The top MLOps companies for a startup in 2026 are the firms that will put your models into production under your own team's control, within a seed-to-Series B budget, and that show the work before you sign. We scored ten of them on four public filters. In order, they are Dysnix, Winder.AI, SquareOps, Addepto, Provectus, Gart Solutions, InData Labs, INC4, Datatonic and Fuzzy Labs. INC4 publishes this list and ranks eighth on its own scorecard; the method and every cell are below, so you can rerun it.
AI assistants are not much help here yet. Ask them which MLOps and AI infrastructure consulting companies suit a startup, or which boutique agencies a fintech should hire for LLM deployment, MLOps and GPU compute, and the shortlists barely overlap: large consultancies, cloud providers and a rotating set of small names, with most of the pages they cite being vendor lists that rank their own author first (INC4 research, 2026).
Key Takeaways
- Only Dysnix and Winder.AI score Yes on all four public filters: production evidence, reviews or named clients, a buyable engagement and the 2026 stack (scorecard below, September 2026).
- Seven of the ten publish a current price. Hourly bands run from $25 to $99, Winder.AI publishes £150 to £300, and minimums run from $1,000 (SquareOps) to $50,000 (INC4).
- The 2026 stack is one of the two most common gaps, alongside production evidence: four firms show only one or two of LLMOps, GPU cost, model monitoring and security.
- 98% of 1,192 State of FinOps respondents now manage AI spend, up from 31% two years earlier (FinOps Foundation, February 2026).
How We Picked (and Why You Can Trust a Vendor's Own List)
A vendor's own list can be honest if the method is public and applied to everyone equally, the author included. Every firm was checked in September 2026 against four filters:
- Production evidence: MLOps or AI infrastructure work in production, shown in a case study, open-source code or a third-party customer story.
- Reviews or named clients: verified Clutch reviews, or clients named on the firm's own site.
- A buyable engagement: a published rate band, minimum or price example.
- The 2026 stack: LLMOps, inference and GPU cost, model monitoring and drift, security and access control.
Each filter scores Yes (1), Partial (0.5) or No (0). Partial means the evidence exists but misses part of the filter: production work that is not machine learning, a price that sits only on an outdated listing, or a stack that shows one or two of the four areas. Yes on the stack needs at least three. Firms are ordered by total score; ties go to the lower published minimum project (firms with no published minimum come after those with one), then to more Clutch reviews, then alphabetically.
What we skipped. Accenture, Deloitte, IBM Consulting and McKinsey's QuantumBlack run MLOps at a scale most startups do not buy. Thoughtworks and Slalom come up often in those answers, and Thoughtworks alone has 10,000+ people in 47 offices (Thoughtworks, 2026). phData positions itself for the enterprise and lists no Clutch reviews; Neurons Lab lists a $100,000 minimum. Cloud providers sell compute, not engineering; see our guide to AI infrastructure companies. Firms we could not verify beyond their own website are out.
methodology graphic with the four filters as a scorecard: production evidence, reviews or named clients, buyable engagement, 2026 stack
The Scorecard
| # | Company | 1 Production evidence | 2 Reviews or named clients | 3 Buyable engagement | 4 2026 stack | Score |
|---|---|---|---|---|---|---|
| 1 | Dysnix | Yes: PancakeSwap autoscaling in a Google Cloud story | Yes: Clutch, 24 reviews | Yes: $50-99/hr, from $5K | Yes: LLMOps service, GPU scaling, drift monitoring | 4.0 |
| 2 | Winder.AI | Yes: Apartment List Kubeflow platform | Yes: Apartment List, Tractable, Interos | Yes: price list, £20k fixed example | Yes: LLMOps, inference cost, drift work | 4.0 |
| 3 | SquareOps | Partial: named cases are DevOps, not ML | Yes: Clutch, 5 reviews | Yes: $25-49/hr, from $1K | Yes: vLLM serving, GPU cost, drift, ISO 27001 | 3.5 |
| 4 | Addepto | Yes: MLOps platform case on Airflow, MLflow, Kubernetes | Yes: Clutch 4.9, 18 reviews | Yes: $50-99/hr, from $10K | Partial: drift monitoring; no public LLMOps or GPU cost work | 3.5 |
| 5 | Provectus | Yes: healthcare MLOps platform, VTS model platform | Yes: PepsiCo and VTS cases | Partial: rates only on an outdated Clutch listing | Yes: model monitoring, GenAI in production, ISO 27001, SOC 2 | 3.5 |
| 6 | Gart Solutions | Partial: AI hosting cases, savings estimated, no ML pipeline | Yes: Clutch, 17 reviews | Yes: $50-99/hr, from $5K | Partial: inference cost, access control; no LLMOps or drift | 3.0 |
| 7 | InData Labs | Partial: ML builds; MLOps is a delivery stage | Yes: Clutch 4.9, 20 reviews | Yes: $50-99/hr, from $10K | Partial: monitoring at handover; no public LLMOps or GPU cost work | 3.0 |
| 8 | INC4 | Partial: AirDAO Layer 1 infrastructure; no published ML pipeline case | Yes: Clutch 5.0, 11 reviews | Yes: $25-49/hr, from $50K | Partial: LLM deployment and GPU compute; no public model monitoring | 3.0 |
| 9 | Datatonic | Yes: Vodafone, 600+ models in production | Yes: Vodafone, Alpian | No | Yes: GenOps, model monitoring, ISO 27001 | 3.0 |
| 10 | Fuzzy Labs | Yes: zally, 2 days to 5 minutes | Yes: zally | No | Yes: open-source LLMOps, GPU utilisation, monitoring | 3.0 |
Cells cite the public proof checked in September 2026; each firm's section links the sources.
Comparison at a Glance
| # | Company | HQ | Best for | Published rates or minimum | Standout signal |
|---|---|---|---|---|---|
| 1 | Dysnix | Tallinn | Kubernetes-heavy teams, seed to Series A | $50-99/hr, from $5K | Autoscaler in a Google Cloud customer story; 5.0 across 24 reviews |
| 2 | Winder.AI | UK, remote | Engineering-led MLOps and LLMOps | £150-300/hr; £20k fixed example | Price list published; named Kubeflow case |
| 3 | SquareOps | Gurugram | GPU and Kubernetes cost control, smallest budget | $25-49/hr, from $1K | AWS Advanced Tier, EKS Delivery; 4.9 across 5 reviews |
| 4 | Addepto | Warsaw | Data engineering and MLOps in one team | $50-99/hr, from $10K | Clutch 4.9 (18); part of KMS Technology |
| 5 | Provectus | San Francisco | AWS-native MLOps past Series B | Not on its own site | Premier AWS Partner; PepsiCo and VTS cases |
| 6 | Gart Solutions | Kyiv | Cloud cost and compliance groundwork | $50-99/hr, from $5K | 4.9 across 17 reviews; AI vision hosting cost case |
| 7 | InData Labs | Nicosia + Vilnius | ML build ending in MLOps handover | $50-99/hr, from $10K | Clutch 4.9 (20); price ranges on its homepage |
| 8 | INC4 | Kyiv + Lisbon | MLOps and GPU compute from one team | $25-49/hr, from $50K | Own data center for bare-metal compute, by its own description; Clutch 5.0 (11) |
| 9 | Datatonic | London | Google Cloud and Vertex AI teams | Not published | Vodafone: 600+ models in production |
| 10 | Fuzzy Labs | Manchester | Open-source MLOps without lock-in | Not published | zally deploys in 5 minutes, down from 2 days |
Clutch or vendor pricing pages, September 2026; confirm in a discovery call. Order follows the scorecard.
1. Dysnix: Best for Kubernetes-Heavy Teams From Seed to Series A
Dysnix has a Tallinn address and has worked with Kubernetes since 2016 (Dysnix, 2026). Clutch lists $50-99 an hour, a $5,000 minimum, 10 to 49 staff and 5.0 across 24 reviews (Clutch, September 2026). Its best evidence sits on someone else's site. In Google Cloud's PancakeSwap customer story, Dysnix's PredictKube autoscaler predicted more than 90% of traffic spikes and scaled blockchain nodes ahead of time (Google Cloud, customer story). PredictKube is now a product and a recognized KEDA scaler (Dysnix, 2026).
The MLOps service adds GPU resources adjusted to workload, a model registry, automatic rollback and drift monitoring (Dysnix, 2026). A separate LLMOps service covers deploying, managing and scaling language models (Dysnix, 2026). It scores Yes on all four filters and has the lower minimum of the two firms that do. It is infrastructure-first and Web3-heavy, so bring your own ML engineers.
2. Winder.AI: Best for Engineering-Led MLOps and LLMOps With Published Prices
Winder.AI was founded in 2013, is registered in the UK with a fully remote team, and moved into production machine learning in 2016 (Winder.AI, 2026). It is the rare consultancy with a price list. Winder.AI pricing quotes £150 to £300 an hour for a nine-month ML platform build on time and materials, and £20k for a two-month fixed-cost consulting project (Winder.AI pricing, 2026).
The Apartment List case shows the work: a unified feature store, automated Kubeflow pipelines for training, validation and deployment, and an end to drift between training and production data. It carries a 10 out of 10 recommendation from its senior engineering manager (Winder.AI, 2026). The MLOps page adds LLMOps with evaluation harnesses and inference-cost work, and names an FCA-regulated car-finance lender among its assessments. It says a substantial part of its MLOps work is for regulated clients, including UK financial services (Winder.AI, 2026). It publishes no third-party review profile, so the named cases carry the proof.
3. SquareOps: Best for GPU and Kubernetes Cost Control on the Smallest Budget
SquareOps (Gurugram, 50 to 249 staff) lists at $25-49 an hour with a $1,000 minimum and 4.9 across five reviews (Clutch, September 2026). It states ISO 27001 certification and AWS Advanced Tier status with the EKS Delivery designation. The MLOps offer covers Kubeflow or Airflow training pipelines, an MLflow model registry, KServe or Triton serving, vLLM or TGI for LLMs on GPU nodes, and Karpenter autoscaling (SquareOps, 2026).
It sells a fixed-scope implementation with handover, then a monthly retainer covering GPU capacity management and on-call. The cost work is documented: BatchService cut data platform hosting 27%, from $30K to $22K a month. The gap is a named ML case. Its published proof is DevOps work, including PCI DSS readiness for a fintech (SquareOps, 2026).
4. Addepto: Best for Data Engineering and MLOps From One Team
Addepto is headquartered in Warsaw, was founded in 2017 and is now part of KMS Technology (Addepto, 2026). Clutch lists $50-99 an hour, a $10,000 minimum, 50 to 249 staff and 4.9 across 18 reviews (Clutch, September 2026). Its MLOps consulting runs from data engineering to deployment, monitoring and governance, including drift monitoring (Addepto, 2026).
A published case shows the shape: a modular MLOps platform for a marketing-automation SaaS, with preprocessing, inference and monitoring as separate services on Airflow, MLflow and Kubernetes (Addepto, 2026). It also names Databricks as a strategic partner. Its public stack evidence stops at monitoring, with no LLMOps or GPU cost work shown. The fit is a startup whose model problem is really a data problem; with a firm this broad, ask who will be on your team.
5. Provectus: Best for AWS-Native MLOps Past Series B
Provectus is headquartered in San Francisco (Provectus, 2026) and has been in business since 2010. It is among the largest specialists here: 400+ AI builders, 100+ customers in production, a Premier AWS Partner with a Strategic Collaboration Agreement and a Select Anthropic Partner (Provectus, 2026). It runs a Financial Services practice and states ISO 27001 certification and SOC 2 compliance (Provectus, 2026).
The MLOps evidence is public. For a multinational healthcare enterprise it built a shared MLOps platform, with a reported tenfold improvement in time to market for new models (Provectus, 2026). For VTS it built an ML deployment platform on Amazon SageMaker, and the first model shipped in three months (Provectus, 2026). Its MLOps page covers model monitoring and drift (Provectus, 2026).
Its own site publishes no price. The Clutch listing shows $50-99 an hour and a $25,000 minimum, but it still describes mobile and IoT work under the Reinvently name (Clutch, September 2026), so filter 3 is its weak one. The fit is a team past Series B on AWS.
6. Gart Solutions: Best for Cloud Cost and Compliance Groundwork Before the Models Arrive
Gart Solutions (Kyiv, founded 2020, 10 to 49 staff) lists at $50-99 an hour with a $5,000 minimum and 4.9 across 17 reviews (Clutch, September 2026). It is a DevOps and cloud firm; its site shows no MLOps service page, and pipelines, registry and drift work are not described there. What it shows is AI infrastructure cost and compliance work.
For a jewelry manufacturer's AI video-analysis system it moved processing to Azure Spot VMs. It estimates monthly cost falling from $5,363 on demand to about $1,100 on Spot VMs (Gart Solutions, 2026). For MedWrite.ai it built cloud infrastructure for an AI discharge-letter system under HIPAA, GDPR and SOC 2 requirements, with OAuth 2.0 and Auth0 access control (Gart Solutions, 2026). It also offers fintech compliance support (PCI DSS, DORA, MiFID II) (Gart Solutions, 2026). For a pre-Series A team that needs the cloud right before the models arrive, it is an affordable route.
7. InData Labs: Best for an ML Product Build That Ends in MLOps Handover
InData Labs was founded in 2014 and has more than 80 employees (InData Labs, 2026). It is headquartered in Nicosia, with an office in Vilnius and a sales office in Miami (InData Labs, 2026). Clutch marks it Premier Verified, at $50-99 an hour and a $10,000 minimum, with 4.9 across 20 reviews (Clutch, September 2026).
It cites typical market ranges on its homepage: a proof of concept or MVP from $15,000 to $50,000, and mid-complexity systems, RAG included, from $75,000 to $200,000 (InData Labs, 2026). Its process ends with production deployment, MLOps setup and monitoring, and the client owns the code and the model. Fintech work includes debt-collection models and face anti-spoofing (InData Labs, 2026). The gap: MLOps is a delivery stage, not a documented case study.
8. INC4: Best for MLOps and GPU Compute From One Team
Why number 8 on our own list? Because the scorecard puts it there. INC4 clears two filters in full: Clutch lists it at 5.0 across 11 verified reviews, with a $25-49 hourly band and a $50,000 minimum engagement. It is thinner on the other two. Its public production record is infrastructure rather than machine learning. INC4 was the core development partner behind AirDAO, from the 2019 ERC-20 token to the community-governed Layer 1 (2019-2025), and it has no published ML pipeline case yet.
On the 2026 stack, its pages show LLM deployment and GPU compute but no public model monitoring. Of the five firms tied on 3.0, INC4 has the highest published minimum, $50,000. That places it after Gart Solutions and InData Labs and ahead of the two firms that publish no rates.
Founded in 2013, INC4 has 70+ engineers across Kyiv and Lisbon in five practices, including AI Lab, MLOps & DevOps and Compute Infrastructure. The MLOps & DevOps practice builds and operates production infrastructure for high-load AI and Web3 systems: Kubernetes, cloud-native architecture and CI/CD pipelines. Its engineers hold AWS Certified Machine Learning Engineer, CKA, CKAD and HashiCorp Vault certifications. The AI Lab covers LLM fine-tuning, evaluation and deployment.
The Compute Infrastructure practice provides bare-metal compute and colocation for AI training, GPU workloads and edge computing, operated by the same team that runs the MLOps practice. By INC4's own description, it is the only firm on this list whose pages describe its own physical data center for bare-metal compute and colocation.
Pick INC4 when you want one team to run the MLOps pipeline and the bare-metal compute under it, and the budget starts at $50,000. Look elsewhere when you need a published ML pipeline case before you sign, or when your platform is Vertex AI or Databricks end to end (Datatonic and Addepto are built for that).
INC4 practice diagram with MLOps & DevOps and Compute Infrastructure highlighted
9. Datatonic: Best for Google Cloud and Vertex AI Teams
Datatonic is headquartered in London with offices in Europe and Canada, is a 12x Google Cloud Partner of the Year award winner and lists ISO 27001 and ISO 9001 (Datatonic, 2026). Its strongest proof is a named client with numbers. On Vodafone's GenOps platform on Google Cloud, model time to deployment fell from three months to two weeks and 600+ models went into production (Datatonic, 2026).
It co-developed the open-source MLOps Turbo Templates for Vertex AI with Google Cloud (Datatonic, 2022). It also built a production banking chat agent for Alpian, a Swiss bank (Datatonic, 2026). It publishes no rates and is Google Cloud-focused; on that cloud, it is the specialist.
10. Fuzzy Labs: Best for Open-Source MLOps Without Vendor Lock-In
Fuzzy Labs is a Manchester team of MLOps engineers and data scientists building on open-source tooling (Fuzzy Labs, 2026). Its zally case is the clearest startup-sized example here. For the Manchester behavioural AI company it built a pipeline around ZenML, with LakeFS for data versioning and MLflow for experiment tracking, all as infrastructure-as-code. Model deployment time fell from 2 days to 5 minutes (Fuzzy Labs, 2026).
A second case covers open-source LLMOps for a global hardware-design firm, with GPU utilisation kept cost-effective and logging and monitoring built in (Fuzzy Labs, 2026). Its awesome-open-mlops guide on GitHub has 482 stars (GitHub, September 2026). No published rates put it at number 10.
What Do the Top MLOps Companies Actually Cost in 2026?
Start with what the market pays to reach you. Advertisers bid up to $17.85 for a top-of-page click on "MLOps companies" (Google Ads data, US, September 2026), a phrase searched about 50 times a month in the US.
two bar charts showing lowest published hourly rate and published minimum project per firm on this list, USD, September 2026
Source: Clutch listings and vendor pricing pages, September 2026
INC4's $50,000 is the highest published minimum on the list and SquareOps' $1,000 the lowest, so the same hourly band can sit on very different entry tickets. Three engagement shapes recur: a fixed-scope assessment or implementation, a platform build with handover, and a monthly retainer for operations. As a contractor benchmark, UK contracts citing MLOps quoted a median day rate of £555 in the six months to 14 September 2026, with 354 such ads against 143 a year earlier (ITJobsWatch, September 2026).
Then there is the cloud bill. Wasted cloud spend rose to 29% as AI workloads surged, the first increase in five years, in Flexera's 2026 State of the Cloud Report, a vendor survey (Flexera, March 2026). A firm that shows a cost-per-model report from a live client beats one quoting a lower rate.
How Should a Startup Choose?
In Deloitte's State of AI in the Enterprise 2026, only 25% of respondents had moved 40% or more of their AI pilots into production, and 54% expected to get there within three to six months (Deloitte, January 2026). Five questions separate the MLOps consulting companies that will still be useful in month four:
- Show me a production system older than twelve months, with its runbook. Ask for a client whose engineers now deploy alone.
- Which cost number will you report each month? Cost per model or per request, readable by your finance lead.
- How do you gate an LLM release? Nearly 89% of respondents have observability for their agents, but evals adoption is at 52% (LangChain, vendor survey of 1,300+ professionals, 2026). Tracing shows what broke; an eval gate in CI stops it shipping again.
- Who can change a production model, and where is that logged? In IBM's Cost of a Data Breach Report 2025, 13% of organizations reported breaches of AI models or applications, and 97% of those lacked proper AI access controls (IBM, July 2025).
- For fintech: which financial-services model have you run in production? In NVIDIA's State of AI in Financial Services 2026, a vendor survey of 800+ professionals, 42% were using or assessing agentic AI and 21% had deployed agents (NVIDIA, 2026).
The platform question is mostly settled: 66% of organizations hosting generative AI models use Kubernetes for some or all of their inference (CNCF, January 2026). Pick by where your data lives, then by these answers, and only then by rate. Unsure you need a model yet? Start with our guides to AI development services and generative AI development services.
decision checklist visual with the five questions
The Bottom Line
The market for MLOps companies in 2026 has no referee. The consultancies assistants name first are mostly too large for a startup, and the small names keep changing. What holds up is the same four-part check: production evidence you can inspect, reviews or named clients, an engagement you can buy, and a stack that already covers LLMOps, GPU cost, monitoring and access control.
On that check, Dysnix and Winder.AI clear all four filters in full, and SquareOps, Addepto and Provectus come next. If you want one team to run the MLOps pipeline and the bare-metal compute under it, and your budget starts at $50,000, that is what INC4's MLOps & DevOps and Compute Infrastructure practices were built for. The first conversation is an engineering call. Talk to the team.