New VDF AI Enterprise is now available on AWS Marketplace
Enterprise AI Comparison

deepset Alternative for
Governed Enterprise Agents

deepset builds Haystack, the open-source framework many AI engineers use for retrieval and agents, and sells Haystack Enterprise Platform on top of it as a platform for sovereign AI. VDF AI serves the same sovereignty-minded buyers, so the overlap is real. The difference is who builds and who uses: deepset gives engineers code-aligned pipelines they can export, while VDF AI gives business teams governed agents, workflows and chat. Facts below were checked against deepset’s site and GitHub on 8 November 2026.

Book a Demo Skip to the Verdict
QUICK VERDICT

Same Sovereignty Goal, Different Builders

deepset is the stronger choice when an AI engineering team owns the build, wants an Apache 2.0 framework underneath so its pipelines stay portable, and needs certificates such as SOC 2 Type II and ISO 27001 to get through procurement.

VDF AI is the stronger choice when business teams must build and use the agents themselves, under per-role tool grants, approval steps and an evidence trail, with Cloud prices published up front and on-premises licensing fixed per seat for the year.

deepset
VDF AI
Best for
AI engineers building retrieval and agent pipelines
Business teams building and using governed agents
Open-source core
Haystack, Apache 2.0, nearly 27,000 GitHub stars
Proprietary platform; every service has an HTTP API
How agents are built
Visual pipeline builder that exports Python or YAML
No-code agents and Networks, plus chat for staff
Free entry point
Studio: one user, 100 pipeline hours, cloud only
Free Cloud plan; 3-month trial on every on-prem bundle
Paid pricing
Enterprise priced on request
Cloud from $20 a month; on-prem fixed annual Seat Bundles
Certifications
SOC 2 Type II, ISO 27001, HIPAA, CSA STAR Level 1
Controls mapped in the Trust Center; runs in your boundary
Governance in the workflow
RBAC, traces, guardrails, audit logs
Per-role tool grants, Human Approval nodes, Vault evidence
PRICING & DEPLOYMENT

deepset Pricing vs VDF AI Licensing

One vendor publishes a free tier and quotes everything above it; the other publishes its Cloud plans and fixes on-premises cost per seat per year.

deepset Pricing

Checked 8 Nov 2026 on deepset.ai/pricing

Studio$0One user and one workspace, deployed in deepset’s cloud
Studio limits100 hPipeline hours, 50 files up to 10 MB, two development pipelines
EnterpriseCustomUnlimited users, workspaces and production pipelines
Enterprise onlySSO, role-based access, dedicated infrastructure, deployment options on request
Haystack Enterprise StarterUnpublishedSupport for the open-source framework: up to 4 hours of consultation, 6 months of extended version support

Studio is a prototyping tier: anything that needs production pipelines, single sign-on or role-based access starts with a sales conversation. deepset can add forward deployed engineers who build alongside your team.

VDF AI Licensing

Published on the VDF AI pricing page

CloudFrom $0A Free plan, then Starter at $20 a month and Professional at $50 per seat a month
On-premisesSeat BundlesOne price for the year, with the whole platform in each seat
Trial3 monthsAny on-prem bundle can begin with a free platform trial
Not metered on-premChat use, model tokens, policy checks and audit records
PeopleA named VDF AI engineer on the Growth, Scale and Enterprise bundles

Neither deepset Enterprise nor VDF AI on-prem bundle prices are public, so compare written quotes. What differs is the unit: deepset’s plans count pipeline hours, files and pipelines, while VDF AI counts seats, plus API calls if you add an API pack.

Where the two genuinely overlap

Both vendors sell sovereignty. deepset’s platform page leads with it, and VDF AI’s self-hosted and air-gapped packages exist for the same banks, insurers and public bodies. Both are model-agnostic, both can run on your own infrastructure, and both put RBAC, single sign-on and audit logs around the agent. The decision turns on the builder: deepset produces Haystack pipelines that engineers can export and run as code, while VDF AI keeps agents and workflows inside one governed product that business teams operate and employees reach through chat. See how we deploy inside your perimeter in the on-prem reference architecture.

GOVERNANCE

Governing Agents in Production

Both platforms log and restrict; they differ in where approvals and evidence live.

Access control
deepsetRole-based access control and SSO support, both on the Enterprise plan
VDF AIRBAC on every plan, tools granted per role, and native Microsoft Entra ID sign-in for self-hosted installs
Guardrails
deepsetGuardrails and policy enforcement on pipelines; open-source Haystack adds agent hooks such as before_llm and before_tool
VDF AIEach Network declares its allowed models, allowed external services and a budget cap, checked before a step runs
Human oversight
deepsetApproval, audit and rollback controls appear in its GTC reference architecture; no approval step is listed as a platform feature
VDF AIHuman Approval nodes pause a Network until the accountable owner signs off
Observability
deepsetUnified run history, traces, performance monitoring and usage reports
VDF AIEvery intermediate stage of a run is kept, with an audit view you can filter by user, action, Network and date
Evaluation
deepsetPlayground, Prompt Explorer, feedback collection and groundedness observability for retrieval answers
VDF AIThe Model Evaluation Suite scores any model on your own scenarios with BLEU, ROUGE, METEOR and BERTScore
Audit evidence
deepsetAudit logs and full traces, backed by SOC 2 Type II and ISO 27001 for the platform itself
VDF AIA Vault record for each execution, assembled into EU AI Act and sector evidence packs

How the controls fit together is set out on the AI governance platform page.

BUILDING AGENTS

Context Engineering vs Governed Agent Building

The layer where the two products compete head to head.

What deepset provides

  • Haystack open source — explicit pipelines with loops, branches and agents; version 3.4.1 shipped on 21 October 2026
  • Visual pipeline builder — templates, a debugger, custom components and MCP, with export to Python or YAML
  • 100+ Haystack integrations — models from Anthropic, OpenAI, Mistral, Qwen, DeepSeek and Ollama; vector stores from PostgreSQL and OpenSearch to Qdrant, Weaviate and Milvus
  • Serverless deployment — one-click deploys with autoscaling, consumed through a REST API
  • Engineering ownership — components, retrieval tuning and the applications that call each pipeline belong to your developers
  • Front end not included — the product pages offer a REST API and shareable prototypes, not a governed chat workspace for staff

What VDF AI provides

  • Agents and Networks — no-code agents and multi-agent workflows with a planner, routing and approval steps
  • VDF AI Chat — permission-aware retrieval and per-turn audit for every employee
  • MCP gateway — a tool registry granted per role, plus custom MCP servers and HTTP tools for in-house systems
  • Shipped connectors — Microsoft 365, Google, Jira, Confluence, GitHub, Slack and Notion
  • VDF AI Router — a model chosen per step on quality, cost, latency and energy, with an air-gap mode
  • No open-source core — there is no framework to fork; developers work through the HTTP API every service exposes

deepset renamed its commercial product from deepset AI Platform to Haystack Enterprise Platform on 19 December 2025, so older reviews and RFP answers may use the previous name.

ARCHITECTURE

How Each Platform Is Built

An open framework with an operational layer on top, against one governed product.

deepset

Open framework plus operational layer

  • Haystack — components, pipelines and agents in Python, Apache 2.0
  • Haystack Enterprise Platform — build, test, deploy and govern on top of Haystack
  • Hayhooks — serves pipelines as REST, OpenAI-compatible or MCP endpoints
  • Haystack Enterprise Starter — support and blueprints for self-run Haystack
  • Forward deployed engineers — deepset staff who build next to your team

Infrastructure partners named on deepset’s platform page include NVIDIA, AWS, Azure, HPE, secunet and Hetzner.

VDF AI

One governed product, cloud or self-hosted

  • Chat, Agents and Networks — where employees and builders do their work
  • Router — model selection per step across local and commercial sources
  • MCP gateway — role-scoped tool access into enterprise systems
  • Vault — per-run records behind Living Knowledge and evidence packs
  • Model sources — vLLM, Ollama, llama.cpp or another OpenAI-compatible server

The platform services are containers on CPU nodes backed by PostgreSQL; GPUs are only needed for the models you host yourself.

FAIR PLAY

When to Choose deepset

For engineering-led retrieval work, an open core with a commercial layer is hard to argue against.

deepset is the right call when…

  • Your AI engineers already build with Haystack and want the managed layer from the team that maintains it.
  • Portability is non-negotiable: you want pipelines you can export as Python or YAML and run without the vendor.
  • Procurement asks for SOC 2 Type II and ISO 27001 certificates, or a vendor that states HIPAA compliance.
  • Retrieval quality is the product, so context engineering, evaluation and groundedness monitoring are daily work.
  • You want the vendor’s own engineers building beside your team during the first deployments.
  • A public-sector precedent matters: deepset says its platform underpins the European Commission’s AI@EC platform.
Where deepset is genuinely strong
Open source at the core

Haystack is Apache 2.0 with nearly 27,000 GitHub stars. Version 3.4.1 shipped on 21 October 2026, and the 2.x line still receives patch releases.

Certificates on file

SOC 2 Type II, ISO 27001, HIPAA and GDPR compliance, and CSA STAR Level 1 are all listed on the platform page.

Retrieval engineering depth

A debugger, Prompt Explorer, evaluation tools and groundedness observability, built for people who tune context for a living.

A sovereign partner network

An NVIDIA reference architecture for air-gapped agents, and partners such as secunet and Hetzner for European hosting. Named customers include Airbus, The Economist and OakNorth.

DECISION SIGNALS

Signals That VDF AI Fits

When the bottleneck is governed use across the business, not retrieval tuning.

Business teams own the use cases

Claims, procurement and HR teams want to build and change agents themselves. No-code Agents and Networks keep that work out of the engineering backlog.

Every employee needs a governed chat

VDF AI Chat gives staff permission-aware answers out of the box, instead of a separate front end your developers build and secure for each pipeline.

Tool access must follow roles

Grants in the MCP gateway decide which team may call which system, and each call lands in the audit trail with the person and the run behind it.

Approvals belong in the process

A Human Approval node pauses the workflow for sign-off, so oversight is a visible step in the Network rather than code someone wrote around a pipeline.

Finance wants a price list

VDF AI Cloud prices are public, and on-prem Seat Bundles fix the yearly cost per seat. deepset’s first paid step, Enterprise, begins with a quote.

Many models, one policy

The VDF AI Router chooses a model per step on quality, cost, latency and energy, and its air-gap mode keeps every call inside your network.

For the open-source side of the market, read our RAG frameworks comparison, or see the on-premises agentic platform roundup for the wider vendor field.

COEXISTENCE

Keep Haystack, Add Governance

How teams that already run Haystack pipelines usually bring VDF AI in.

1
Keep the pipelines that work

Retrieval pipelines your engineers have tuned stay in Haystack, whether they run on deepset’s platform or on your own servers. None of them has to be rewritten for VDF AI to arrive.

2
Publish each pipeline as a tool

Hayhooks, deepset’s open-source server, can expose a pipeline as a REST endpoint or run an MCP server that offers it to MCP clients as a tool.

3
Register it and grant it by role

Add the MCP server to the VDF AI MCP gateway, or call the REST endpoint as an HTTP tool, then grant it only to the roles that need it. Neither vendor documents this pairing, so prove it in the pilot before you depend on it.

4
Wrap the business workflow around it

Place the pipeline inside a Network with an approval step, a budget and audit, then open it to staff through VDF AI Chat. Engineers keep owning retrieval quality; the business owns the process.

Plan a Haystack Pilot
FULL COMPARISON

Capability by Capability

deepset details taken from deepset.ai and GitHub on 8 November 2026.

CapabilityVDF AIdeepset
Product typeGoverned agent platform for business teams and ITOperational layer on top of the open-source Haystack framework
Open sourceProprietary; every service exposes an HTTP APIHaystack and Hayhooks under Apache 2.0
BuildingNo-code Agents and multi-agent NetworksVisual pipeline builder with export to Python or YAML
End usersVDF AI Chat for employees, with per-turn auditREST API for your applications; shareable prototypes for testers
Retrieval engineeringPermission-aware RAG in chat; federated vector searchContext engineering, 11 named vector stores, groundedness observability
Tools and MCPMCP gateway with per-role grants; custom MCP servers and HTTP toolsMCP and tool calling inside pipelines; Hayhooks can serve pipelines over MCP
Model choiceRouter picks a model per step, with an air-gap modeAny provider per component: Anthropic, OpenAI, Mistral, Llama, Qwen, Ollama and more
Human approvalHuman Approval nodes inside NetworksShown in a GTC reference architecture; not listed as a platform feature
CertificationsControls mapped to EU AI Act, GDPR, DORA, HIPAA and NIS2 in the Trust CenterSOC 2 Type II, ISO 27001, HIPAA, GDPR, CSA STAR Level 1
DeploymentVDF AI Cloud, self-hosted, private cloud, air-gappedManaged cloud, self-hosted or hybrid; air-gapped reference architecture
PricingCloud plans published; on-prem fixed annual Seat BundlesStudio free; Enterprise priced on request
Vendor engineersNamed VDF AI engineer from the Growth bundle upForward deployed engineers; Starter support for self-run Haystack

deepset revises plan limits and product names from time to time. Confirm current terms and certificates with deepset before you sign.

deepset sources checked for this page

FAQ

Frequently Asked Questions

What platform and security teams ask when deepset is on the shortlist.

deepset is the Berlin company behind Haystack, the Apache 2.0 open-source framework for building retrieval pipelines and AI agents in Python. Its commercial product, called deepset AI Platform until it was renamed on 19 December 2025, is now Haystack Enterprise Platform: an operational layer on top of Haystack with a visual pipeline builder, testing tools, one-click deployment to deepset’s cloud or your own infrastructure, and controls such as RBAC, traces, guardrails and audit logs. deepset also sells Haystack Enterprise Starter, a support package for teams that run the open-source framework themselves.

deepset’s pricing page (checked 8 November 2026) lists two plans. Studio costs $0 and covers one user and one workspace, 100 pipeline hours, 50 files of up to 10 MB each and two development pipelines, deployed in deepset’s cloud. Enterprise is priced on request and adds unlimited users, workspaces and pipelines, production pipelines with high availability, SSO, role-based access control and cloud or custom deployment. Haystack Enterprise Starter has no published price. VDF AI publishes its Cloud plans (a Free plan, Starter at $20 a month, Professional at $50 per seat a month, and Enterprise Cloud on contract) and licenses on-premises use through fixed annual Seat Bundles, each of which can start with a three-month platform trial.

Yes. deepset says Haystack Enterprise Platform deploys to its managed cloud or self-hosted on your infrastructure, and its enterprise page adds hybrid set-ups. At NVIDIA GTC Berlin in October 2026 it announced an open reference architecture, built with NVIDIA, for self-improving agents in locally controlled and air-gapped environments; a sovereign cloud with secunet, NVIDIA and Red Hat for classified information was described as planned. VDF AI self-hosts on a conformant Kubernetes cluster, documents an air-gapped install from your internal container registry, and also runs as VDF AI Cloud.

deepset’s platform page lists SOC 2 Type II and ISO 27001 certification, HIPAA and GDPR compliance, and CSA STAR Level 1. When a security questionnaire insists on an independent audit report, that is a genuine advantage for deepset. With VDF AI the platform usually runs inside your own audited environment, and our Trust Center maps its controls to the EU AI Act, GDPR, DORA, HIPAA and NIS2 so reviewers can check them against your own policies.

For most of the value, yes. deepset presents the platform as one for AI and business teams, but its design is code-aligned: pipelines built in the visual editor export to Python or YAML, and Haystack itself is a Python framework that engineers extend with custom components. That suits teams whose AI engineers tune retrieval for a living. VDF AI is aimed at a different builder: business teams assemble agents and multi-agent Networks without code, employees use them through VDF AI Chat, and developers reach every service through an HTTP API.

Usually, through a standard interface. Hayhooks, deepset’s open-source serving tool, can publish a Haystack pipeline or agent as a REST endpoint, an OpenAI-compatible chat endpoint or an MCP server. VDF AI registers custom MCP servers in its MCP gateway and calls REST endpoints as HTTP tools, so a tuned pipeline can become a tool that only the right roles may call, with every call kept in the audit trail. Neither vendor documents this pairing, so treat it as something to prove in a pilot.

Both keep records; they differ in what they assemble from them. deepset’s platform keeps a unified run history with traces, audit logs and usage reports, and enforces guardrails on pipelines. VDF AI keeps a Vault record for every execution, places Human Approval nodes inside workflows where an accountable person must sign off, grants tools per role, and builds EU AI Act and sector evidence packs from those records. If your compliance team needs an evidence pack rather than raw logs, check that part of each vendor’s demo closely.

Start from who will build and who will use the agents. If an AI engineering team owns retrieval-heavy applications and wants an open-source core it can export and run anywhere, deepset is a strong fit. If business teams need to build and run agents under policy, with approvals, per-role tool access, chat for every employee and evidence for auditors, VDF AI fits better. The two can also coexist: keep the Haystack pipelines that work and govern how the business uses them in VDF AI.
Explore further

Related resources

Teams weighing deepset usually care most about retrieval quality and where data lives, so these links cover private RAG, evaluation and the other sovereign platforms on the shortlist.

Running Haystack Already?

Bring one Haystack pipeline and the business workflow that should use it. We will map both onto VDF AI with role-based tool grants, an approval step and an audit trail, on infrastructure you control.

Read the On-Prem Guide Book a Pipeline Review