Is Gemini Enterprise cheaper than self-hosting Onyx for your organization?
Self-hosting does not make an AI assistant cheaper by default. It moves recurring per-seat spend into infrastructure, model serving, implementation and operations, and the outcome depends on your user count, the volume of documents you index and whether you need GPU-hosted models or pay-per-call API models. If your renewal is driven by seat counts, start by modeling those variables for your own organization rather than comparing list figures. This page does not state Gemini Enterprise prices because none were supplied. Take seat fees, query and credit allowances, and storage overage terms from your Google Cloud agreement and confirm with your account team whether any of them change at renewal.
On the self-hosted side, Onyx Community Edition is MIT licensed, so the software itself carries no license fee. Costs come from the components you run. Onyx can use self-hosted models through Ollama, LiteLLM or vLLM, or proprietary APIs from providers such as Anthropic, OpenAI and Gemini, and those choices change the cost profile. vLLM is Apache-2.0 licensed and has no paid edition, so its cost is GPU or other accelerator capacity, the surrounding infrastructure, implementation work and the people who operate it. API-model usage is billed by the provider per call, which recurs with usage. Ask your team to estimate peak concurrent users, index size and token volume, then price GPU capacity or API consumption against those numbers.
Plan for the costs that do not appear on an infrastructure invoice. Your staff time for upgrades, connector maintenance, backups, monitoring and access reviews is recurring, and an implementation partner's fixed-scope work is one-time. Ask any quote to separate those lines: one-time setup, recurring infrastructure, model consumption, support and internal operator time. If you are considering a retainer for upgrades and ongoing testing, ask for its scope in writing so it can be compared with the in-house effort it replaces.
Which cost components sit on each side, and which are one-time or recurring
This table lists each cost component of a Gemini Enterprise subscription and a self-hosted Onyx deployment, showing who is paid, whether the cost is one-time or recurring, and what drives it. Read it as a checklist for a quote request, since the largest differences usually sit in model serving, staff time and connector setup rather than in license lines.
| Cost component | Who is paid | One-time or recurring | What drives it |
|---|---|---|---|
| Seats or subscription | Google Cloud for Gemini Enterprise; none for the Onyx Community Edition | Recurring for Gemini Enterprise; none for Onyx Community Edition | Confirm the per-user price and terms in your Google Cloud agreement, and count the users who need access |
| Query and credit allowances | Google Cloud, if your plan meters usage | Recurring | Confirm in writing how queries, agent runs and credits are counted and what happens when allowances run out |
| Storage overage | Google Cloud, if indexed content exceeds plan limits | Recurring | Size of indexed content across connected sources; ask how overage is billed |
| Onyx Enterprise Edition license | Chainscore Labs' Onyx partner or Onyx directly, depending on your contract | Recurring, separate from the open-source install | Needed only for code under the Onyx Enterprise License, such as premium features like RBAC; confirm the terms with the vendor |
| Connector setup | Implementation team or your staff | One-time, with ongoing maintenance | Number of sources, permission mapping complexity and how many connectors need custom configuration |
| Infrastructure (compute, storage, database, search index) | Your cloud provider or data center vendor | Recurring | Index size, Postgres, Vespa or OpenSearch footprint, Redis and object storage, plus user concurrency |
| Model serving (GPU or API tokens) | GPU hardware or cloud provider for self-hosted models; model API provider for hosted models | GPU capacity recurring, or one-time hardware purchase; API tokens recurring | Model size, query volume and whether vLLM runs on your GPUs or a proprietary API is called; vLLM itself has no paid edition |
| Implementation and migration | Implementation partner or your staff | One-time | Scope of sources to reconnect, agent rebuilds and period in which both systems run in parallel |
| Support and upgrades | Google Cloud for the managed service; your staff or a retainer provider for Onyx | Recurring | Release cadence of Onyx and vLLM, Helm chart changes, patching and whether you buy a support contract |
| Internal skills and staff time | Your own employees | Recurring | Team capacity to operate Kubernetes, GPUs, monitoring and access control; ask for a named owner and on-call expectations |
What moves the cost comparison most
Gemini Enterprise is priced per seat and by allowances, while a self-hosted Onyx deployment shifts cost to infrastructure, model consumption and people. The drivers below decide which side of that trade-off is cheaper for your organization, and each one should be quantified in your own environment before you compare quotes.
Active user count
Gemini Enterprise is charged per seat, so cost scales with the number of licensed users. Onyx has no per-seat license fee in its open-source edition, so per-user cost mainly reflects query volume, which drives model and compute spend. Growth in active users pushes Gemini Enterprise cost up linearly, while a self-hosted stack grows in steps as capacity is added. Ask Google for the seat price in writing and count only users who will really query the assistant.
Indexed data and connected sources
Every connected source and every indexed gigabyte adds storage, embedding work and sync load. Onyx connects to 50+ applications, and its documented index uses Postgres, Vespa or OpenSearch, Redis and model servers, so larger indexes need more memory, disk and sync capacity. Gemini Enterprise charges storage overage beyond included allowances. Ask Google for the storage allowance and overage rate, and size your Onyx index from your actual document volume.
Onyx Community or Enterprise Edition
Onyx Community Edition is MIT licensed and covers RAG, chat, agents and actions. Features such as RBAC, usage analytics and query history auditing sit in the Onyx Enterprise License, so a deployment that needs them carries a commercial license cost that a community deployment does not. Ask Onyx whether your required controls are in the Community code or the ee directories, and get the Enterprise license price in writing before you assume the open-source total.
Self-hosted GPUs or proprietary APIs
Models can run on your own GPUs through vLLM, Ollama or LiteLLM, or they can be called through a proprietary API such as Anthropic, OpenAI or Gemini. GPU capacity is a fixed or reserved cost that is worth it only at sustained high utilization. API models are metered per token, so cost rises with usage. vLLM itself has no paid edition, so its cost is hardware, infrastructure and the people who operate it. Model the expected token volume against both options before choosing.
Availability and staffing needs
A self-hosted deployment needs someone to patch, upgrade, back up and monitor it. Onyx publishes releases and a Helm chart, and vLLM ships new minor releases about every two weeks, so upgrade effort recurs. Gemini Enterprise includes Google's operation of the service. Count on-call coverage, upgrade cycles and monitoring as recurring staff time, and ask any implementation partner for a fixed scope covering these tasks.
Skills already in house
Teams that already run Kubernetes, PostgreSQL, GPU nodes and identity integration can absorb much of the Onyx workload internally, lowering the outside implementation cost. Teams without those skills will pay for training, hiring or a retainer. Check which of these skills you already have, and ask any implementation partner for a written split between what your staff will own and what the partner will deliver.
How to build the comparison with your own numbers
This sequence turns vague cost claims into a side-by-side, three-year view of Gemini Enterprise and a self-hosted Onyx deployment, built from your own seat counts, source inventory, model choices and staff time.
Export your Gemini Enterprise terms
Ask your Google Cloud account team or procurement contact for the signed agreement and order form, then record the seat count, edition, query or credit allowance, storage terms and any overage rates in one sheet. Confirm with Google whether allowances are pooled or per user and how overage is billed. You are done when every line of the current invoice maps to a contract clause or a named cost component.
Inventory sources and permission rules
List every system the assistant must search, such as SharePoint, OneDrive, Google Drive, Confluence, Jira, Slack, Teams and Salesforce, with the approximate document volume and the number of users who can see each one. Note how permissions are inherited today, including groups, shared drives and external sharing. Onyx syncs source permissions, so this list also tells you which connectors and identity integrations (Google OAuth, OIDC or SAML, SCIM) you must build and maintain. You are done when each source has an owner and a permission model.
Size Onyx Standard against Onyx Lite
Decide which deployment tier fits the use case. Onyx Standard includes Postgres, Vespa or OpenSearch for the index, Redis, model inference servers and object storage, along with background sync workers. Onyx Lite drops the vector database and background workers, which suits chat and agents without broad document search. Map your source list to one of these tiers, then ask your infrastructure team or DeployOpen for the node sizes, storage and backup footprint each component needs at your document volume. You are done when each component has a sizing assumption and a cloud or data center line item.
Choose a model path and estimate volume
Pick between self-hosted models served through vLLM or Ollama, and a proprietary API such as OpenAI, Anthropic or Gemini. For self-hosted models, estimate GPU capacity from expected concurrent users and token volume, and confirm accelerator availability and pricing with your cloud or hardware vendor. For API models, estimate monthly input and output tokens from a pilot sample and request the provider's current price sheet. You are done when you have a monthly token or GPU-hour estimate with a stated source for each price.
Add implementation, migration and staff time
Price the one-time work separately: architecture, deployment, connector configuration, identity integration, security hardening, backup setup, user migration and training. Add the recurring work: upgrades, patching, monitoring, model updates, connector maintenance and on-call coverage, and assign each to an internal role or a support retainer. Request fixed-scope written quotes for the one-time work and clear retainer terms for the recurring work. You are done when every hour of expected effort has an owner and a cost line.
Run a pilot group to measure real usage
Before committing, run a limited group of users against both the current assistant and the candidate Onyx deployment for a period agreed in advance. Record actual query volume, which sources people search, how often answers need follow-up, token consumption, GPU utilization and any permission exceptions. Use these measurements to replace the estimates from earlier steps. You are done when the pilot produces measured volumes that you can substitute into the model and staffing assumptions.
Build the three-year side-by-side view
Create one table with Gemini Enterprise and self-hosted Onyx in separate columns, and split each into one-time costs (implementation, migration, hardware purchase, initial training) and recurring costs (seats or subscriptions, allowances or overage, cloud or GPU hosting, API tokens, support and staff time). Add a sensitivity row for user growth and token growth. Ask your finance team to review the assumptions and your security team to confirm the controls both options provide. You are done when both columns total over the same three years and every figure links to a source or an assumption.
What vLLM and GPU capacity add to the self-hosted bill
vLLM carries no license fee. It is an open-source inference and serving engine licensed under Apache-2.0, and it has no paid edition. The cost of running it therefore sits in four places: accelerator capacity (GPUs or other accelerators), the surrounding infrastructure such as servers, storage and networking, the implementation work to deploy and integrate it, and the people who operate it. Ask your team or DeployOpen to size the accelerator capacity against the model sizes and concurrent users you expect, and ask whether that capacity is purchased once or rented monthly. Hardware is largely a one-time or fixed recurring charge, while utilization decides whether it is cost-effective.
vLLM runs on NVIDIA CUDA, AMD ROCm and Intel XPU GPUs, on x86 and ARM CPUs, on Apple silicon and on IBM Z CPUs, so the hardware choice is open, but it changes the cost profile. A single large model may need tensor parallelism across several GPUs in one node, and pipeline parallelism across nodes if it spans more than one machine. Multi-node runs use Ray or vLLM's own multiprocessing backend, and the vLLM security documentation states that inter-node traffic is insecure by default, so those nodes must sit on an isolated network. That network work and the extra GPUs both add cost, and both should appear as line items in any quote.
Onyx reaches vLLM through its OpenAI-compatible endpoints, including /v1/chat/completions and /v1/embeddings, so the model server can be swapped without rewriting the assistant. vLLM ships new minor releases about every two weeks, with v0.31.0 released on October 5, 2026, so upgrade and regression testing recurs as a staff cost. Keep proprietary API models as the alternative: they replace GPU capacity and operations with per-token charges that scale with usage. Compare the two by estimating monthly tokens, then ask for the per-token price in writing from each provider and the GPU purchase or rental price from your hardware vendor.
Questions buyers ask before comparing cost
How much effort does it take to self-host Onyx?
Expect the effort to depend on which Onyx deployment you choose and how many sources you connect. Onyx Standard runs vector and keyword indexing, background sync workers, model inference servers, object storage and Redis, along with Postgres and Vespa or OpenSearch for the index. Onyx Lite is a lighter chat and agents deployment without a vector database or background workers, so it has fewer moving parts. Effort rises with the number of connectors, the volume of documents, permission sync, SSO and the operating model you need for upgrades, backups and monitoring. Ask your implementation partner to split the work into installation, connector setup, identity integration, hardening and handover, each with its own written scope.
Can our team run Onyx without outside help?
Your team can run Onyx alone if it already operates Docker or Kubernetes, manages PostgreSQL and Redis, and has someone who owns identity integration and security review. The harder parts are usually permission sync across sources, model serving on GPUs, and keeping the stack patched. Gemini Enterprise moves most of that operational load to Google, so the staff time you save is the part that most often surprises buyers. Be honest about who on the team will carry on-call and upgrade duties, and whether that person has time. A fixed-scope engagement can cover the initial build and handover, while a retainer covers ongoing upgrades and testing if you want them.
How long does a migration from Gemini Enterprise take?
Timelines depend on scope. The main variables are how many sources you reconnect, how complex your Google Drive, SharePoint and Slack permissions are, whether you rebuild existing agents, and whether you run both systems while users switch. Each of these adds planning, testing and user communication. Ask for a written schedule that lists the sources, the agents to rebuild, the parallel-run period and the acceptance criteria. Any date given before the scope is agreed should be treated as provisional.
What drives the cost of GPUs compared with API model fees?
The two options shift cost in different directions. API models bill per usage, so cost follows query volume, prompt length and the model you choose, and there is little upfront hardware. Self-hosted models through vLLM, Ollama or LiteLLM need GPU or other accelerator capacity, which is paid for whether or not it is busy, plus power, cooling or cloud instance hours, and the people who operate it. vLLM itself has no paid edition, so its cost comes from the hardware, the surrounding infrastructure and the staff time. Usage levels, latency targets, redundancy needs and data residency rules usually decide which mix is cheaper. Ask your partner to model both paths against your actual query volume rather than a generic estimate.
Is Onyx Community Edition enough for our needs?
Onyx Community Edition is MIT licensed and covers RAG, AI chat, agents and actions, so it may be enough for many internal search and assistant use cases. The Enterprise Edition adds premium features such as role-based access control, and code in ee directories is under the Onyx Enterprise License. Check which features your governance team needs, including RBAC, usage analytics and query history auditing, and confirm with Onyx which edition includes each one. Then decide whether the gap justifies the licence cost in your quote.
What licence conditions apply to Onyx in production?
Onyx is open core. The Community Edition is MIT licensed, while code in ee directories falls under the Onyx Enterprise License, so you need to know which parts of your deployment use which terms. Confirm the exact licence files in the repository before you deploy, and ask your legal team to review them if you plan to modify the code or redistribute it. Also review the terms for the models and API providers you connect, since those are separate contracts with their own data handling terms.
What should we ask for in an implementation quote?
A useful quote lists the scope, deliverables, timeline and assumptions in writing before work starts. Ask for the deployment target (Docker, Kubernetes, Helm or Terraform), the number and type of connectors, how permission sync will be tested, the identity integration (Google OAuth, OIDC or SAML with SCIM), the model strategy (self-hosted through vLLM, Ollama or LiteLLM, or API models), backup and recovery steps, and the handover documents. Also ask which costs are one-time, such as setup and migration, and which recur, such as hosting, support and retainers. DeployOpen does not publish a price list, and a quote follows from the written scope.
Licence terms that change the total cost
The first cost question for Onyx is not hardware but which edition you run, because the license boundary decides which controls you pay for. Onyx Community Edition is MIT licensed and covers RAG, AI chat, agents and actions. Code in the ee directories falls under the Onyx Enterprise License, and the Enterprise Edition adds features such as role-based access control, SCIM user provisioning and usage analytics. If your model of cost assumes those controls are free, the estimate is wrong. Before you price anything, list the controls your auditors and identity team require, then check each one against the Community and Enterprise feature lists in the Onyx README and the Onyx LICENSE file. Ask Onyx which of those features are in the ee directories and what the Enterprise Edition license fee covers, in writing.
Onyx is also available as Onyx Cloud, a managed service. That changes the cost model from running infrastructure yourself to paying for a hosted service, and it changes where indexed content and conversations are stored. Treat Onyx Cloud as a separate option with its own quote, and confirm with the vendor whether it supports the connectors, permission sync and data residency you need. Do not assume that a self-hosted Enterprise Edition and Onyx Cloud carry the same terms or the same feature set. Before you request a quote, confirm the current release details on the Onyx releases page: version v4.9.2, released October 15, 2026, and Helm chart 0.9.17. Release cadence affects upgrade effort, so plan who applies new versions, how you test them, and whether your support scope covers them. Ask whether the license terms or feature split changed between the versions you plan to run.
What to ask for in a fixed-scope quote
A written scope turns a rough idea of Onyx costs into a quote you can compare against Gemini Enterprise, and it sets out who owns each piece of work after go-live. DeployOpen publishes no price list, so the quote follows from the scope below.
Inventory sources, permissions and connectors
List every system you want searchable, such as Google Drive, SharePoint, OneDrive, Confluence, Jira, Slack or Salesforce, and note which Onyx connectors cover each one. Ask the scope to state how document-level permissions will sync, how group membership is mapped, and which sources need custom work or a connector check before build. The output is a source register that sets the connector effort and the permission test cases your team will sign off on.
Size the Onyx edition and infrastructure
Decide between Onyx Standard, which includes vector and keyword indexing, background sync workers, model inference servers, object storage and Redis, and Onyx Lite, which drops the vector database and background workers. The scope should name the index backend (Vespa or OpenSearch), Postgres, Redis, model servers, and the expected user count and document volume that drive sizing. Ask for the sizing assumptions in writing so you can rerun them when headcount or content grows.
Choose the model path and its cost owner
Specify whether answers come from self-hosted models served through vLLM, Ollama or LiteLLM, from an API provider such as OpenAI, Anthropic or Gemini, or from a mix. Each path moves cost to a different owner: GPU capacity and operations for self-hosted serving, per-token billing for APIs. The scope should state which hardware is provided by you and which by DeployOpen, and what throughput target the model layer must meet.
Define air-gap, residency and identity needs
State whether the deployment must run air-gapped, which regions hold the index, database and model inference, and how SSO will work through Google OAuth, OIDC or SAML with SCIM provisioning. Also confirm which Onyx features your edition needs, since RBAC, usage analytics and query history auditing are listed among the Enterprise features and their licensing differs from the Community Edition. The output is a written boundary the architecture and audit evidence must respect.
Scope security testing and hardening
Agree which tests run before go-live, such as application and API testing of the Onyx interface, cloud and infrastructure review, and checks on the model endpoints. For self-hosted vLLM, include review of the --api-key coverage gaps and a reverse proxy that allowlists only user-facing endpoints. The deliverable should be a findings report with a retest plan. DeployOpen prepares evidence for compliance assessments, but independent auditors issue any audit report.
Assign backup, upgrade and monitoring duties
Write down who backs up the Postgres database, index and object storage, how restores are tested, and who applies Onyx and Helm chart upgrades and vLLM releases, which ship roughly every two weeks. Include monitoring of vLLM Prometheus metrics and alert ownership. Name the party responsible for each recurring task, so the quote separates one-time build work from ongoing operation. The output is a responsibility matrix signed by both sides.
Agree documentation, handover and retainer terms
List the runbooks, architecture diagrams, configuration records and access procedures to be delivered, along with the training sessions for administrators and the handover date criteria. Then decide whether ongoing support, upgrades and continuous testing will be covered by a retainer, and what response scope it includes. The completed scope becomes the written agreement, and the quote is produced from it.
How DeployOpen scopes a fixed-price Onyx implementation
DeployOpen plans, implements, hardens, documents and supports self-hosted open-source platforms inside your own cloud accounts, data centers or air-gapped networks. For a Gemini Enterprise cost comparison, we start from a written scope that separates the Gemini Enterprise bill you are replacing from the infrastructure, model and staff costs of running Onyx yourself.
How we scope
- Engagements are fixed-scope, with scope, deliverables and timelines agreed in writing before work starts.
- The written scope lists which Gemini Enterprise seats, connectors and storage tiers are being replaced, and which stay.
- Scope names the model path for Onyx, whether self-hosted models on your GPUs or external API providers, and who pays for each.
How we build
- We deploy Onyx with its index, database, Redis and model servers in the infrastructure you select and approve.
- Connectors to sources such as Google Drive, SharePoint, Slack and Confluence are configured with permission sync tested against sample users.
- Model serving is sized from your measured query volume, with cost tracking so you can compare actual spend against the original Gemini Enterprise invoice.
How we hand over
- You receive the deployment code, configuration records, runbooks and a cost model that shows which items are one-time and which recur.
- Handover covers operating Onyx, rotating credentials, upgrading the chart and images, and reading usage analytics and query history.
- Documentation includes the evidence your auditors will ask for, such as access settings, logging and data location, prepared for their review rather than issued by us.
How we support
- Ongoing support, upgrades and continuous testing are available as retainers, scoped separately from the implementation.
- Retainers can cover patching of Onyx and its dependencies, model server updates and periodic permission sync checks.
- Ask for a written quote that lists implementation, infrastructure, model usage, and staff time as separate lines, so you can compare it with your current Gemini Enterprise contract.
