bottleneck has been the database underneath. Existing options are too slow to keep up with a model during a live query, and too heavy to deploy alongside one.
TETRA is fast enough and small enough to sit inside the AI stack itself — no separate infrastructure, no additional power draw, no added complexity. This turns AI from a tool that sometimes gets it right into a system that reasons from your actual data, every time.
And because the engine is so compact, it runs wherever the AI runs — in a datacenter, at the edge, on a device in the field. Intelligence moves to where the data lives, without a round trip to a central server and without spinning up another rack.
Timing
Why now.
The data infrastructure industry is hitting a wall. Power constraints are dictating where data centers can be built, what workloads they can run, and how much they can grow.7 Every efficiency gain at the database layer translates directly into capacity freed up for everything else.
projected by 20307
data center power3
use by 20302
TETRA doesn’t just reduce cost. It reduces the physical resources required to do the same work — at a moment when those resources are the binding constraint on the entire technology industry.
The result changes the economics of enterprise data infrastructure at exactly the moment those economics are breaking.
Sources
See TETRA in action.
Explore the technical benchmarks or talk to us about what TETRA can do for your infrastructure.
A graph data system
that runs anywhere.
We didn’t improve the graph database. We reinvented it. Sub-millisecond queries. No specialized hardware. Tiny RAM footprint.
Benchmark Report
LDBC Social Network Benchmark
Interactive Complex Reads · IC1–IC14
SF10 · 30M nodes · 115M edges · single file · single process
What this proves
TETRA executes all 14 of the LDBC Interactive Complex reads — the hardest standardized benchmark for graph databases — on a single laptop, from a single file. These are not micro-benchmarks. Each query is a real analytical question that traverses variable-length friendship paths, scans millions of messages, runs correlated subqueries, and finds shortest paths across 27 million nodes and 172 million edges. Average latency: 405 ms. Six queries finish under 100 ms. Two finish under 1 ms.
Latency distribution · 14 Interactive Complex queries
All 14 Interactive Complex queries
Every query returns correct, non-zero results. Parameters sampled live from the graph (top persons by KNOWS degree — maximum fan-out at every hop).
| ID | Query | Latency |
|---|---|---|
| IC1 | Transitive friends by name | 74ms |
| IC2 | Recent friend messages | 47ms |
| IC3 | Friends in countries | 1.12s |
| IC4 | New tags in friend posts | 883ms |
| IC5 | New forum memberships | 1.49s |
| IC6 | Tag co-occurrence | 514ms |
| IC7 | Recent likers | 14ms |
| IC8 | Recent replies | 89ms |
| IC9 | Recent FoF messages | 357ms |
| IC10 | FoF birthday + interests | 237ms |
| IC11 | Friends work in country | 247ms |
| IC12 | Expert replies | 602ms |
| IC13 | Shortest path | 0.5ms |
| IC14 | All shortest paths | 0.5ms |
Query work — what each query actually does
Click any query to see the traversal shape and the factual work involved. Edge counts are estimated from the LDBC SF10 degree distributions for high-degree seed persons.
graph LR P((Person)):::p -->|"KNOWS*1..3"| F((friend)):::p F -->|IS_LOCATED_IN| C[City]:::loc F -.->|STUDY_AT| U[Uni]:::org F -.->|WORK_AT| W[Comp]:::org classDef p fill:#4e79a7,stroke:none,color:#F5F0E6 classDef loc fill:#59a14f,stroke:none,color:#F5F0E6 classDef org fill:#f28e2b,stroke:none,color:#F5F0E6
Explore all persons within 3 friendship hops who share a given first name. Visits ~80,000 persons across the 3-hop KNOWS frontier, reads the firstName property on each, deduplicates. After filtering and ranking, enriches the top 20 with their city of residence, universities attended (with enrollment year), and employers (with start year).
graph LR P((Person)):::p -->|KNOWS| F((friend)):::p M["Post | Comment"]:::m -->|HAS_CREATOR| F classDef p fill:#4e79a7,stroke:none,color:#F5F0E6 classDef m fill:#76b7b2,stroke:none,color:#F5F0E6
Retrieve the 20 most recent messages from direct friends. Finds ~200 friends, examines ~77,000 messages (Posts and Comments) authored by those friends, applies a date ceiling, and selects the top 20 newest by creation date using a heap.
graph LR P((Person)):::p -->|"KNOWS*1..2"| F((friend)):::p F -->|located| Co[Country]:::loc M["message"]:::m -->|HAS_CREATOR| F M -->|located| Co2["Country X/Y"]:::loc classDef p fill:#4e79a7,stroke:none,color:#F5F0E6 classDef loc fill:#59a14f,stroke:none,color:#F5F0E6 classDef m fill:#76b7b2,stroke:none,color:#F5F0E6
Count messages by 2-hop friends in two specific countries, excluding friends who live in either country. Explores ~5,500 persons across 2 hops, resolves each person’s location through a City→Country chain, applies the exclusion filter, then scans ~1.9M messages and joins each to its country of origin. Splits counts into two country-specific buckets using conditional aggregation.
graph LR P((Person)):::p -->|KNOWS| F((friend)):::p Po[Post]:::m -->|HAS_CREATOR| F Po -->|HAS_TAG| T[Tag]:::org X["NOT EXISTS: same tag before date"]:::res classDef p fill:#4e79a7,stroke:none,color:#F5F0E6 classDef m fill:#76b7b2,stroke:none,color:#F5F0E6 classDef org fill:#f28e2b,stroke:none,color:#F5F0E6 classDef res fill:#e15759,stroke:none,color:#F5F0E6
Find tags that appear on recent friend posts but never on older friend posts — newly trending topics. Scans ~200 friends’ posts within a date window, collects their tags (~36,000 post-tag pairs), then for each candidate tag verifies it has no occurrence before the start date. This is a correlated anti-join: the engine must cross-reference each tag against the entire pre-window post history of the same friend set.
graph LR P((Person)):::p -->|"KNOWS*1..2"| F((friend)):::p Fo[Forum]:::org -->|HAS_MEMBER| F Fo -->|CONTAINER_OF| Po[Post]:::m classDef p fill:#4e79a7,stroke:none,color:#F5F0E6 classDef m fill:#76b7b2,stroke:none,color:#F5F0E6 classDef org fill:#f28e2b,stroke:none,color:#F5F0E6
Rank forums by how many 2-hop friends joined recently, then count each forum’s total posts. Deduplicates ~5,500 persons from the 2-hop frontier, scans ~55,000 forum memberships with a date filter on the membership edge, groups by forum, sorts by friend count. For the top 20 forums, counts all contained posts. Four sequential pipeline stages, each requiring full materialization before the next begins.
graph LR P((Person)):::p -->|"KNOWS*1..2"| F((friend)):::p Po[Post]:::m -->|HAS_CREATOR| F Po -->|HAS_TAG| T1["known Tag"]:::org Po -->|HAS_TAG| T2["co-occurring"]:::res classDef p fill:#4e79a7,stroke:none,color:#F5F0E6 classDef m fill:#76b7b2,stroke:none,color:#F5F0E6 classDef org fill:#f28e2b,stroke:none,color:#F5F0E6 classDef res fill:#b07aa1,stroke:none,color:#F5F0E6
Find tags that co-occur with a named tag on posts by 2-hop friends. Scans ~660,000 posts from ~5,500 friends and checks ~2.6M tag edges to find the ~16,500 posts carrying the target tag. Then reads ~66,000 co-occurring tags on those posts. Groups by tag name with distinct post count.
graph LR P((Person)):::p -->|created| M["messages"]:::m M -->|LIKES| L((likers)):::res classDef p fill:#4e79a7,stroke:none,color:#F5F0E6 classDef m fill:#76b7b2,stroke:none,color:#F5F0E6 classDef res fill:#b07aa1,stroke:none,color:#F5F0E6
Find who most recently liked the seed person’s content. Scans ~385 messages, follows ~3,850 LIKES edges, sorts by like timestamp, groups by liker keeping only the most recent like per person. Checks whether each liker is already a friend (boolean NOT EXISTS). Computes the time gap between the like and the original message creation.
graph LR P((Person)):::p -->|created| M["messages"]:::m C[Comment]:::m2 -->|REPLY_OF| M C -->|HAS_CREATOR| A((author)):::res classDef p fill:#4e79a7,stroke:none,color:#F5F0E6 classDef m fill:#76b7b2,stroke:none,color:#F5F0E6 classDef m2 fill:#76b7b2,stroke:none,color:#F5F0E6 classDef res fill:#b07aa1,stroke:none,color:#F5F0E6
Find the most recent replies to the seed person’s content. Scans ~385 messages, follows ~3,080 REPLY_OF edges to find comments, resolves each comment’s author. Returns the 20 newest replies sorted by creation date. A straight 4-hop chain with no aggregation or VLP.
graph LR P((Person)):::p -->|"KNOWS*1..2"| F((friend)):::p M["Post | Comment"]:::m -->|HAS_CREATOR| F classDef p fill:#4e79a7,stroke:none,color:#F5F0E6 classDef m fill:#76b7b2,stroke:none,color:#F5F0E6
Retrieve the 20 most recent messages from 2-hop friends. Deduplicates ~5,500 persons from the 2-hop frontier, then scans all of their authored content — approximately 2.1 million messages. Applies a date filter and multi-label check (Post or Comment), then selects the top 20 by creation date via a 20-element heap over the full 2.1M candidates.
graph LR P((Person)):::p -->|"KNOWS*2"| FoF((fof)):::p FoF -->|IS_LOCATED_IN| C[City]:::loc Po[Post]:::m -->|HAS_CREATOR| FoF Po -->|HAS_TAG| T[Tag]:::org T -->|HAS_INTEREST| P classDef p fill:#4e79a7,stroke:none,color:#F5F0E6 classDef loc fill:#59a14f,stroke:none,color:#F5F0E6 classDef m fill:#76b7b2,stroke:none,color:#F5F0E6 classDef org fill:#f28e2b,stroke:none,color:#F5F0E6
Score friend-of-friends by shared interests. Takes the exactly-2-hop KNOWS frontier (~8,000 persons), excludes direct friends and the seed, filters by birthday month (~833 candidates remaining). For each candidate, runs two passes: one counting posts whose tags match the seed’s declared interests, one counting posts that don’t match. Computes a similarity score as the difference.
graph LR P((Person)):::p -->|"KNOWS*1..2"| F((friend)):::p F -->|WORK_AT| O[Org]:::org O -->|IS_LOCATED_IN| Co[Country]:::loc classDef p fill:#4e79a7,stroke:none,color:#F5F0E6 classDef org fill:#f28e2b,stroke:none,color:#F5F0E6 classDef loc fill:#59a14f,stroke:none,color:#F5F0E6
Find 2-hop friends who work at companies in a specific country before a given year. Explores ~5,500 persons, follows ~8,250 WORK_AT edges (most people have 1–2 jobs), resolves each company’s country, filters by country name and employment start year. Sorts by work-from year with tiebreakers on person ID and company name (mixed ASC/DESC).
graph LR P((Person)):::p -->|KNOWS| F((friend)):::p C[Comment]:::m -->|HAS_CREATOR| F C -->|REPLY_OF| Po[Post]:::m Po -->|HAS_TAG| T[Tag]:::org T -->|HAS_TYPE| TC[TagClass]:::org TC -->|"SUBCLASS*0.."| B["base class"]:::res classDef p fill:#4e79a7,stroke:none,color:#F5F0E6 classDef m fill:#76b7b2,stroke:none,color:#F5F0E6 classDef org fill:#f28e2b,stroke:none,color:#F5F0E6 classDef res fill:#b07aa1,stroke:none,color:#F5F0E6
Find friends whose comments reply to posts tagged under a given tag class hierarchy — the deepest chain at 7+ hops. For ~200 friends, examines ~53,000 comments. Each comment is chased through a mandatory 7-hop path: Comment→REPLY_OF→Post→HAS_TAG→Tag→HAS_TYPE→TagClass, then up the IS_SUBCLASS_OF tree (variable depth, min 0) to test against a named base class. Every comment must complete the full chain before the engine knows if it qualifies.
graph LR P1((Person 1)):::p ---|"KNOWS* — BFS"| P2((Person 2)):::res classDef p fill:#4e79a7,stroke:none,color:#F5F0E6 classDef res fill:#b07aa1,stroke:none,color:#F5F0E6
Find the shortest friendship path between two people. Launches a bidirectional breadth-first search from both endpoints through the KNOWS graph (68,673 persons). Social network path lengths average ~4 hops, so each direction expands only ~2 frontiers before they meet. No property reads, no filters — pure adjacency-list iteration on the compact KNOWS subgraph.
graph LR P1((Person 1)):::p ---|"KNOWS* — all paths"| P2((Person 2)):::res classDef p fill:#4e79a7,stroke:none,color:#F5F0E6 classDef res fill:#b07aa1,stroke:none,color:#F5F0E6
Enumerate all shortest friendship paths between two people. Same BFS as IC13, but tracks every equal-length route by recording multiple parent pointers when a node is reached by different shortest paths simultaneously. After BFS completes, reconstructs all 15 paths by backtracking through the parent DAG. Returns each path as a list of person IDs. Same ~16K edge reads as IC13; the multi-parent bookkeeping and path enumeration (15 paths × ~4 nodes) add negligible cost.
Dataset: LDBC SNB SF10
The LDBC Social Network Benchmark is the industry-standard benchmark for graph databases, maintained by an independent consortium. SF10 represents a social network at 10× base scale with realistic power-law degree distributions, temporal properties, and correlated data generation.
| Entity | Count |
|---|---|
Persons | 68,673 |
Posts | 8,273,491 |
Comments | 18,196,074 |
Forums | 667,545 |
Tags | 16,080 |
Total nodes | 27,231,349 |
Total edges | 172,183,299 |
Edge types | 16 |
Node labels | 13 |
Environment
engine: Tetra / EdgeGlider · native arm64 binary · single process · in-process (no network hop)
storage: Single file · mmap’d · LDBC SNB SF10
hardware: Apple M-series · single machine · CPU only — no GPU, no cluster
protocol: Bolt v4.4 (Neo4j wire-compatible) · openCypher
parameters: Sampled live from graph · top persons by KNOWS degree
date: 2026-04-14
Tetra / EdgeGlider · LDBC SNB SF10 · 14/14 Interactive Complex queries passed
For context
TigerGraph’s audited LDBC SNB BI result at SF1000 was run on a Dell PowerEdge R7725 bare-metal server (AMD EPYC). GraphScope Flex holds throughput records (130K+ ops/s at SF100/SF300) on full server infrastructure. Neo4j failed to complete several IC queries at SF10 in the 2019 Rusu & Huang study. Amazon Neptune has no published LDBC results and its openCypher implementation does not support shortestPath() or allShortestPaths(). TETRA passes 14/14 on a single process from a single file.
What’s included
Everything. No add-on licensing.
Visualization, algorithms, encryption, compliance. It’s all in the box. Other vendors charge separately for each of these.
Retina — 3D/2D Graph Data Viewer & Explorer
Force-directed, spherical, and radial — switch on the fly
2D overhead or full 3D orbit
Six distinct platonic solids. Each node type gets its own shape and color automatically.
Click to focus — see N hops deep
Fly to any node in the graph
Pin and inspect multiple nodes at once
WebGPU-driven rendering (2D and 3D modes). Server-side and client-side analytics built in (shortest path, centrality, community detection). Query UI for exploring and verifying data or validating migrations. Export results as CSV or JSON.
Free and ungated — part of the TETRA suite, no add-on licensing. Neo4j charges $1,200–$2,500/user/year for Bloom (self-hosted Enterprise). TigerGraph charges 10% compute surcharge for Insights. Neptune has only the open-source graph-explorer.
Built in. No add-on licensing. Neo4j charges $10K–$25K+/yr for Graph Data Science. TigerGraph charges 10% compute surcharge for Insights.
Encrypted at rest with post-quantum resistant cryptography. The encrypted, compressed file is the queryable database — no decryption step.
Raw data compresses to ~38% including all query structures. No separate indexes. By formal proof, zero data resolution lost.
1,611/1,611 openCypher scenarios passed. Full compliance over Bolt v4.4. Neptune can’t run shortestPath(). TigerGraph uses proprietary GSQL.
66 MB RAM for 166K edges. Native binary in a 24 MB Alpine container. Runs alongside your app — no cluster, no network hop.
10 GB of CSV ingested, converted, and loaded in under 7 minutes on 5 GB of RAM. Single-process. No ETL cluster.
Performance
78 queries. 300 iterations each. Same hardware.
Recommendations dataset — 28,863 nodes, 166,261 edges. Both containerized on Apple M4 Pro, 16GB, CPU only. Native binary shown for reference.
Container Benchmark
Head-to-head. Same hardware. Same queries. No tricks.
105 Cypher queries, 30 iterations each. Both databases containerized with hard resource limits. Neo4j gets 2× the RAM budget and still uses 10.8× more memory than Tetra.
105 queries · Dataset: Recommendations — 28,863 nodes, 166,261 edges · p99 determines winner · 5% tie threshold
Neo4j’s wins are concentrated in multi-variable RETURN projections and OPTIONAL MATCH chains — areas where Tetra’s query planner has known optimization opportunities.
These represent active query planner optimization targets for TETRA. We expect these gaps to close as the planner matures. We ship what’s real, including what’s not done yet.
Write Operations
11 write tests. Tetra passes all. Neo4j: OOM.
CREATE, SET, REMOVE, DELETE, DETACH DELETE, MERGE (ON CREATE, ON MATCH). Neo4j could not complete the write verification suite under 1 GB with 1 CPU. Every write attempt resulted in an OOM crash.
Throughput
Concurrent scaling. Same hardware. Fair fight.
Mixed workload — queries per second as concurrent Bolt clients increase. Tetra: 1 CPU / 512 MB. Neo4j: 1 CPU / 1 GB. At 64 clients, Neo4j runs out of memory and crashes. Tetra keeps serving.
Queries / second by concurrency level
Higher is betterNeo4j’s throughput drops as concurrency increases — from 90 q/s at 1 client to 29 q/s at 8 clients. The JVM garbage collector fights the 1 GB memory limit. Tetra’s throughput increases with concurrency and stays flat. No GC, no heap pressure.
Tetra: 1 CPU / 512 MB · Neo4j: 1 CPU / 1 GB · Bolt v4.4 · Mixed read workload · Recommendations dataset
Economics
What it actually costs.
Every graph database prices differently. We did the math so you don’t have to. All prices verified from vendor sites, April 2026.
| Provider | Config | Monthly | What’s missing |
|---|---|---|---|
| TETRA | Flat rate | $299 | Nothing. Retina, 30+ algorithms, full Cypher included. |
| Neptune | db.r5.large, single | $297 | No HA/failover. +replicas (2–4× cost), +storage, +I/O. No shortestPath(). No built-in visualization. |
| Neo4j AuraDB Pro | 16 GB / 3 CPU | $1,051 | Bloom included in cloud. Self-hosted GDS: $10K–$25K+/yr extra. |
| Neo4j AuraDB BC | 8 GB / 2 CPU | $1,168 | SLAs, RBAC, SSO. Self-hosted Enterprise: $20K–$200K+/yr. |
| TigerGraph Savanna | TG-00 (2 vCPU, 16 GB) | $720 | Compute only. +HA (2.8× → ~$2,016/mo), +storage, +Insights (10%). Proprietary GSQL. |
Cumulative annual TCO
| Neo4j Pro | Neo4j BC | TigerGraph | Neptune | TETRA + migration | TETRA only | |
|---|---|---|---|---|---|---|
| Year 1 | $12,614 | $14,016 | $8,640 | $3,559 | $14,452 | $3,588 |
| Year 3 | $37,843 | $42,048 | $25,920 | $10,678 | $21,328 | $10,764 |
| Year 5 | $63,072 | $70,080 | $43,200 | $17,796 | $28,504 | $17,940 |
Important caveats
Neptune $297/mo is single instance, no HA. Production requires writer + replicas (2–4× instance cost).
TigerGraph $720/mo is compute only. With HA (2.8×) it’s ~$2,016/mo before storage or add-ons.
Neo4j self-hosted Bloom ($1,200–$2,500/user/yr) and GDS ($10K–$25K+/yr) pricing is from Vendr third-party transaction data, not Neo4j-published.
Neo4j Pro 16GB: $1,051.20 × 12 = $12,614/yr
Neo4j BC 8GB: $1,168.00 × 12 = $14,016/yr
TigerGraph TG-00: $720 × 12 = $8,640/yr (compute only)
Neptune db.r5.large: $296.61 × 12 = $3,559/yr (single instance)
TETRA w/ migration: $10,864 + ($299 × 12) = $14,452 yr 1; $3,588/yr after
TETRA product: $299 × 12 = $3,588/yr
Pricing sources: neo4j.com/pricing · TigerGraph Savanna · AWS Neptune · Vendr (self-hosted estimates)
All prices verified April 2026. Smallest production-viable configuration for each provider.
Methodology
hardware: Apple M-series silicon · single machine · CPU only
containers: Podman 5.x · resource limits via cpus + mem_limit
tetra: 1 CPU / 512 MB RAM · Alpine 3.21 · single Go binary · no JVM
neo4j: 1 CPU / 1 GB RAM · neo4j:5-community · JVM heap 256–512 MB · page cache 128 MB
dataset: Recommendations — 28,863 nodes, 166,261 edges · same JSONL source
queries: 105 Cypher queries across 12 categories · 30 iterations each
metric: p50 median latency (reported) · p99 determines winner · 5% tie threshold
driver: neo4j-go-driver/v5 · same Bolt client for all targets
throughput: 4 representative queries · 5-second wall clock per level · 1–64 clients
writes: 11 operations: CREATE, SET, REMOVE, DELETE, DETACH DELETE, MERGE
cypher_tck: 1,611 / 1,611 scenarios (100%)
What we’re NOT doing
No cherry-picking queries. All 105 run on all engines. Neo4j wins are reported alongside Tetra wins.
No warm-up discarding. First-query latency counts.
No query hints or engine-specific tuning. Identical Cypher strings over the same Bolt protocol.
No pre-warming caches. Both containers start fresh, load data, and run the benchmark.
See it for yourself.
Explore the recommendations dataset in 3D — 2,330 nodes, 3,506 edges, running in your browser.
WebGPU-powered Retina viewer. Click a node, follow the connections, see what’s actually there. No install. No account. Free.
Launch Demo →Introductory Pricing
Includes Retina visualization, 30+ graph algorithms, full openCypher, and operational support. The one-time fee is optional — for existing data migrations only. Not a license fee.
Get TETRA →Questions? Schedule a call or reach out.