Top AI Integration Services

Slalom vs EPAM Systems: full comparison for 2026

Quick verdict

Slalom (4.5/5) edges ahead of EPAM Systems (4.1/5) overall. Slalom is the better choice for mid-size and large firms on several platforms at once. EPAM Systems is the stronger option for enterprises with multi-year engineering budgets. The right choice depends on your project size, budget, and required tech stack.

Slalom vs EPAM Systems: head-to-head summary

Criterion Slalom EPAM Systems
Founded 2001 1993
HQ Seattle, WA, USA Newtown, PA, USA
Team size ~8,000 61,000+
Rating 4.5 / 5 4.1 / 5
Primary differentiator Partner depth across Snowflake, Microsoft, Salesforce, and OpenAI in one firm Engineering capacity across Europe, India, and the Americas for long AI programs
Pricing model Time & materials and fixed-scope statements of work; rates on request Time & materials and dedicated teams; rates on request
Min. engagement Not disclosed Not disclosed
Primary tech stack Snowflake, Salesforce, Microsoft Dynamics 365 Azure OpenAI, AWS Bedrock, Databricks
Industries served Financial services, Healthcare, Retail & e-commerce, Public sector, Energy Financial services, Healthcare, Retail & e-commerce, Media, Energy

Slalom vs EPAM Systems: overview

Slalom

Slalom is a business and technology consultancy founded in Seattle in 2001, with an estimated 8,000 employees. It holds partner status with several platforms at once; in Q1 2026 it became a Snowflake Cortex Code preferred partner and earned Microsoft's Frontier partner badge. In mid-2026 it also expanded a services partnership with OpenAI for ChatGPT rollouts in federal agencies. Slalom's strength for integration buyers is breadth across platforms within a single engagement, delivered by local teams in many U.S. metros.

EPAM Systems

EPAM Systems was founded in 1993 and is headquartered in Newtown, Pennsylvania, with over 61,200 employees at the end of 2025. Acquisitions shaped the recent mix: NEORIS (2024) for Latin America and Iberia, and First Derivative (completed December 2024) for financial-services data. Management targets more than $600 million of AI-native revenue in 2026, after reporting over $105 million in Q4 2025. Its AI/Run tooling and Agentic QA product support large engineering programs more than single-workflow pilots.

Services and capabilities: Slalom vs EPAM Systems

Capability Slalom EPAM Systems
CRM / ERP integration ✓ ✓
LLM API gateway & cost control ✗ ✓
Document processing ✗ ✗
Conversational AI ✓ ✗
Agentic workflows ✗ ✓
Fixed-price pilot ✗ ✗
Managed services after launch ✓ ✓

Tech stack comparison: Slalom vs EPAM Systems

Framework / platform Slalom EPAM Systems
Salesforce ✓ N/A
SAP N/A ✓
Microsoft Dynamics 365 ✓ N/A
HubSpot N/A N/A
Snowflake ✓ ✓
Databricks ✓ ✓
BigQuery N/A N/A
Azure OpenAI ✓ ✓
AWS Bedrock ✓ ✓
Zendesk N/A N/A

Pricing comparison: Slalom vs EPAM Systems

Criterion Slalom EPAM Systems
Minimum engagement Not disclosed Not disclosed
Engagement models Fixed-scope project, Time & materials, Managed services Time & materials, Dedicated team, Managed services
Rate transparency Not public Not public
Price tier Mid-market Mid-market

Target audience comparison: Slalom vs EPAM Systems

Dimension Slalom EPAM Systems
Best company size Mid-market to enterprise Enterprise
Best industries Financial services, Healthcare, Retail & e-commerce Financial services, Healthcare, Retail & e-commerce
Best use cases Putting a Cortex-based assistant on top of an existing Snowflake warehouse, Adding generative features to a Salesforce org and a Microsoft tenant in one program Large data-platform programs that end in AI features, Agent development across several business units
Typical project type Fixed-scope project Time & materials

Slalom vs EPAM Systems: pros and cons

Slalom
+ One firm can cover the warehouse, the CRM, and the Microsoft tenant, which avoids splitting a project between three vendors.
+ Local office model means consultants are often in the same city as the client.
+ Snowflake Cortex Code preferred status (Q1 2026) is relevant for anyone putting AI over warehouse data.
+ Change-management and adoption work sits next to the engineering.
- No public fixed-price pilot offer, so the first engagement is scoped from scratch
- Consulting-firm rate structure puts small pilots on the expensive side
- Headcount is a third-party estimate; Slalom doesn't publish a current figure
EPAM Systems
+ Engineering depth to staff many workstreams at once.
+ Public reporting on AI-native revenue gives a measurable view of the practice.
+ First Derivative added capital-markets data skills.
+ Agentic QA product addresses testing of AI-generated code.
- Acquisition-driven growth (NEORIS, First Derivative) means teams are still being integrated
- Not built for a small fixed-price pilot
- Management flagged slower organic growth in 2026 guidance

Who should choose Slalom?

A typical fit: putting a Cortex-based assistant on top of an existing Snowflake warehouse.

Partner depth across Snowflake, Microsoft, Salesforce, and OpenAI in one firm. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Healthcare, Retail & e-commerce, Public sector, Energy.

Who should choose EPAM Systems?

A typical fit: large data-platform programs that end in AI features.

Engineering capacity across Europe, India, and the Americas for long AI programs. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Healthcare, Retail & e-commerce, Media, Energy.

Decision matrix: Slalom vs EPAM Systems

Your situation Recommended choice
You want a priced pilot before committing to a rollout Neither advertises one; ask for a scoped pilot quote
You need someone to run and monitor the system after launch Both offer managed services
Your budget is at the lower end Compare: Slalom (Not disclosed) vs EPAM Systems (Not disclosed)
The AI has to read and write in your CRM or ERP Both have CRM or ERP integration work
You need multi-step agents acting across systems EPAM Systems
You need a large team for a multi-year program EPAM Systems

Use case fit: Slalom vs EPAM Systems

Use case Slalom fit EPAM Systems fit Winner
Putting a Cortex-based assistant on top of an existing Snowflake warehouse Strong Limited Slalom
Adding generative features to a Salesforce org and a Microsoft tenant in one program Strong Limited Slalom
Large data-platform programs that end in AI features Limited Strong EPAM Systems
Agent development across several business units Limited Strong EPAM Systems

Verdict: Slalom vs EPAM Systems

Slalom (4.5/5) is the stronger overall choice for most AI Integration Services projects. Partner depth across Snowflake, Microsoft, Salesforce, and OpenAI in one firm.

EPAM Systems (4.1/5) is worth a look if you need agent development across several business units. If your situation matches that, EPAM Systems is a competitive option.

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Slalom vs EPAM Systems FAQ

Is Slalom better than EPAM Systems?

Slalom (4.5/5) scores higher overall, but "better" depends on your use case. Slalom's strongest advantage: one firm can cover the warehouse, the CRM, and the Microsoft tenant, which avoids splitting a project between three vendors. EPAM Systems's strongest advantage: engineering depth to staff many workstreams at once.

How do Slalom and EPAM Systems differ in pricing?

Slalom's pricing: time & materials and fixed-scope statements of work; rates on request. EPAM Systems's pricing: time & materials and dedicated teams; rates on request. Any hourly bands shown come from Clutch, not a published rate card, so a scoping call is still needed for a project quote.

Which is better for enterprise: Slalom or EPAM Systems?

EPAM Systems is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each provider before shortlisting.

What are the main differences between Slalom and EPAM Systems?

Slalom's primary differentiator is: partner depth across Snowflake, Microsoft, Salesforce, and OpenAI in one firm. EPAM Systems's primary differentiator is: engineering capacity across Europe, India, and the Americas for long AI programs. They also differ in team size (~8,000 vs 61,000+), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Financial services, Healthcare vs Financial services, Healthcare).

Verify all details directly with each provider before making a decision.